From b023d0b60786eceafcbf71ee65dabe9efbb5f2b2 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 22 Mar 2026 20:25:01 +0800 Subject: [PATCH 01/28] Add missing deps for more models --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 36b6a33..1c307bd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,6 +21,7 @@ dependencies = [ "numpy>=1.24.0", "torch>=2.4.0", "transformers>=4.40.0", + "bitsandbytes>=0.46.1", "accelerate>=0.26.0", "huggingface-hub>=0.23.0", "datasets>=2.14.0", @@ -32,6 +33,8 @@ dependencies = [ "wonderwords>=2.2.0", "openai>=1.0.0", "tiktoken>=0.7.0", + "python-dotenv>=1.0.0", + "auto-gptq>=0.5.0", ] [project.optional-dependencies] From 36c1c031b7cd20e4d76ddc83fea6f74973bc6b5f Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 22 Mar 2026 20:28:30 +0800 Subject: [PATCH 02/28] Correct model classification --- src/llm_dna/models/ModelLoader.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/llm_dna/models/ModelLoader.py b/src/llm_dna/models/ModelLoader.py index f3c8b65..b2c52ee 100644 --- a/src/llm_dna/models/ModelLoader.py +++ b/src/llm_dna/models/ModelLoader.py @@ -82,7 +82,9 @@ def _detect_model_type(self, model_path_or_name: str) -> str: "cohere/command", "perplexity/", ] - if any(model_lower.startswith(prefix) for prefix in openrouter_prefixes): + huggingface_prefixes_openai = "openai/gpt-oss" + + if any(model_lower.startswith(prefix) for prefix in openrouter_prefixes) and not model_lower.startswith(huggingface_prefixes_openai): return "openrouter" # Check for Google Gemini model names From e6efb18b60254f4981253005fbb0a0183014a794 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 22 Mar 2026 20:39:45 +0800 Subject: [PATCH 03/28] More deps --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 1c307bd..f561357 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,9 @@ dependencies = [ "tiktoken>=0.7.0", "python-dotenv>=1.0.0", "auto-gptq>=0.5.0", + "optimum>=1.16.0", + "mamba-ssm>=1.0.0", + "compressed-tensors>=0.1.0", ] [project.optional-dependencies] From 353c144352888df2ba7c664ba862307fa163a72b Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 22 Mar 2026 21:08:03 +0800 Subject: [PATCH 04/28] More deps --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index f561357..6890fb5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,8 @@ dependencies = [ "optimum>=1.16.0", "mamba-ssm>=1.0.0", "compressed-tensors>=0.1.0", + "mlx>=0.10.0", + "mlx-lm>=0.10.0", ] [project.optional-dependencies] From c7e54db0f93f3875bfbcc47f642e921e0daa7945 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 22 Mar 2026 21:52:19 +0800 Subject: [PATCH 05/28] Update dep --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 6890fb5..4497cba 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,7 @@ dependencies = [ "compressed-tensors>=0.1.0", "mlx>=0.10.0", "mlx-lm>=0.10.0", + "timm>=0.9.0", ] [project.optional-dependencies] From 7e9d0f5954507c9ea14b035fdb3e0b4b2cbf4d61 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 18:49:15 +0800 Subject: [PATCH 06/28] Refactor deps --- README.md | 21 +++++++++++++++++++++ pyproject.toml | 30 +++++++++++++++++++++++++----- 2 files changed, 46 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 8d66ea7..796b863 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,27 @@ pip install llm-dna Use `llm-dna` for install/package naming, and `llm_dna` for Python imports. +Optional extras are available for model families that need additional runtime dependencies: + +```bash +# Apple Silicon / MLX-backed models +pip install "llm-dna[apple]" + +# Quantized HuggingFace models (bitsandbytes, GPTQ, compressed-tensors, optimum) +pip install "llm-dna[quantization]" + +# Architecture-specific model families such as Mamba or TIMM-backed models +pip install "llm-dna[model_families]" + +# Everything above +pip install "llm-dna[full]" +``` + +Extra guidance: +- `apple`: required for MLX and `mlx-community/*` style model families on Apple Silicon. +- `quantization`: required for many GPTQ, bitsandbytes, and compressed-tensors model families. +- `model_families`: required for specific architectures whose modeling code depends on packages like `mamba-ssm` or `timm`. + ## Quick Start ```python diff --git a/pyproject.toml b/pyproject.toml index 4497cba..608182f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,6 @@ dependencies = [ "numpy>=1.24.0", "torch>=2.4.0", "transformers>=4.40.0", - "bitsandbytes>=0.46.1", "accelerate>=0.26.0", "huggingface-hub>=0.23.0", "datasets>=2.14.0", @@ -34,18 +33,39 @@ dependencies = [ "openai>=1.0.0", "tiktoken>=0.7.0", "python-dotenv>=1.0.0", +] + +[project.optional-dependencies] +model_scraping = ["requests>=2.31.0"] + +apple = [ + "mlx>=0.10.0", + "mlx-lm>=0.10.0", +] + +quantization = [ + "bitsandbytes>=0.46.1", "auto-gptq>=0.5.0", "optimum>=1.16.0", - "mamba-ssm>=1.0.0", "compressed-tensors>=0.1.0", +] + +model_families = [ + "mamba-ssm>=1.0.0", + "timm>=0.9.0", +] + +full = [ "mlx>=0.10.0", "mlx-lm>=0.10.0", + "bitsandbytes>=0.46.1", + "auto-gptq>=0.5.0", + "optimum>=1.16.0", + "compressed-tensors>=0.1.0", + "mamba-ssm>=1.0.0", "timm>=0.9.0", ] -[project.optional-dependencies] -model_scraping = ["requests>=2.31.0"] - vllm = ["vllm>=0.4.0"] dev = [ From 2e939e8242e4cb127b4cdf95083829a8d2533c90 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 18:50:34 +0800 Subject: [PATCH 07/28] Improve model loader classification --- src/llm_dna/models/ModelLoader.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/llm_dna/models/ModelLoader.py b/src/llm_dna/models/ModelLoader.py index b2c52ee..9a10b40 100644 --- a/src/llm_dna/models/ModelLoader.py +++ b/src/llm_dna/models/ModelLoader.py @@ -75,16 +75,15 @@ def _detect_model_type(self, model_path_or_name: str) -> str: "openrouter:", "anthropic/claude-", "deepseek/", - "openai/gpt-", + "openai/gpt-3", + "openai/gpt-4", "google/gemini-", - "z-ai/", "x-ai/grok-", "cohere/command", "perplexity/", ] - huggingface_prefixes_openai = "openai/gpt-oss" - if any(model_lower.startswith(prefix) for prefix in openrouter_prefixes) and not model_lower.startswith(huggingface_prefixes_openai): + if any(model_lower.startswith(prefix) for prefix in openrouter_prefixes): return "openrouter" # Check for Google Gemini model names From 38a3450acb33765eb4d7955e671dd9cdcaf411b4 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 18:51:19 +0800 Subject: [PATCH 08/28] Protect sensitive info like token --- src/llm_dna/core/extraction.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/llm_dna/core/extraction.py b/src/llm_dna/core/extraction.py index de46f42..a953fc1 100644 --- a/src/llm_dna/core/extraction.py +++ b/src/llm_dna/core/extraction.py @@ -629,6 +629,13 @@ def main(): logging.info(f"DNA signature saved to: {output_path}") # Save summary + # Create safe args dict without sensitive information + safe_args = vars(args).copy() + # Remove sensitive fields that should not be saved to output files + sensitive_fields = ['token', 'OPENROUTER_API_KEY', 'OPENAI_API_KEY'] + for field in sensitive_fields: + safe_args.pop(field, None) + summary = { "model_name": args.model_name, "dataset": args.dataset, @@ -641,7 +648,7 @@ def main(): "signature_stats": signature.get_statistics(), "metadata": signature.metadata.__dict__, "output_file": str(output_path), - "args": vars(args) + "args": safe_args } # Keep summary filename model-only as well From 5a89dac6531cbb3303ab2aa06253098eec44fe70 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 19:20:48 +0800 Subject: [PATCH 09/28] Fix: save responses.json for single model dna generation as well --- src/llm_dna/api.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/src/llm_dna/api.py b/src/llm_dna/api.py index 858e9a0..c5a60e9 100644 --- a/src/llm_dna/api.py +++ b/src/llm_dna/api.py @@ -642,6 +642,31 @@ def calc_dna(config: DNAExtractionConfig) -> DNAExtractionResult: ) vector = _validate_signature(signature) + if config.save: + cached_responses = _load_cached_responses(response_path, expected_count=len(probe_texts)) + if cached_responses is None: + logging.info( + "Generating and saving responses for '%s' to %s to align single-model caching with batch mode.", + config.model_name, + response_path, + ) + responses = _generate_responses_for_model( + model_name=config.model_name, + config=config, + model_meta=model_meta, + probe_texts=probe_texts, + device=resolved_device, + resolved_token=resolved_token, + incremental_save_path=response_path, + ) + _save_response_cache( + path=response_path, + model_name=config.model_name, + dataset=config.dataset, + prompts=probe_texts, + responses=responses, + ) + elapsed_seconds = time.time() - start_time output_path: Optional[Path] = None From b26887c6e5de313eeea6e564f6bfa2ce92a6dde6 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 19:48:56 +0800 Subject: [PATCH 10/28] Add awq dep --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index b41e62b..9c9622a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,6 +45,7 @@ apple = [ quantization = [ "bitsandbytes>=0.46.1", + "autoawq>=0.2.0", "auto-gptq>=0.5.0", "optimum>=1.16.0", "compressed-tensors>=0.1.0", @@ -59,6 +60,7 @@ full = [ "mlx>=0.10.0", "mlx-lm>=0.10.0", "bitsandbytes>=0.46.1", + "autoawq>=0.2.0", "auto-gptq>=0.5.0", "optimum>=1.16.0", "compressed-tensors>=0.1.0", From 4c819e2d8200f4d0cdf05910e991b4fa939e2a05 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 20:51:23 +0800 Subject: [PATCH 11/28] Change cli to load env properly --- src/llm_dna/cli.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/llm_dna/cli.py b/src/llm_dna/cli.py index 10d203f..ef98247 100644 --- a/src/llm_dna/cli.py +++ b/src/llm_dna/cli.py @@ -8,6 +8,8 @@ from pathlib import Path from typing import Iterable, List, Optional +from dotenv import load_dotenv + def _load_models_from_file(path: Path) -> List[str]: """Load model names from a file, one per line.""" @@ -183,6 +185,10 @@ def main(argv: Optional[Iterable[str]] = None) -> int: """Main CLI entrypoint for DNA extraction.""" from .api import DNAExtractionConfig, calc_dna, calc_dna_parallel + project_root = Path(__file__).resolve().parents[2] + load_dotenv(project_root / ".env", override=False) + load_dotenv(override=False) + args = parse_arguments(argv) # Resolve model names From ce7cc7722eb392863227fbf73b5b443056d606a1 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 28 Mar 2026 20:51:43 +0800 Subject: [PATCH 12/28] Directly read from openrouter list to prevent misclassification --- src/llm_dna/models/ModelLoader.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/src/llm_dna/models/ModelLoader.py b/src/llm_dna/models/ModelLoader.py index 9a10b40..bde650d 100644 --- a/src/llm_dna/models/ModelLoader.py +++ b/src/llm_dna/models/ModelLoader.py @@ -3,6 +3,7 @@ """ import os +import json from typing import Optional, Dict, Any, Union from pathlib import Path import logging @@ -14,10 +15,34 @@ class ModelLoader: """Factory class for loading different types of LLMs.""" + _openrouter_model_ids: Optional[set[str]] = None def __init__(self, config_dict: Optional[Dict[str, Any]] = None): self.logger = logging.getLogger(__name__) self.config_dict = config_dict or {} + + @classmethod + def _load_openrouter_model_ids(cls) -> set[str]: + if cls._openrouter_model_ids is not None: + return cls._openrouter_model_ids + + model_ids: set[str] = set() + config_path = Path(__file__).resolve().parents[3] / "configs" / "openrouter_llm_list.jsonl" + try: + with config_path.open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + record = json.loads(line) + model_id = str(record.get("model_id", "")).strip().lower() + if model_id: + model_ids.add(model_id) + except Exception: + model_ids = set() + + cls._openrouter_model_ids = model_ids + return model_ids def load_model( self, @@ -86,6 +111,9 @@ def _detect_model_type(self, model_path_or_name: str) -> str: if any(model_lower.startswith(prefix) for prefix in openrouter_prefixes): return "openrouter" + if model_lower in self._load_openrouter_model_ids(): + return "openrouter" + # Check for Google Gemini model names gemini_prefixes = [ "gemini-", From 4ec8af6182a175d76690d9aaf1172b96865e5677 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 5 Apr 2026 20:13:09 +0800 Subject: [PATCH 13/28] Organise pyproject.toml --- pyproject.toml | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9c9622a..d202f2e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,8 +39,8 @@ dependencies = [ model_scraping = ["requests>=2.31.0"] apple = [ - "mlx>=0.10.0", - "mlx-lm>=0.10.0", + "mlx>=0.10.0; sys_platform == 'darwin' and platform_machine == 'arm64'", + "mlx-lm>=0.10.0; sys_platform == 'darwin' and platform_machine == 'arm64'", ] quantization = [ @@ -52,20 +52,14 @@ quantization = [ ] model_families = [ - "mamba-ssm>=1.0.0", + "mamba-ssm>=1.0.0; sys_platform == 'linux'", "timm>=0.9.0", ] full = [ - "mlx>=0.10.0", - "mlx-lm>=0.10.0", - "bitsandbytes>=0.46.1", - "autoawq>=0.2.0", - "auto-gptq>=0.5.0", - "optimum>=1.16.0", - "compressed-tensors>=0.1.0", - "mamba-ssm>=1.0.0", - "timm>=0.9.0", + "llm-dna[apple]", + "llm-dna[quantization]", + "llm-dna[model_families]", ] vllm = ["vllm>=0.4.0"] From 1ac10eb99e5a9d43a839429782b3a6d92ab96394 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 5 Apr 2026 20:16:05 +0800 Subject: [PATCH 14/28] Fix hardcode --- src/llm_dna/cli.py | 2 -- src/llm_dna/models/ModelLoader.py | 30 +----------------------------- 2 files changed, 1 insertion(+), 31 deletions(-) diff --git a/src/llm_dna/cli.py b/src/llm_dna/cli.py index ef98247..56a4d64 100644 --- a/src/llm_dna/cli.py +++ b/src/llm_dna/cli.py @@ -185,8 +185,6 @@ def main(argv: Optional[Iterable[str]] = None) -> int: """Main CLI entrypoint for DNA extraction.""" from .api import DNAExtractionConfig, calc_dna, calc_dna_parallel - project_root = Path(__file__).resolve().parents[2] - load_dotenv(project_root / ".env", override=False) load_dotenv(override=False) args = parse_arguments(argv) diff --git a/src/llm_dna/models/ModelLoader.py b/src/llm_dna/models/ModelLoader.py index bde650d..15ac4ba 100644 --- a/src/llm_dna/models/ModelLoader.py +++ b/src/llm_dna/models/ModelLoader.py @@ -3,7 +3,6 @@ """ import os -import json from typing import Optional, Dict, Any, Union from pathlib import Path import logging @@ -15,34 +14,10 @@ class ModelLoader: """Factory class for loading different types of LLMs.""" - _openrouter_model_ids: Optional[set[str]] = None def __init__(self, config_dict: Optional[Dict[str, Any]] = None): self.logger = logging.getLogger(__name__) self.config_dict = config_dict or {} - - @classmethod - def _load_openrouter_model_ids(cls) -> set[str]: - if cls._openrouter_model_ids is not None: - return cls._openrouter_model_ids - - model_ids: set[str] = set() - config_path = Path(__file__).resolve().parents[3] / "configs" / "openrouter_llm_list.jsonl" - try: - with config_path.open("r", encoding="utf-8") as handle: - for line in handle: - line = line.strip() - if not line: - continue - record = json.loads(line) - model_id = str(record.get("model_id", "")).strip().lower() - if model_id: - model_ids.add(model_id) - except Exception: - model_ids = set() - - cls._openrouter_model_ids = model_ids - return model_ids def load_model( self, @@ -103,17 +78,14 @@ def _detect_model_type(self, model_path_or_name: str) -> str: "openai/gpt-3", "openai/gpt-4", "google/gemini-", + "z-ai/", "x-ai/grok-", "cohere/command", "perplexity/", ] - if any(model_lower.startswith(prefix) for prefix in openrouter_prefixes): return "openrouter" - if model_lower in self._load_openrouter_model_ids(): - return "openrouter" - # Check for Google Gemini model names gemini_prefixes = [ "gemini-", From b7ee090bcacd8d5fcbcfbe0898d6a9c616009a0d Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 5 Apr 2026 21:14:34 +0800 Subject: [PATCH 15/28] Do not regenerate for single mode --- src/llm_dna/api.py | 140 ++++++++++++--------------------------------- 1 file changed, 38 insertions(+), 102 deletions(-) diff --git a/src/llm_dna/api.py b/src/llm_dna/api.py index c5a60e9..ff6cac1 100644 --- a/src/llm_dna/api.py +++ b/src/llm_dna/api.py @@ -11,7 +11,6 @@ from datetime import datetime from dataclasses import asdict, dataclass, replace from pathlib import Path -from types import SimpleNamespace from typing import TYPE_CHECKING, Any, Dict, Optional import numpy as np @@ -310,6 +309,12 @@ def _load_cached_responses(path: Path, expected_count: int) -> Optional[list[str logging.warning("Failed to parse cached responses from %s: %s", path, exc) return None + if isinstance(payload, dict): + complete = payload.get("complete") + if complete is False: + logging.warning("Ignoring incomplete cached responses at %s", path) + return None + responses: list[str] if isinstance(payload, dict) and isinstance(payload.get("items"), list): responses = [str(item.get("response", "")) for item in payload["items"] if isinstance(item, dict)] @@ -322,6 +327,11 @@ def _load_cached_responses(path: Path, expected_count: int) -> Optional[list[str if not responses: return None + non_empty_count = sum(1 for response in responses if response.strip()) + if non_empty_count == 0: + logging.warning("Ignoring cached responses at %s because all responses are empty.", path) + return None + if len(responses) != expected_count: logging.warning( "Cached responses at %s have probe count mismatch (%s != %s); normalizing by truncating/padding.", @@ -537,11 +547,11 @@ def calc_dna(config: DNAExtractionConfig) -> DNAExtractionResult: signature: "DNASignature" vector: np.ndarray - is_api_mode = _is_api_parallel_mode(config, [config.model_name]) + if config.extractor_type != "embedding": + raise ValueError(f"Unsupported extractor_type for calc_dna: {config.extractor_type}") + response_path = _response_cache_path(config, config.model_name) - cached_responses: Optional[list[str]] = None - if is_api_mode and config.extractor_type == "embedding": - cached_responses = _load_cached_responses(response_path, expected_count=len(probe_texts)) + cached_responses = _load_cached_responses(response_path, expected_count=len(probe_texts)) if cached_responses is not None: logging.info( @@ -550,21 +560,22 @@ def calc_dna(config: DNAExtractionConfig) -> DNAExtractionResult: response_path, ) model_meta = _default_model_metadata(config.model_name) - signature, vector, _ = _extract_signature_from_text_responses( - model_name=config.model_name, - responses=cached_responses, - config=config, - model_meta=model_meta, - generation_device=resolved_device, - encoder_device=resolved_device, - ) - elif is_api_mode and config.extractor_type == "embedding": - # API model without cached responses: generate via API, then encode - logging.info( - "Generating responses for API model '%s' via provider API...", - config.model_name, - ) + responses = cached_responses + else: + if _is_api_model_type(config.model_type): + logging.info( + "Generating responses for API model '%s' via provider API...", + config.model_name, + ) + model_meta = _load_model_metadata_for_model(config.model_name, metadata_file, token=resolved_token) + is_generative = model_meta.get("architecture", {}).get("is_generative") + if is_generative is False: + arch_type = model_meta.get("architecture", {}).get("type") + raise ValueError( + f"Model '{config.model_name}' is non-generative (architecture={arch_type})." + ) + responses = _generate_responses_for_model( model_name=config.model_name, config=config, @@ -574,7 +585,6 @@ def calc_dna(config: DNAExtractionConfig) -> DNAExtractionResult: resolved_token=resolved_token, incremental_save_path=response_path if config.save else None, ) - # Save final response cache if config.save: _save_response_cache( path=response_path, @@ -583,89 +593,15 @@ def calc_dna(config: DNAExtractionConfig) -> DNAExtractionResult: prompts=probe_texts, responses=responses, ) - signature, vector, _ = _extract_signature_from_text_responses( - model_name=config.model_name, - responses=responses, - config=config, - model_meta=model_meta, - generation_device=resolved_device, - encoder_device=resolved_device, - ) - else: - # Non-API model: use hidden-state extraction - model_meta = _load_model_metadata_for_model(config.model_name, metadata_file, token=resolved_token) - - is_generative = model_meta.get("architecture", {}).get("is_generative") - if is_generative is False: - arch_type = model_meta.get("architecture", {}).get("type") - raise ValueError( - f"Model '{config.model_name}' is non-generative (architecture={arch_type})." - ) - - resolved_model_path = _resolve_model_path(config.model_path, model_meta) - - args = SimpleNamespace( - model_name=config.model_name, - model_path=resolved_model_path, - model_type=config.model_type, - dataset=config.dataset, - probe_set=config.probe_set, - max_samples=config.max_samples, - data_root=config.data_root, - extractor_type=config.extractor_type, - dna_dim=config.dna_dim, - reduction_method=config.reduction_method, - embedding_merge=config.embedding_merge, - max_length=config.max_length, - save_format="json", - output_dir=Path(config.output_dir), - load_in_8bit=config.load_in_8bit, - load_in_4bit=config.load_in_4bit, - no_quantization=config.no_quantization, - metadata_file=metadata_file, - token=resolved_token, - trust_remote_code=config.trust_remote_code, - device=resolved_device, - log_level=config.log_level, - random_seed=config.random_seed, - use_chat_template=config.use_chat_template, - ) - - signature = core.extract_dna_signature( - model_name=config.model_name, - model_path=resolved_model_path, - model_type=config.model_type, - probe_texts=probe_texts, - extractor_type=config.extractor_type, - model_metadata=model_meta, - args=args, - ) - vector = _validate_signature(signature) - if config.save: - cached_responses = _load_cached_responses(response_path, expected_count=len(probe_texts)) - if cached_responses is None: - logging.info( - "Generating and saving responses for '%s' to %s to align single-model caching with batch mode.", - config.model_name, - response_path, - ) - responses = _generate_responses_for_model( - model_name=config.model_name, - config=config, - model_meta=model_meta, - probe_texts=probe_texts, - device=resolved_device, - resolved_token=resolved_token, - incremental_save_path=response_path, - ) - _save_response_cache( - path=response_path, - model_name=config.model_name, - dataset=config.dataset, - prompts=probe_texts, - responses=responses, - ) + signature, vector, _ = _extract_signature_from_text_responses( + model_name=config.model_name, + responses=responses, + config=config, + model_meta=model_meta, + generation_device=resolved_device, + encoder_device=resolved_device, + ) elapsed_seconds = time.time() - start_time From 4dff95ce3d0c1244adbcbaede088b5821b78c816 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sat, 11 Apr 2026 16:57:12 +0800 Subject: [PATCH 16/28] Add remaining list for HF models --- configs/huggingface_llm_list_left.jsonl | 11813 ++++++++++++++++++++++ 1 file changed, 11813 insertions(+) create mode 100644 configs/huggingface_llm_list_left.jsonl diff --git a/configs/huggingface_llm_list_left.jsonl b/configs/huggingface_llm_list_left.jsonl new file mode 100644 index 0000000..d18962f --- /dev/null +++ b/configs/huggingface_llm_list_left.jsonl @@ -0,0 +1,11813 @@ +{"model_id": "Qwen/Qwen3-0.6B", 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"text-generation", "downloads": 265} +{"model_id": "lmstudio-community/Qwen2.5-Math-72B-Instruct-GGUF", "provider": "huggingface", "task": "text-generation", "downloads": 265} +{"model_id": "ysn-rfd/Qwen2.5-1.5B-Instruct-Q8_0-GGUF", "provider": "huggingface", "task": "text-generation", "downloads": 265} +{"model_id": "QuantFactory/gemma-2-9b-it-SimPO-rudpo-GGUF", "provider": "huggingface", "task": "text-generation", "downloads": 265} +{"model_id": "Intel/tiny-random-falcon_ipex_model", "provider": "huggingface", "task": "text-generation", "downloads": 265} From 26db4d9c510ca39676dcd98464b90dc2f9f580eb Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Wed, 22 Jul 2026 21:08:16 +0800 Subject: [PATCH 17/28] Enhance DNA experiment scripts --- scripts/analyze_openrouter_dna_stability.py | 255 +++ scripts/analyze_tree_aware_errors.py | 216 +++ scripts/build_experiment_cohort.py | 205 ++ scripts/calc_dna.py | 47 +- scripts/debug_openrouter_max_tokens.py | 204 ++ scripts/evaluate_dna_probe_sweep.py | 1413 ++++++++++++++ scripts/evaluate_exact_dna_classification.py | 405 ++++ scripts/export_hf_model_tree.py | 233 +++ scripts/filter_seen_models_from_lists.py | 90 + scripts/flag_empty_responses.py | 81 + scripts/prune_model_list_by_status.py | 128 ++ scripts/prune_rand_chinese_from_saved.py | 81 + scripts/report_sweep_progress.py | 128 ++ .../run_dna_temperature_top_p_missing_only.sh | 44 + scripts/run_dna_temperature_top_p_sweep.sh | 215 +++ scripts/run_hf_dna_pipeline.py | 1707 +++++++++++++++++ scripts/run_primary_analysis.sh | 64 + scripts/run_primary_robustness_experiment.sh | 31 + scripts/run_rbf_mmd_pilot.py | 573 ++++++ scripts/run_top_p_control_t05.sh | 46 + scripts/visualize_dna_rerun_heatmap.py | 362 ++++ 21 files changed, 6527 insertions(+), 1 deletion(-) create mode 100644 scripts/analyze_openrouter_dna_stability.py create mode 100644 scripts/analyze_tree_aware_errors.py create mode 100644 scripts/build_experiment_cohort.py create mode 100644 scripts/debug_openrouter_max_tokens.py create mode 100644 scripts/evaluate_dna_probe_sweep.py create mode 100644 scripts/evaluate_exact_dna_classification.py create mode 100644 scripts/export_hf_model_tree.py create mode 100644 scripts/filter_seen_models_from_lists.py create mode 100644 scripts/flag_empty_responses.py create mode 100644 scripts/prune_model_list_by_status.py create mode 100644 scripts/prune_rand_chinese_from_saved.py create mode 100644 scripts/report_sweep_progress.py create mode 100755 scripts/run_dna_temperature_top_p_missing_only.sh create mode 100755 scripts/run_dna_temperature_top_p_sweep.sh create mode 100644 scripts/run_hf_dna_pipeline.py create mode 100644 scripts/run_primary_analysis.sh create mode 100644 scripts/run_primary_robustness_experiment.sh create mode 100644 scripts/run_rbf_mmd_pilot.py create mode 100755 scripts/run_top_p_control_t05.sh create mode 100644 scripts/visualize_dna_rerun_heatmap.py diff --git a/scripts/analyze_openrouter_dna_stability.py b/scripts/analyze_openrouter_dna_stability.py new file mode 100644 index 0000000..3137668 --- /dev/null +++ b/scripts/analyze_openrouter_dna_stability.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +"""Generate sliding 50-response subset DNAs from cached responses.""" + +from __future__ import annotations + +import json +import logging +import os +import sys +from dataclasses import dataclass, replace +from datetime import datetime +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +from llm_dna.api import ( + DNAExtractionConfig, + _default_model_metadata, + _extract_signature_from_text_responses, + _save_signature_outputs, +) + +DATA_DIR = ROOT / "dna-out-chat" / "rand_chinese" +OUTPUT_ROOT = ROOT / "dna-out-chat" / "rand_chinese_subsets" +ENCODER_NAME = "all-mpnet-base-v2" +WINDOW_SIZE = 50 +WINDOW_STEP = 5 + + +@dataclass(frozen=True) +class ValidModel: + key: str + model_name: str + responses: list[str] + payload: dict + + +def load_valid_models(data_dir: Path) -> list[ValidModel]: + """Load complete artifacts with at least one non-empty cached response.""" + records: list[ValidModel] = [] + if not data_dir.exists(): + return records + for model_dir in sorted(path for path in data_dir.iterdir() if path.is_dir()): + response_path = model_dir / "responses.json" + summary_files = list(model_dir.glob("*_summary.json")) + dna_files = list(model_dir.glob("*_dna.json")) + if not response_path.exists() or not summary_files or not dna_files: + continue + try: + payload = json.loads(response_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + continue + responses = [ + str(item.get("response", "")).strip() + for item in payload.get("items", []) + if isinstance(item, dict) and str(item.get("response", "")).strip() + ] + if responses: + records.append( + ValidModel( + key=model_dir.name, + model_name=str(payload.get("model") or model_dir.name), + responses=responses, + payload=payload, + ) + ) + return records + + +def timestamp() -> str: + return datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + +def default_run_dir() -> Path: + stamp = datetime.now().strftime("%Y%m%d_%H%M%S") + return OUTPUT_ROOT / f"run_{stamp}" + + +def log(message: str, log_file: Path) -> None: + line = f"[{timestamp()}] {message}" + print(line, flush=True) + with log_file.open("a", encoding="utf-8") as handle: + handle.write(line + "\n") + + +def load_models(limit: int | None) -> list[tuple[str, dict]]: + models: list[tuple[str, dict]] = [] + for record in load_valid_models(DATA_DIR): + payload = dict(record.payload) + items = [item for item in payload.get("items", []) if isinstance(item, dict) and str(item.get("response", "")).strip()] + if len(items) < WINDOW_SIZE: + continue + payload["items"] = items + models.append((record.key, payload)) + if limit is not None and len(models) >= limit: + break + return models + + +def build_windows(item_count: int) -> list[tuple[str, int, int]]: + windows = [] + index = 1 + for start in range(0, item_count - WINDOW_SIZE + 1, WINDOW_STEP): + end = start + WINDOW_SIZE + windows.append((f"50_{index:03d}", start, end)) + index += 1 + return windows + + +def subset_payload(base_payload: dict, subset_name: str, start: int, end: int) -> dict: + subset_items = base_payload["items"][start:end] + return { + "model": base_payload.get("model", ""), + "dataset": base_payload.get("dataset", "rand_chinese"), + "count": len(subset_items), + "subset": subset_name, + "subset_start": start, + "subset_end": end, + "items": subset_items, + } + + +def save_subset_responses(path: Path, payload: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + + +def build_config(model_name: str, output_dir: Path, output_path: Path, sample_count: int) -> DNAExtractionConfig: + return DNAExtractionConfig( + model_name=model_name, + model_type="openrouter", + dataset="rand_chinese", + probe_set="rand", + max_samples=sample_count, + extractor_type="embedding", + dna_dim=128, + reduction_method="random_projection", + embedding_merge="concat", + output_dir=output_dir, + output_path=output_path, + save=True, + device="cpu", + random_seed=42, + max_length=1024, + ) + + +def main() -> int: + limit = None if len(sys.argv) <= 1 or sys.argv[1] == "all" else int(sys.argv[1]) + run_dir = Path(sys.argv[2]) if len(sys.argv) > 2 else default_run_dir() + run_dir.mkdir(parents=True, exist_ok=True) + log_file = run_dir / "run.log" + distance_file = run_dir / "distance_summary.json" + log_file.write_text("", encoding="utf-8") + + os.environ.setdefault("HF_HUB_OFFLINE", "1") + os.environ.setdefault("TRANSFORMERS_OFFLINE", "1") + logging.getLogger().setLevel(logging.WARNING) + + models = load_models(limit) + if not models: + raise SystemExit(f"no models with at least {WINDOW_SIZE} non-empty responses found in {DATA_DIR}") + + log(f"loaded {len(models)} model(s)", log_file) + all_distances: dict[str, dict[str, object]] = {} + + for index, (model_key, payload) in enumerate(models, 1): + log(f"processing {index}/{len(models)}: {model_key}", log_file) + signatures = {} + subset_dirs = {} + windows = build_windows(len(payload["items"])) + log(f" windows={len(windows)}", log_file) + + for subset_name, start, end in windows: + tagged_key = f"{model_key}__subset_{subset_name}" + subset_dir = run_dir / tagged_key + subset_dirs[subset_name] = subset_dir + subset_data = subset_payload(payload, subset_name, start, end) + save_subset_responses(subset_dir / "responses.json", subset_data) + + output_path = subset_dir / f"{tagged_key}_dna.json" + config = build_config( + model_name=payload.get("model", model_key), + output_dir=run_dir, + output_path=output_path, + sample_count=len(subset_data["items"]), + ) + signature, _, elapsed = _extract_signature_from_text_responses( + model_name=payload.get("model", model_key), + responses=[item["response"] for item in subset_data["items"]], + config=config, + model_meta=_default_model_metadata(payload.get("model", model_key)), + generation_device="cpu", + sentence_encoder=ENCODER_NAME, + encoder_device="cpu", + ) + _save_signature_outputs( + signature=signature, + config=config, + output_path=output_path, + summary_path=output_path.with_name(f"{output_path.stem}_summary.json"), + elapsed_seconds=elapsed, + ) + signatures[subset_name] = signature + + pairwise = {} + subset_names = list(signatures) + cosine_values = [] + euclidean_values = [] + for i in range(len(subset_names)): + for j in range(i + 1, len(subset_names)): + a = subset_names[i] + b = subset_names[j] + cosine = signatures[a].distance_to(signatures[b], metric="cosine") + euclidean = signatures[a].distance_to(signatures[b], metric="euclidean") + pairwise[f"{a}__{b}"] = {"cosine": cosine, "euclidean": euclidean} + cosine_values.append(cosine) + euclidean_values.append(euclidean) + + all_distances[model_key] = { + "subset_dirs": {name: str(path) for name, path in subset_dirs.items()}, + "windows": [ + {"name": subset_name, "start": start, "end": end} + for subset_name, start, end in windows + ], + "pairwise": pairwise, + "summary": { + "window_size": WINDOW_SIZE, + "window_step": WINDOW_STEP, + "window_count": len(windows), + "mean_cosine": sum(cosine_values) / len(cosine_values) if cosine_values else 0.0, + "max_cosine": max(cosine_values) if cosine_values else 0.0, + "mean_euclidean": sum(euclidean_values) / len(euclidean_values) if euclidean_values else 0.0, + "max_euclidean": max(euclidean_values) if euclidean_values else 0.0, + }, + } + log( + f"finished {model_key}: " + f"window_count={len(windows)}, " + f"mean_cos={all_distances[model_key]['summary']['mean_cosine']:.4f}, " + f"max_cos={all_distances[model_key]['summary']['max_cosine']:.4f}", + log_file, + ) + + distance_file.write_text(json.dumps(all_distances, ensure_ascii=False, indent=2), encoding="utf-8") + log(f"saved distance summary to {distance_file}", log_file) + log(f"saved run log to {log_file}", log_file) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/analyze_tree_aware_errors.py b/scripts/analyze_tree_aware_errors.py new file mode 100644 index 0000000..8fa2a9f --- /dev/null +++ b/scripts/analyze_tree_aware_errors.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +"""Analyze exact-classification errors and DNA distance by model-tree distance.""" + +from __future__ import annotations + +import argparse +import csv +import json +from collections import Counter, deque +from pathlib import Path + +import numpy as np + +from evaluate_exact_dna_classification import ( + cosine_scores, + load_records_for_suffix, + parse_run_spec, + select_common_cohort, +) + + +def load_components(path: Path | None) -> dict[str, int]: + lookup: dict[str, int] = {} + if path is None: + return lookup + for index, line in enumerate(path.read_text(encoding="utf-8").splitlines()): + if not line.strip(): + continue + row = json.loads(line) + for model in row.get("models", []): + lookup[str(model)] = index + return lookup + + +def load_graph(path: Path | None) -> dict[str, set[str]]: + graph: dict[str, set[str]] = {} + if path is None or not path.exists(): + return graph + for line in path.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + row = json.loads(line) + left = str(row.get("source", "")).strip() + right = str(row.get("target", "")).strip() + if left and right: + graph.setdefault(left, set()).add(right) + graph.setdefault(right, set()).add(left) + return graph + + +def shortest_paths(graph: dict[str, set[str]], models: list[str]) -> dict[tuple[str, str], int]: + allowed = set(models) + distances: dict[tuple[str, str], int] = {} + for source in models: + queue = deque([(source, 0)]) + seen = {source} + while queue: + node, distance = queue.popleft() + if node in allowed: + distances[(source, node)] = distance + for neighbor in graph.get(node, set()): + if neighbor not in seen: + seen.add(neighbor) + queue.append((neighbor, distance + 1)) + return distances + + +def relation_bucket( + left: str, + right: str, + paths: dict[tuple[str, str], int], + components: dict[str, int], +) -> str: + if left == right: + return "exact" + distance = paths.get((left, right)) + if distance == 1: + return "direct_1hop" + if distance == 2: + return "indirect_2hop" + if distance is not None: + return "indirect_3plus" + if left in components and components.get(left) == components.get(right): + return "same_component_unknown_hops" + return "disconnected" + + +def analyze_run( + labels: list[str], + reference_matrix: np.ndarray, + query_matrix: np.ndarray, + paths: dict[tuple[str, str], int], + components: dict[str, int], +) -> tuple[dict[str, object], list[dict[str, object]], list[dict[str, object]]]: + scores = cosine_scores(query_matrix, reference_matrix) + distances = 1.0 - scores + error_counts: Counter[str] = Counter() + predictions: list[dict[str, object]] = [] + pair_values: dict[str, list[float]] = {} + for row_index, truth in enumerate(labels): + prediction = labels[int(np.argmax(scores[row_index]))] + bucket = relation_bucket(truth, prediction, paths, components) + error_counts[bucket] += 1 + predictions.append({"model": truth, "predicted_model": prediction, "relation_bucket": bucket}) + for column_index, candidate in enumerate(labels): + pair_bucket = relation_bucket(truth, candidate, paths, components) + pair_values.setdefault(pair_bucket, []).append(float(distances[row_index, column_index])) + + count = len(labels) + exact = error_counts["exact"] + direct = error_counts["direct_1hop"] + two_hop = error_counts["indirect_2hop"] + component = error_counts["indirect_3plus"] + error_counts["same_component_unknown_hops"] + summary: dict[str, object] = { + "model_count": count, + "exact_top1": exact / count, + "within_1hop_top1": (exact + direct) / count, + "within_2hop_top1": (exact + direct + two_hop) / count, + "same_component_top1": (exact + direct + two_hop + component) / count, + **{f"top1_error_{key}": value for key, value in sorted(error_counts.items())}, + } + distance_rows = [ + { + "relation_bucket": bucket, + "ordered_pair_count": len(values), + "mean_cosine_distance": float(np.mean(values)), + "median_cosine_distance": float(np.median(values)), + "q25_cosine_distance": float(np.quantile(values, 0.25)), + "q75_cosine_distance": float(np.quantile(values, 0.75)), + } + for bucket, values in sorted(pair_values.items()) + ] + return summary, predictions, distance_rows + + +def write_csv(path: Path, rows: list[dict[str, object]]) -> None: + if not rows: + path.write_text("", encoding="utf-8") + return + fields: list[str] = [] + for row in rows: + fields.extend(key for key in row if key not in fields) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Tree-distance and hierarchical error analysis.") + parser.add_argument("--dataset-dir", type=Path, default=Path("out/rand_chinese")) + parser.add_argument("--reference-suffix", required=True) + parser.add_argument("--run", action="append", default=[], help="name:suffix:temperature:top_p:repeat") + parser.add_argument("--components-file", type=Path, default=Path("out/hf_model_tree/model_relations.jsonl")) + parser.add_argument("--edges-file", type=Path, default=Path("out/hf_model_tree/model_direct_edges.jsonl")) + parser.add_argument("--minimum-models", type=int, default=100) + parser.add_argument("--output-dir", type=Path, default=Path("out/tree_aware_errors")) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + runs = [parse_run_spec(value) for value in args.run] + if not runs: + raise SystemExit("At least one --run is required.") + reference = load_records_for_suffix(args.dataset_dir, args.reference_suffix) + records = {run.name: load_records_for_suffix(args.dataset_dir, run.suffix) for run in runs} + all_records = {"reference": reference, **records} + labels = select_common_cohort(all_records, all_records) + if len(labels) < args.minimum_models: + raise SystemExit( + f"Only {len(labels)} common models are available; minimum is {args.minimum_models}." + ) + components = load_components(args.components_file) + graph = load_graph(args.edges_file) + paths = shortest_paths(graph, labels) + reference_matrix = np.stack([reference[label].vector for label in labels]) + + summaries: list[dict[str, object]] = [] + predictions: list[dict[str, object]] = [] + distance_rows: list[dict[str, object]] = [] + for run in runs: + query = np.stack([records[run.name][label].vector for label in labels]) + summary, run_predictions, run_distances = analyze_run( + labels, reference_matrix, query, paths, components + ) + identity = { + "run": run.name, + "temperature": run.temperature, + "top_p": run.top_p, + "repeat": run.repeat, + } + summaries.append({**identity, **summary}) + predictions.extend({**identity, **row} for row in run_predictions) + distance_rows.extend({**identity, **row} for row in run_distances) + + args.output_dir.mkdir(parents=True, exist_ok=True) + write_csv(args.output_dir / "tree_aware_summary.csv", summaries) + write_csv(args.output_dir / "tree_aware_predictions.csv", predictions) + write_csv(args.output_dir / "tree_distance_distributions.csv", distance_rows) + payload = { + "cohort_size": len(labels), + "direct_edges_available": bool(graph), + "warning": None if graph else "Direct-edge export unavailable; same-component hop distances are unknown.", + "summaries": summaries, + "distance_distributions": distance_rows, + } + (args.output_dir / "tree_aware_summary.json").write_text( + json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8" + ) + print(json.dumps({"output_dir": str(args.output_dir), "cohort_size": len(labels)}, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/build_experiment_cohort.py b/scripts/build_experiment_cohort.py new file mode 100644 index 0000000..460053b --- /dev/null +++ b/scripts/build_experiment_cohort.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +"""Build a larger, reproducible model cohort from existing reference artifacts. + +Selection never uses evaluation accuracy. Eligible models must have a valid +unsuffixed reference DNA and recorded parameter count, then deterministic +round-robin sampling balances architecture and logarithmic size strata. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import re +from collections import defaultdict +from pathlib import Path + +import numpy as np + + +DEFAULT_RELIABILITY_REPLACEMENTS = { + "tiiuae/Falcon3-3B-Instruct": "princeton-nlp/Sheared-LLaMA-2.7B", + "sarvamai/sarvam-30b": "tiiuae/Falcon3-10B-Base", + "allenai/Olmo-3-7B-Instruct-DPO": "allenai/Llama-3.1-Tulu-3-8B", +} + + +def safe_model_name(model_name: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_name.strip("/")) + + +def load_catalog(path: Path) -> dict[str, dict[str, object]]: + output: dict[str, dict[str, object]] = {} + for line in path.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + row = json.loads(line) + model = str(row.get("model_id", "")).strip() + if model: + output[model] = row + return output + + +def load_eligible_references( + dataset_dir: Path, minimum_billions: float, maximum_billions: float +) -> tuple[dict[str, dict[str, object]], dict[str, str]]: + eligible: dict[str, dict[str, object]] = {} + excluded: dict[str, str] = {} + for path in sorted(dataset_dir.glob("*/*_dna.json")): + try: + payload = json.loads(path.read_text(encoding="utf-8")) + metadata = payload.get("metadata", {}) + model = str(metadata.get("model_name", "")).strip() + vector = np.asarray(payload.get("signature", []), dtype=np.float32) + except (OSError, ValueError, TypeError, json.JSONDecodeError): + continue + if not model or path.parent.name != safe_model_name(model): + continue + size = metadata.get("model_metadata", {}).get("size", {}).get("parameter_count_billions") + if not isinstance(size, (int, float)): + excluded[model] = "missing_parameter_count" + continue + if not minimum_billions <= float(size) <= maximum_billions: + excluded[model] = "outside_size_range" + continue + if vector.ndim != 1 or vector.size == 0 or not np.all(np.isfinite(vector)): + excluded[model] = "invalid_reference_dna" + continue + architecture = str( + metadata.get("model_metadata", {}).get("architecture", {}).get("model_type") or "unknown" + ) + eligible[model] = { + "parameter_count_billions": float(size), + "architecture": architecture, + "reference_path": str(path), + } + return eligible, excluded + + +def size_bucket(size_billions: float) -> str: + lower = 2 ** math.floor(math.log2(max(size_billions, 0.125))) + upper = lower * 2 + return f"{lower:g}-{upper:g}B" + + +def select_stratified( + eligible: dict[str, dict[str, object]], target: int, seed: int +) -> list[str]: + if target > len(eligible): + raise ValueError(f"Requested {target} models but only {len(eligible)} are eligible.") + strata: dict[tuple[str, str], list[str]] = defaultdict(list) + for model, metadata in eligible.items(): + key = (str(metadata["architecture"]), size_bucket(float(metadata["parameter_count_billions"]))) + strata[key].append(model) + rng = np.random.default_rng(seed) + for values in strata.values(): + rng.shuffle(values) + ordered_keys = sorted(strata, key=lambda key: (key[0], key[1])) + selected: list[str] = [] + while len(selected) < target: + made_progress = False + for key in ordered_keys: + if strata[key] and len(selected) < target: + selected.append(strata[key].pop()) + made_progress = True + if not made_progress: + break + return selected + + +def apply_reliability_replacements( + selected: list[str], + eligible: dict[str, dict[str, object]], + replacements: dict[str, str], +) -> tuple[list[str], list[dict[str, object]]]: + """Replace known generation failures without using evaluation accuracy.""" + output = list(selected) + audit_rows: list[dict[str, object]] = [] + for removed, replacement in replacements.items(): + if removed not in output: + continue + if replacement not in eligible: + raise ValueError(f"Reliability replacement is not eligible: {replacement}") + if replacement in output: + raise ValueError(f"Reliability replacement is already selected: {replacement}") + index = output.index(removed) + output[index] = replacement + audit_rows.append( + { + "removed_model": removed, + "removed_size_billions": eligible[removed]["parameter_count_billions"], + "replacement_model": replacement, + "replacement_size_billions": eligible[replacement]["parameter_count_billions"], + "reason": "repeated_failed_empty_response", + "validation": "cached baseline contains 100 non-empty responses", + } + ) + return output, audit_rows + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Create a stratified DNA experiment cohort.") + parser.add_argument("--dataset-dir", type=Path, default=Path("out/rand_chinese")) + parser.add_argument("--catalog", type=Path, default=Path("configs/huggingface_llm_list.jsonl")) + parser.add_argument("--target", type=int, default=300) + parser.add_argument("--minimum-billions", type=float, default=0.3) + parser.add_argument("--maximum-billions", type=float, default=7.5) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--output", type=Path, default=Path("configs/rand_chinese_stratified_300.jsonl")) + parser.add_argument("--audit-output", type=Path, default=Path("configs/rand_chinese_stratified_300_audit.json")) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + catalog = load_catalog(args.catalog) + eligible, excluded = load_eligible_references( + args.dataset_dir, args.minimum_billions, args.maximum_billions + ) + try: + selected = select_stratified(eligible, args.target, args.seed) + selected, reliability_replacements = apply_reliability_replacements( + selected, eligible, DEFAULT_RELIABILITY_REPLACEMENTS + ) + except ValueError as exc: + raise SystemExit(str(exc)) from exc + args.output.parent.mkdir(parents=True, exist_ok=True) + with args.output.open("w", encoding="utf-8") as handle: + for model in selected: + source = catalog.get(model, {}) + row = { + "model_id": model, + "provider": source.get("provider", "huggingface"), + "task": source.get("task", "text-generation"), + "downloads": int(source.get("downloads", 0) or 0), + "parameter_count_billions": eligible[model]["parameter_count_billions"], + "architecture": eligible[model]["architecture"], + } + handle.write(json.dumps(row, ensure_ascii=False) + "\n") + digest = hashlib.sha256(args.output.read_bytes()).hexdigest() + audit = { + "selection_uses_performance": False, + "selection_method": "seeded round-robin over architecture x logarithmic parameter-size strata, followed by documented reliability replacements that do not use evaluation accuracy", + "seed": args.seed, + "target": args.target, + "eligible_count": len(eligible), + "selected_count": len(selected), + "minimum_billions": args.minimum_billions, + "maximum_billions": args.maximum_billions, + "output_sha256": digest, + "post_selection_replacements": reliability_replacements, + "selected_models": selected, + "selected_metadata": {model: eligible[model] for model in selected}, + "exclusion_reason_counts": { + reason: sum(value == reason for value in excluded.values()) for reason in sorted(set(excluded.values())) + }, + } + args.audit_output.write_text(json.dumps(audit, indent=2, ensure_ascii=False), encoding="utf-8") + print(json.dumps({"output": str(args.output), "audit": str(args.audit_output), **{key: audit[key] for key in ["eligible_count", "selected_count", "output_sha256"]}}, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/calc_dna.py b/scripts/calc_dna.py index 99d3a46..5de1c3f 100644 --- a/scripts/calc_dna.py +++ b/scripts/calc_dna.py @@ -68,11 +68,51 @@ def main(): action="store_true", help="Continue processing remaining models if one fails (batch mode only)" ) + parser.add_argument( + "--try-vllm", + action="store_true", + help="Try vLLM first for decoder-only HuggingFace models" + ) + parser.add_argument( + "--temperature", + type=float, + default=0.0, + help="Generation temperature. Defaults to 0.0 to preserve deterministic historical runs.", + ) + parser.add_argument( + "--top-p", + type=float, + default=1.0, + help="Top-p nucleus sampling value used when sampling is enabled.", + ) + parser.add_argument( + "--do-sample", + action="store_true", + default=False, + help="Enable stochastic sampling during generation.", + ) + parser.add_argument( + "--no-do-sample", + dest="do_sample", + action="store_false", + help="Disable stochastic sampling during generation.", + ) parser.add_argument( "--no-save", action="store_true", help="Don't save results to disk" ) + parser.add_argument( + "--output-suffix", + type=str, + default="", + help="Suffix appended to the safe model output directory name, e.g. _2.", + ) + parser.add_argument( + "--ignore-response-cache", + action="store_true", + help="Regenerate responses even if responses.json already exists; new responses are still saved.", + ) args = parser.parse_args() # Set paths relative to project root @@ -94,8 +134,14 @@ def main(): data_root=data_root, metadata_file=metadata_file if metadata_file.exists() else None, output_dir=output_dir, + output_suffix=args.output_suffix, save=not args.no_save, trust_remote_code=True, + try_vllm=args.try_vllm, + temperature=args.temperature, + do_sample=args.do_sample, + top_p=args.top_p, + use_response_cache=not args.ignore_response_cache, ) # Batch mode: process multiple models from file @@ -140,4 +186,3 @@ def main(): if __name__ == "__main__": sys.exit(main()) - diff --git a/scripts/debug_openrouter_max_tokens.py b/scripts/debug_openrouter_max_tokens.py new file mode 100644 index 0000000..9ad0d67 --- /dev/null +++ b/scripts/debug_openrouter_max_tokens.py @@ -0,0 +1,204 @@ +#!/usr/bin/env python3 +"""Minimal OpenRouter max_tokens debugger.""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +from typing import Any + +from dotenv import load_dotenv +from openai import OpenAI + + +def _resolve_api_key_with_source() -> tuple[str, str]: + for key_name in ("OPENROUTER_API_KEY", "APIKEY_OPENROUTER", "OPENROUTER_KEY"): + value = os.getenv(key_name, "").strip() + if value: + return value, key_name + raise RuntimeError( + "OpenRouter API key not found. Set OPENROUTER_API_KEY (or APIKEY_OPENROUTER / OPENROUTER_KEY)." + ) + + +def _mask_secret(value: str) -> str: + if not value: + return "" + if len(value) <= 10: + return f"{value[:2]}...{value[-2:]}" + return f"{value[:6]}...{value[-4:]}" + + +def _parse_int_list(raw: str) -> list[int]: + values: list[int] = [] + for item in raw.split(","): + text = item.strip() + if not text: + continue + value = int(text) + if value <= 0: + raise ValueError(f"max_tokens values must be positive: {value}") + values.append(value) + if not values: + raise ValueError("No valid max_tokens values were provided.") + return values + + +def _extract_text(message: Any) -> str: + content = getattr(message, "content", "") + if isinstance(content, str): + return content.strip() + if isinstance(content, list): + parts: list[str] = [] + for item in content: + if isinstance(item, dict): + text = item.get("text") + if isinstance(text, str) and text.strip(): + parts.append(text.strip()) + return "\n".join(parts).strip() + return "" + + +def _classify_error(exc: Exception) -> str: + text = str(exc) + lower = text.lower() + + if "403" in lower and "key limit exceeded" in lower: + return "quota_exceeded" + if "429" in lower or "rate limit" in lower: + return "rate_limited" + if "401" in lower or "unauthorized" in lower or "invalid api key" in lower: + return "auth_error" + if "400" in lower and "max_tokens" in lower: + return "invalid_max_tokens" + return "request_error" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run one OpenRouter model with different max_tokens values.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--model", + default=(os.getenv("REPTRACE_OPENROUTER_TEST_MODEL", "").strip() or "openai/gpt-4o-mini"), + help="OpenRouter model id.", + ) + parser.add_argument( + "--prompt", + default="Reply with exactly: ACCESS_OK", + help="Prompt to send for each trial.", + ) + parser.add_argument( + "--max-tokens-list", + default="16,32,64,128,256,512,1024", + help="Comma-separated max_tokens values to test.", + ) + parser.add_argument("--temperature", type=float, default=0.0) + parser.add_argument("--top-p", type=float, default=1.0) + parser.add_argument("--timeout", type=float, default=120.0, help="Request timeout in seconds.") + parser.add_argument( + "--verbose", + action="store_true", + help="Print key source and request settings.", + ) + parser.add_argument("--http-referer", default=os.getenv("OPENROUTER_HTTP_REFERER", "").strip()) + parser.add_argument("--x-title", default=os.getenv("OPENROUTER_X_TITLE", "").strip()) + parser.add_argument( + "--show-text", + action="store_true", + help="Print full response text for each trial (instead of a preview).", + ) + return parser.parse_args() + + +def main() -> int: + load_dotenv(override=False) + args = parse_args() + + try: + max_tokens_values = _parse_int_list(args.max_tokens_list) + api_key, key_source = _resolve_api_key_with_source() + except Exception as exc: + print(f"[error] {exc}", file=sys.stderr) + return 2 + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1", + timeout=args.timeout, + ) + + extra_headers: dict[str, str] = {} + if args.http_referer: + extra_headers["HTTP-Referer"] = args.http_referer + if args.x_title: + extra_headers["X-Title"] = args.x_title + + print(f"model={args.model}") + print(f"prompt={args.prompt}") + print(f"max_tokens_list={max_tokens_values}") + if args.verbose: + print(f"key_source={key_source}") + print(f"key_masked={_mask_secret(api_key)}") + print(f"timeout={args.timeout}") + print(f"temperature={args.temperature}") + print(f"top_p={args.top_p}") + + print("-" * 90) + + for max_tokens in max_tokens_values: + try: + response = client.chat.completions.create( + model=args.model, + messages=[{"role": "user", "content": args.prompt}], + max_tokens=max_tokens, + temperature=args.temperature, + top_p=args.top_p, + extra_headers=extra_headers or None, + ) + + choice = response.choices[0] + finish_reason = getattr(choice, "finish_reason", None) + text = _extract_text(choice.message) + usage = getattr(response, "usage", None) + + usage_prompt = getattr(usage, "prompt_tokens", None) if usage else None + usage_completion = getattr(usage, "completion_tokens", None) if usage else None + usage_total = getattr(usage, "total_tokens", None) if usage else None + + summary = { + "max_tokens": max_tokens, + "finish_reason": finish_reason, + "has_content": bool(text), + "content_len": len(text), + "usage_prompt_tokens": usage_prompt, + "usage_completion_tokens": usage_completion, + "usage_total_tokens": usage_total, + } + print(json.dumps(summary, ensure_ascii=False)) + + if args.show_text: + print(text if text else "") + else: + preview = text[:160].replace("\n", "\\n") if text else "" + print(f"preview={preview}") + + except Exception as exc: + error_type = _classify_error(exc) + error_summary = { + "max_tokens": max_tokens, + "error_type": error_type, + "error": str(exc), + } + print(json.dumps(error_summary, ensure_ascii=False)) + + print("-" * 90) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/evaluate_dna_probe_sweep.py b/scripts/evaluate_dna_probe_sweep.py new file mode 100644 index 0000000..be4d3af --- /dev/null +++ b/scripts/evaluate_dna_probe_sweep.py @@ -0,0 +1,1413 @@ +#!/usr/bin/env python3 +"""Evaluate DNA identification robustness across generation settings. + +Each rerun suffix is treated as a query set. Query DNAs are matched against the +original/base DNAs by nearest distance, and optionally by a pairwise LinearSVC +probe trained on balanced positive/negative pairs. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import re +import warnings +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +import numpy as np +from sklearn.exceptions import ConvergenceWarning +from sklearn.metrics import ( + average_precision_score, + balanced_accuracy_score, + confusion_matrix, + matthews_corrcoef, + roc_auc_score, +) +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.svm import LinearSVC, SVC + + +@dataclass(frozen=True) +class RunSpec: + name: str + suffix: str + temperature: float | None + top_p: float | None + + +@dataclass(frozen=True) +class DnaRecord: + model_name: str + safe_name: str + path: Path + vector: np.ndarray + + +@dataclass(frozen=True) +class PairExample: + left_label: str + right_label: str + distance: float + target: int + + +def safe_model_name(model_name: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_name.strip("/")) + + +def parse_float_or_none(value: str) -> float | None: + value = value.strip() + if value in {"", "-", "none", "None", "null"}: + return None + return float(value) + + +def parse_run_spec(raw: str) -> RunSpec: + """Parse name:suffix:temperature:top_p.""" + parts = raw.split(":") + if len(parts) != 4: + raise ValueError( + f"Invalid --run {raw!r}. Expected format name:suffix:temperature:top_p, " + "for example temp07:_temp07_run:0.7:0.9" + ) + name, suffix, temperature, top_p = parts + if not name or not suffix: + raise ValueError(f"Invalid --run {raw!r}: name and suffix are required.") + return RunSpec( + name=name, + suffix=suffix, + temperature=parse_float_or_none(temperature), + top_p=parse_float_or_none(top_p), + ) + + +def load_dna(path: Path) -> DnaRecord: + payload = json.loads(path.read_text(encoding="utf-8")) + metadata = payload.get("metadata", {}) + model_name = str(metadata.get("model_name") or path.parent.name) + vector = np.asarray(payload["signature"], dtype=np.float32) + return DnaRecord( + model_name=model_name, + safe_name=safe_model_name(model_name), + path=path, + vector=vector, + ) + + +def load_records_for_suffix(dataset_dir: Path, suffix: str) -> dict[str, DnaRecord]: + records: dict[str, DnaRecord] = {} + pattern = "*" if suffix == "" else f"*{suffix}" + for model_dir in sorted(dataset_dir.glob(pattern)): + if not model_dir.is_dir(): + continue + if suffix == "" and re.search(r"_[0-9]+_run$|_temp.*_run$", model_dir.name): + continue + if suffix and not model_dir.name.endswith(suffix): + continue + dna_files = sorted(model_dir.glob("*_dna.json")) + if not dna_files: + continue + record = load_dna(dna_files[0]) + if suffix == "" and model_dir.name != record.safe_name: + # Base records must remain in the unsuffixed directory. + # Rerun directories carry the same metadata.model_name and would + # otherwise overwrite the real base record. + continue + records[record.model_name] = record + return records + + +def select_common_model_cohort( + base_records: dict[str, DnaRecord], + run_records: dict[str, dict[str, DnaRecord]], + minimum_run_models: int, +) -> tuple[dict[str, DnaRecord], dict[str, dict[str, DnaRecord]], dict[str, object]]: + """Keep sufficiently populated runs and restrict them to one shared cohort.""" + available_counts = { + run_name: len(set(base_records) & set(records)) + for run_name, records in run_records.items() + } + included_names = [ + run_name + for run_name in run_records + if available_counts[run_name] >= minimum_run_models + ] + excluded_names = [run_name for run_name in run_records if run_name not in included_names] + if not included_names: + raise ValueError( + f"No run has at least {minimum_run_models} models in the reference gallery." + ) + + common_models = set(base_records) + for run_name in included_names: + common_models &= set(run_records[run_name]) + if len(common_models) < 2: + raise ValueError( + "The selected runs have fewer than two common models; increase " + "--minimum-run-models or wait for more sweep outputs." + ) + + ordered_models = sorted(common_models) + filtered_base = {model: base_records[model] for model in ordered_models} + filtered_runs = { + run_name: {model: run_records[run_name][model] for model in ordered_models} + for run_name in included_names + } + metadata: dict[str, object] = { + "minimum_run_models": int(minimum_run_models), + "common_model_count": len(ordered_models), + "common_models": ordered_models, + "available_model_counts": available_counts, + "included_runs": included_names, + "excluded_runs": excluded_names, + } + return filtered_base, filtered_runs, metadata + + +def cosine_distance_matrix(query: np.ndarray, base: np.ndarray) -> np.ndarray: + query_norm = query / np.maximum(np.linalg.norm(query, axis=1, keepdims=True), 1e-12) + base_norm = base / np.maximum(np.linalg.norm(base, axis=1, keepdims=True), 1e-12) + return 1.0 - query_norm @ base_norm.T + + +def euclidean_distance_matrix(query: np.ndarray, base: np.ndarray) -> np.ndarray: + diff = query[:, None, :] - base[None, :, :] + return np.linalg.norm(diff, axis=2) + + +def macro_metrics(y_true: list[str], y_pred: list[str], labels: list[str]) -> dict[str, float]: + accuracy = sum(t == p for t, p in zip(y_true, y_pred)) / len(y_true) if y_true else 0.0 + precisions: list[float] = [] + recalls: list[float] = [] + f1s: list[float] = [] + for label in labels: + tp = sum(t == label and p == label for t, p in zip(y_true, y_pred)) + fp = sum(t != label and p == label for t, p in zip(y_true, y_pred)) + fn = sum(t == label and p != label for t, p in zip(y_true, y_pred)) + precision = 0.0 if tp + fp == 0 else tp / (tp + fp) + recall = 0.0 if tp + fn == 0 else tp / (tp + fn) + f1 = 0.0 if precision + recall == 0.0 else 2.0 * precision * recall / (precision + recall) + precisions.append(float(precision)) + recalls.append(float(recall)) + f1s.append(float(f1)) + return { + "accuracy": float(accuracy), + "precision_macro": float(np.mean(precisions)) if precisions else 0.0, + "recall_macro": float(np.mean(recalls)) if recalls else 0.0, + "f1_macro": float(np.mean(f1s)) if f1s else 0.0, + "precision_micro": float(accuracy), + "recall_micro": float(accuracy), + "f1_micro": float(accuracy), + } + + +def is_same_or_related(left: str, right: str, relation_lookup: dict[str, set[str]]) -> bool: + return left == right or right in relation_lookup.get(left, set()) or left in relation_lookup.get(right, set()) + + +def _split_relation_values(raw: str) -> list[str]: + return [item.strip() for item in re.split(r"[|,;]", raw) if item.strip()] + + +def load_relation_groups(path: Path | None) -> list[set[str]]: + """Load positive relation groups from JSON/JSONL/CSV/TSV. + + Supported forms: + - JSONL or JSON object with {"models": [...]} or {"source": ..., "target": ...} + - CSV/TSV rows with columns source,target or models + """ + if path is None: + return [] + text = path.read_text(encoding="utf-8").strip() + if not text: + return [] + + groups: list[set[str]] = [] + + def add_group(values: Iterable[str]) -> None: + group = {value.strip() for value in values if value.strip()} + if len(group) >= 2: + groups.append(group) + + def process_record(record: dict[str, object]) -> None: + if "models" in record: + models = record["models"] + if isinstance(models, str): + add_group(_split_relation_values(models)) + elif isinstance(models, list): + add_group(str(item) for item in models) + elif "source" in record and "target" in record: + add_group([str(record["source"]), str(record["target"])]) + elif "model" in record and "group" in record: + add_group([str(record["model"]), str(record["group"])]) + + suffix = path.suffix.lower() + if suffix in {".csv", ".tsv"}: + delimiter = "\t" if suffix == ".tsv" else "," + reader = csv.DictReader(text.splitlines(), delimiter=delimiter) + for row in reader: + process_record({key: value for key, value in row.items() if value is not None}) + return groups + + try: + payload = json.loads(text) + except json.JSONDecodeError: + payload = None + if isinstance(payload, dict): + process_record(payload) + return groups + if isinstance(payload, list): + for item in payload: + if isinstance(item, dict): + process_record(item) + return groups + + for raw_line in text.splitlines(): + line = raw_line.strip() + if not line: + continue + payload = json.loads(line) + if isinstance(payload, dict): + process_record(payload) + return groups + + +def build_relation_lookup(groups: list[set[str]]) -> dict[str, set[str]]: + lookup: dict[str, set[str]] = {} + for group in groups: + for model in group: + lookup.setdefault(model, set()).update(group - {model}) + return lookup + + +def build_pair_examples( + base_records: dict[str, DnaRecord], + query_records: dict[str, DnaRecord], + metric: str, + relation_lookup: dict[str, set[str]], + positive_mode: str = "same_or_related", +) -> list[PairExample]: + labels = sorted(set(base_records) & set(query_records)) + base_matrix = np.stack([base_records[label].vector for label in labels], axis=0) + query_matrix = np.stack([query_records[label].vector for label in labels], axis=0) + distances = cosine_distance_matrix(query_matrix, base_matrix) if metric == "cosine" else euclidean_distance_matrix(query_matrix, base_matrix) + + examples: list[PairExample] = [] + for query_idx, query_label in enumerate(labels): + for base_idx, base_label in enumerate(labels): + same = query_label == base_label + related = not same and is_same_or_related(query_label, base_label, relation_lookup) + if positive_mode == "exact_only": + if related: + continue + target = int(same) + elif positive_mode == "lineage_only": + if same: + continue + target = int(related) + elif positive_mode == "same_or_related": + target = int(same or related) + else: + raise ValueError(f"Unknown positive mode: {positive_mode}") + examples.append( + PairExample( + left_label=query_label, + right_label=base_label, + distance=float(distances[query_idx, base_idx]), + target=target, + ) + ) + return examples + + +def balance_examples(examples: list[PairExample], negative_ratio: float, seed: int = 42) -> tuple[list[PairExample], dict[str, int]]: + positives = [example for example in examples if example.target == 1] + negatives = [example for example in examples if example.target == 0] + if not positives or not negatives: + return examples, { + "positive_count": len(positives), + "negative_count": len(negatives), + "sampled_negative_count": len(negatives), + } + + rng = np.random.default_rng(seed) + target_negative_count = int(round(len(positives) * negative_ratio)) + target_negative_count = max(1, min(len(negatives), target_negative_count)) + negative_indices = rng.choice(len(negatives), size=target_negative_count, replace=False) + sampled_negatives = [negatives[int(idx)] for idx in sorted(negative_indices)] + balanced = positives + sampled_negatives + rng.shuffle(balanced) + return balanced, { + "positive_count": len(positives), + "negative_count": len(negatives), + "sampled_negative_count": len(sampled_negatives), + } + + +def binary_metrics(y_true: list[int], y_pred: list[int], y_score: list[float]) -> dict[str, float]: + if not y_true: + return { + "accuracy": 0.0, + "balanced_accuracy": 0.0, + "precision": 0.0, + "recall": 0.0, + "f1": 0.0, + "specificity": 0.0, + "mcc": 0.0, + "roc_auc": float("nan"), + "average_precision": float("nan"), + "tp": 0.0, + "tn": 0.0, + "fp": 0.0, + "fn": 0.0, + } + + tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel() + precision = 0.0 if tp + fp == 0 else tp / (tp + fp) + recall = 0.0 if tp + fn == 0 else tp / (tp + fn) + specificity = 0.0 if tn + fp == 0 else tn / (tn + fp) + f1 = 0.0 if precision + recall == 0.0 else 2.0 * precision * recall / (precision + recall) + accuracy = sum(t == p for t, p in zip(y_true, y_pred)) / len(y_true) + balanced = balanced_accuracy_score(y_true, y_pred) + mcc = matthews_corrcoef(y_true, y_pred) + try: + roc_auc = roc_auc_score(y_true, y_score) + except ValueError: + roc_auc = float("nan") + try: + average_precision = average_precision_score(y_true, y_score) + except ValueError: + average_precision = float("nan") + + return { + "accuracy": float(accuracy), + "balanced_accuracy": float(balanced), + "precision": float(precision), + "recall": float(recall), + "f1": float(f1), + "specificity": float(specificity), + "mcc": float(mcc), + "roc_auc": float(roc_auc), + "average_precision": float(average_precision), + "tp": float(tp), + "tn": float(tn), + "fp": float(fp), + "fn": float(fn), + } + + +def bootstrap_mean_interval( + values: Iterable[float], + *, + confidence: float = 0.95, + samples: int = 2000, + seed: int = 42, +) -> tuple[float, float]: + """Return a deterministic percentile bootstrap CI for a model-level mean.""" + array = np.asarray(list(values), dtype=np.float64) + array = array[np.isfinite(array)] + if array.size == 0: + return float("nan"), float("nan") + if array.size == 1 or samples <= 0: + value = float(array[0]) + return value, value + rng = np.random.default_rng(seed) + draw_indices = rng.integers(0, array.size, size=(samples, array.size)) + means = np.mean(array[draw_indices], axis=1) + tail = (1.0 - confidence) / 2.0 + low, high = np.quantile(means, [tail, 1.0 - tail]) + return float(low), float(high) + + +def wilson_interval(successes: int, total: int, confidence: float = 0.95) -> tuple[float, float]: + """Wilson score interval without requiring scipy.""" + if total <= 0: + return float("nan"), float("nan") + # 1.959963984540054 is the two-sided 95% standard-normal quantile. The + # CLI currently exposes 95% intervals; reject silent misuse for other CIs. + if not math.isclose(confidence, 0.95): + raise ValueError("wilson_interval currently supports confidence=0.95 only") + z = 1.959963984540054 + proportion = successes / total + denominator = 1.0 + z * z / total + centre = (proportion + z * z / (2.0 * total)) / denominator + radius = z * math.sqrt(proportion * (1.0 - proportion) / total + z * z / (4.0 * total * total)) / denominator + return float(max(0.0, centre - radius)), float(min(1.0, centre + radius)) + + +def evaluate_nearest( + base_records: dict[str, DnaRecord], + query_records: dict[str, DnaRecord], + metric: str, + top_ks: Iterable[int], + bootstrap_samples: int = 2000, + seed: int = 42, +) -> tuple[dict[str, float | int], list[dict[str, object]]]: + labels = sorted(set(base_records) & set(query_records)) + base_matrix = np.stack([base_records[label].vector for label in labels], axis=0) + query_matrix = np.stack([query_records[label].vector for label in labels], axis=0) + distances = cosine_distance_matrix(query_matrix, base_matrix) if metric == "cosine" else euclidean_distance_matrix(query_matrix, base_matrix) + + y_true: list[str] = [] + y_pred: list[str] = [] + rows: list[dict[str, object]] = [] + top_hits = {int(k): 0 for k in top_ks} + reciprocal_ranks: list[float] = [] + self_distances: list[float] = [] + nearest_distances: list[float] = [] + margins: list[float] = [] + + for idx, label in enumerate(labels): + order = list(np.argsort(distances[idx])) + pred = labels[order[0]] + rank = order.index(idx) + 1 + sorted_distances = distances[idx][order] + self_distance = float(distances[idx, idx]) + nearest_distance = float(sorted_distances[0]) + second_distance = float(sorted_distances[1]) if len(sorted_distances) > 1 else float("nan") + margin = second_distance - nearest_distance if np.isfinite(second_distance) else float("nan") + + y_true.append(label) + y_pred.append(pred) + reciprocal_ranks.append(1.0 / rank) + self_distances.append(self_distance) + nearest_distances.append(nearest_distance) + margins.append(margin) + for k in top_hits: + if rank <= k: + top_hits[k] += 1 + rows.append( + { + "model": label, + "predicted_model": pred, + "correct_top1": pred == label, + "self_rank": rank, + "self_distance": self_distance, + "nearest_distance": nearest_distance, + "nearest_margin": margin, + "top3_hit": rank <= 3, + "top5_hit": rank <= 5, + } + ) + + metrics = macro_metrics(y_true, y_pred, labels) + metrics.update( + { + "model_count": len(labels), + "top1": metrics["accuracy"], + "mrr": float(np.mean(reciprocal_ranks)) if reciprocal_ranks else 0.0, + "mean_self_distance": float(np.mean(self_distances)) if self_distances else float("nan"), + "median_self_distance": float(np.median(self_distances)) if self_distances else float("nan"), + "mean_nearest_distance": float(np.mean(nearest_distances)) if nearest_distances else float("nan"), + "mean_nearest_margin": float(np.nanmean(margins)) if margins else float("nan"), + } + ) + mrr_low, mrr_high = bootstrap_mean_interval( + reciprocal_ranks, + samples=bootstrap_samples, + seed=seed, + ) + metrics["mrr_ci95_low"] = mrr_low + metrics["mrr_ci95_high"] = mrr_high + for k, count in top_hits.items(): + metrics[f"top{k}"] = count / len(labels) if labels else 0.0 + metrics[f"top{k}_count"] = count + low, high = wilson_interval(count, len(labels)) + metrics[f"top{k}_ci95_low"] = low + metrics[f"top{k}_ci95_high"] = high + return metrics, rows + + +def evaluate_pairwise_svm( + base_records: dict[str, DnaRecord], + run_records: dict[str, dict[str, DnaRecord]], + test_run_name: str, + metric: str, + relation_lookup: dict[str, set[str]], + negative_ratio: float, + test_negative_ratio: float | None = 1.0, + positive_mode: str = "same_or_related", +) -> dict[str, float | int | str]: + test_records = run_records[test_run_name] + labels = sorted(set(base_records) & set(test_records)) + if len(labels) < 2: + return {"linear_status": "skipped_too_few_classes"} + + train_examples: list[PairExample] = [] + for run_name, records in run_records.items(): + if run_name == test_run_name: + continue + examples = build_pair_examples(base_records, records, metric, relation_lookup, positive_mode) + train_examples.extend(examples) + + test_examples = build_pair_examples(base_records, test_records, metric, relation_lookup, positive_mode) + if not train_examples or not test_examples: + return {"linear_status": "skipped_empty_pairs"} + + balanced_train, train_stats = balance_examples(train_examples, negative_ratio) + if test_negative_ratio is None: + balanced_test = list(test_examples) + test_stats = { + "positive_count": sum(example.target == 1 for example in test_examples), + "negative_count": sum(example.target == 0 for example in test_examples), + "sampled_negative_count": sum(example.target == 0 for example in test_examples), + } + else: + balanced_test, test_stats = balance_examples(test_examples, negative_ratio=test_negative_ratio) + + train_targets = [example.target for example in balanced_train] + test_targets = [example.target for example in balanced_test] + if len(set(train_targets)) < 2 or len(set(test_targets)) < 2: + return {"linear_status": "skipped_too_few_binary_classes"} + + train_x = np.array([[-example.distance] for example in balanced_train], dtype=np.float32) + test_x = np.array([[-example.distance] for example in balanced_test], dtype=np.float32) + + clf = make_pipeline( + StandardScaler(), + LinearSVC(C=1.0, class_weight="balanced", dual="auto", max_iter=20000, random_state=42), + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", ConvergenceWarning) + warnings.simplefilter("ignore", UserWarning) + clf.fit(train_x, train_targets) + + y_pred = list(clf.predict(test_x)) + decision_scores = list(clf.decision_function(test_x)) + metrics = {f"linear_{key}": value for key, value in binary_metrics(test_targets, y_pred, decision_scores).items()} + metrics.update( + { + "linear_status": "ok", + "linear_train_samples": len(balanced_train), + "linear_train_positive_samples": train_stats["positive_count"], + "linear_train_negative_samples": train_stats["negative_count"], + "linear_train_sampled_negative_samples": train_stats["sampled_negative_count"], + "linear_test_samples": len(balanced_test), + "linear_test_positive_samples": test_stats["positive_count"], + "linear_test_negative_samples": test_stats["negative_count"], + "linear_test_sampled_negative_samples": test_stats["sampled_negative_count"], + "linear_positive_rate_train": float(train_stats["positive_count"] / max(train_stats["positive_count"] + train_stats["sampled_negative_count"], 1)), + "linear_positive_rate_test": float(test_stats["positive_count"] / max(test_stats["positive_count"] + test_stats["sampled_negative_count"], 1)), + "linear_min_train_per_class": min(train_targets.count(0), train_targets.count(1)), + "linear_mean_train_per_class": float(np.mean([train_targets.count(0), train_targets.count(1)])), + "linear_feature": metric, + "linear_negative_ratio": float(negative_ratio), + "linear_test_negative_ratio": "all" if test_negative_ratio is None else float(test_negative_ratio), + "linear_positive_mode": positive_mode, + } + ) + return metrics + + +def evaluate_pairwise_kernel_svm( + base_records: dict[str, DnaRecord], + run_records: dict[str, dict[str, DnaRecord]], + test_run_name: str, + metric: str, + relation_lookup: dict[str, set[str]], + negative_ratio: float, + test_negative_ratio: float | None = 1.0, + kernel: str = "rbf", + gamma: float | str | None = "scale", + c: float = 1.0, + positive_mode: str = "same_or_related", +) -> dict[str, float | int | str]: + test_records = run_records[test_run_name] + labels = sorted(set(base_records) & set(test_records)) + if len(labels) < 2: + return {"kernel_status": "skipped_too_few_classes"} + + train_examples: list[PairExample] = [] + for run_name, records in run_records.items(): + if run_name == test_run_name: + continue + examples = build_pair_examples(base_records, records, metric, relation_lookup, positive_mode) + train_examples.extend(examples) + + test_examples = build_pair_examples(base_records, test_records, metric, relation_lookup, positive_mode) + if not train_examples or not test_examples: + return {"kernel_status": "skipped_empty_pairs"} + + balanced_train, train_stats = balance_examples(train_examples, negative_ratio) + if test_negative_ratio is None: + balanced_test = list(test_examples) + test_stats = { + "positive_count": sum(example.target == 1 for example in test_examples), + "negative_count": sum(example.target == 0 for example in test_examples), + "sampled_negative_count": sum(example.target == 0 for example in test_examples), + } + else: + balanced_test, test_stats = balance_examples(test_examples, negative_ratio=test_negative_ratio) + + train_targets = [example.target for example in balanced_train] + test_targets = [example.target for example in balanced_test] + if len(set(train_targets)) < 2 or len(set(test_targets)) < 2: + return {"kernel_status": "skipped_too_few_binary_classes"} + + train_x = np.array([[-example.distance] for example in balanced_train], dtype=np.float32) + test_x = np.array([[-example.distance] for example in balanced_test], dtype=np.float32) + + clf = SVC(C=c, kernel=kernel, gamma=gamma, class_weight="balanced", probability=False, random_state=42) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", ConvergenceWarning) + warnings.simplefilter("ignore", UserWarning) + clf.fit(train_x, train_targets) + + y_pred = list(clf.predict(test_x)) + decision_scores = list(clf.decision_function(test_x)) + metrics = {f"kernel_{key}": value for key, value in binary_metrics(test_targets, y_pred, decision_scores).items()} + metrics.update( + { + "kernel_status": "ok", + "kernel_train_samples": len(balanced_train), + "kernel_train_positive_samples": train_stats["positive_count"], + "kernel_train_negative_samples": train_stats["negative_count"], + "kernel_train_sampled_negative_samples": train_stats["sampled_negative_count"], + "kernel_test_samples": len(balanced_test), + "kernel_test_positive_samples": test_stats["positive_count"], + "kernel_test_negative_samples": test_stats["negative_count"], + "kernel_test_sampled_negative_samples": test_stats["sampled_negative_count"], + "kernel_positive_rate_train": float(train_stats["positive_count"] / max(train_stats["positive_count"] + train_stats["sampled_negative_count"], 1)), + "kernel_positive_rate_test": float(test_stats["positive_count"] / max(test_stats["positive_count"] + test_stats["sampled_negative_count"], 1)), + "kernel_min_train_per_class": min(train_targets.count(0), train_targets.count(1)), + "kernel_mean_train_per_class": float(np.mean([train_targets.count(0), train_targets.count(1)])), + "kernel_feature": metric, + "kernel_negative_ratio": float(negative_ratio), + "kernel_test_negative_ratio": "all" if test_negative_ratio is None else float(test_negative_ratio), + "kernel_positive_mode": positive_mode, + "kernel": kernel, + "kernel_gamma": float(gamma) if isinstance(gamma, (int, float)) else str(gamma), + "kernel_c": float(c), + } + ) + return metrics + + +def load_font(size: int): + from PIL import ImageFont + + for path in [ + "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", + "/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf", + ]: + if Path(path).exists(): + return ImageFont.truetype(path, size=size) + return ImageFont.load_default() + + +def metric_display_name(metric: str) -> str: + return { + "top1_mean": "TOP1", + "top3_mean": "TOP3", + "top5_mean": "TOP5", + "mrr_mean": "MRR", + "linear_accuracy_mean": "LIN ACC", + "linear_balanced_accuracy_mean": "LIN BAL", + "kernel_accuracy_mean": "KER ACC", + "kernel_balanced_accuracy_mean": "KER BAL", + }.get(metric, metric) + + +def build_setting_grid( + rows: list[dict[str, object]], + metric: str, +) -> tuple[list[float], list[float], np.ndarray]: + temperatures = sorted({float(row["temperature"]) for row in rows if row.get("temperature") is not None}) + top_ps = sorted({float(row["top_p"]) for row in rows if row.get("top_p") is not None}) + values = np.full((len(temperatures), len(top_ps)), np.nan, dtype=np.float32) + temp_index = {temperature: idx for idx, temperature in enumerate(temperatures)} + top_p_index = {top_p: idx for idx, top_p in enumerate(top_ps)} + metric_key = metric if metric.endswith("_mean") else f"{metric}_mean" + for row in rows: + if row.get("temperature") is None or row.get("top_p") is None: + continue + if metric_key not in row or row[metric_key] is None: + continue + values[temp_index[float(row["temperature"])], top_p_index[float(row["top_p"])]] = float(row[metric_key]) + return temperatures, top_ps, values + + +def plot_setting_heatmap_grid( + rows: list[dict[str, object]], + metrics: list[str], + output_path: Path, + title: str, +) -> None: + import html + import subprocess + + metric_rows = [] + for metric in metrics: + metric_key = metric if metric.endswith("_mean") else f"{metric}_mean" + if any(metric_key in row for row in rows): + metric_rows.append(metric) + if not metric_rows: + return + + temperatures = sorted({float(row["temperature"]) for row in rows if row.get("temperature") is not None}) + top_ps = sorted({float(row["top_p"]) for row in rows if row.get("top_p") is not None}) + if not temperatures or not top_ps: + return + + cell = max(58, min(78, 360 // max(len(temperatures), len(top_ps), 1))) + panel_w = 54 + len(top_ps) * cell + 24 + panel_h = 42 + len(temperatures) * cell + 48 + # A single landscape row makes cross-metric comparison easier in reports. + cols = min(4, len(metric_rows)) + rows_count = int(math.ceil(len(metric_rows) / cols)) + left_margin = 48 + top_margin = 88 + gap_x = 28 + gap_y = 30 + width = left_margin * 2 + cols * panel_w + (cols - 1) * gap_x + height = top_margin + rows_count * panel_h + (rows_count - 1) * gap_y + 50 + + def esc(text: object) -> str: + return html.escape(str(text), quote=True) + + def rgb(color: tuple[int, int, int]) -> str: + return f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + + def text(x: float, y: float, value: object, size: int = 14, fill: tuple[int, int, int] = (0, 0, 0), weight: str = "normal", anchor: str = "start") -> str: + return ( + f'{esc(value)}' + ) + + parts: list[str] = [ + f'', + f'', + text(left_margin, 34, title, size=24, fill=(20, 20, 20), weight="bold"), + text(left_margin, 58, "GREEN = HIGHER SCORE; EACH CELL IS ANNOTATED.", size=12, fill=(70, 70, 70)), + ] + + for idx, metric in enumerate(metric_rows): + row_idx = idx // cols + col_idx = idx % cols + left = left_margin + col_idx * (panel_w + gap_x) + top = top_margin + row_idx * (panel_h + gap_y) + right = left + panel_w + bottom = top + panel_h + _, _, values = build_setting_grid(rows, metric) + parts.append(f'') + parts.append(text(left + 12, top + 22, metric_display_name(metric), size=16, fill=(25, 25, 25), weight="bold")) + parts.append(text(left + 10, top + 36, "SCORE", size=12, fill=(70, 70, 70))) + parts.append(text(right - 10, top + 36, "TOP_P", size=12, fill=(70, 70, 70), anchor="end")) + parts.append(text(left + 10, bottom - 12, "TEMPERATURE", size=12, fill=(70, 70, 70))) + + inner_left = left + 54 + inner_top = top + 42 + inner_right = right - 24 + inner_bottom = bottom - 48 + grid_w = max(inner_right - inner_left, 1) + grid_h = max(inner_bottom - inner_top, 1) + cell_w = grid_w / max(len(top_ps), 1) + cell_h = grid_h / max(len(temperatures), 1) + + for col, top_p in enumerate(top_ps): + x0 = inner_left + col * cell_w + x1 = inner_left + (col + 1) * cell_w + parts.append(text((x0 + x1) / 2, top + 40, f"{top_p:g}", size=12, fill=(60, 60, 60), anchor="middle")) + for row, temperature in enumerate(temperatures): + y0 = inner_top + row * cell_h + y1 = inner_top + (row + 1) * cell_h + parts.append(text(left + 42, (y0 + y1) / 2 + 4, f"{temperature:g}", size=12, fill=(60, 60, 60), anchor="end")) + + finite = values[np.isfinite(values)] + vmax = 1.0 + if finite.size: + vmax = max(1.0, float(np.max(finite))) + best_idx: tuple[int, int] | None = None + if finite.size: + best_idx = tuple(int(x) for x in np.unravel_index(int(np.nanargmax(values)), values.shape)) + + for row_idx2, _temperature in enumerate(temperatures): + for col_idx2, _top_p in enumerate(top_ps): + value = float(values[row_idx2, col_idx2]) + x0 = inner_left + col_idx2 * cell_w + y0 = inner_top + row_idx2 * cell_h + x1 = inner_left + (col_idx2 + 1) * cell_w + y1 = inner_top + (row_idx2 + 1) * cell_h + fill = rgb(color_for_score(value, vmax=vmax)) + parts.append( + f'' + ) + if best_idx is not None and (row_idx2, col_idx2) == best_idx: + parts.append( + f'' + ) + text_fill = (255, 255, 255) if sum(color_for_score(value, vmax=vmax)) < 360 else (20, 20, 20) + cell_text = "NA" if not np.isfinite(value) else f"{value:.3f}" + parts.append(text((x0 + x1) / 2, (y0 + y1) / 2 + 4, cell_text, size=12, fill=text_fill, anchor="middle")) + + parts.append("") + svg_text = "".join(parts) + output_path.parent.mkdir(parents=True, exist_ok=True) + svg_path = output_path.with_suffix(".svg") + svg_path.write_text(svg_text, encoding="utf-8") + subprocess.run( + ["ffmpeg", "-y", "-hide_banner", "-loglevel", "error", "-i", str(svg_path), str(output_path)], + check=True, + ) + try: + svg_path.unlink() + except FileNotFoundError: + pass + + +def aggregate_by_temperature(summary_rows: list[dict[str, object]]) -> list[dict[str, object]]: + groups: dict[float, list[dict[str, object]]] = {} + for row in summary_rows: + if row.get("temperature") is None: + continue + key = float(row["temperature"]) + groups.setdefault(key, []).append(row) + + metric_names = [ + "top1", + "top3", + "top5", + "mrr", + "precision_macro", + "recall_macro", + "f1_macro", + "mean_self_distance", + "mean_nearest_distance", + "mean_nearest_margin", + "linear_accuracy", + "linear_balanced_accuracy", + "kernel_accuracy", + "kernel_balanced_accuracy", + "linear_precision_macro", + "linear_recall_macro", + "linear_f1_macro", + "linear_precision", + "linear_recall", + "linear_f1", + "linear_specificity", + "linear_mcc", + "linear_roc_auc", + "linear_average_precision", + ] + + aggregate_rows: list[dict[str, object]] = [] + for temperature, rows in sorted(groups.items()): + out: dict[str, object] = { + "temperature": temperature, + "repeat_count": len(rows), + "mean_model_count": float(np.mean([float(row.get("model_count", 0.0)) for row in rows])), + } + for metric in metric_names: + values = [float(row[metric]) for row in rows if metric in row and row[metric] is not None] + if values: + out[f"{metric}_mean"] = float(np.mean(values)) + out[f"{metric}_std"] = float(np.std(values)) + aggregate_rows.append(out) + return aggregate_rows + + +def plot_metric_lines_by_top_p( + rows: list[dict[str, object]], + metrics: list[str], + output_path: Path, + title: str, +) -> None: + """Plot temperature trends with one panel per top-p and labeled metric lines.""" + import html + import subprocess + + if not rows: + return + top_ps = sorted({float(row["top_p"]) for row in rows if row.get("top_p") is not None}) + temperatures = sorted({float(row["temperature"]) for row in rows if row.get("temperature") is not None}) + if not top_ps or not temperatures: + return + + labels = { + "top1_mean": "Top-1", + "top3_mean": "Top-3", + "top5_mean": "Top-5", + "mrr_mean": "MRR", + "linear_accuracy_mean": "Linear SVM", + "kernel_accuracy_mean": "RBF SVM", + } + colors = ["#b63a2b", "#2468a9", "#23864b", "#d28b16"] + dashes = ["", "8 4", "3 3", "12 4 3 4"] + width = 1420 + height = 570 + outer_left = 76 + outer_right = 34 + top = 118 + bottom = 72 + gap = 28 + panel_w = (width - outer_left - outer_right - gap * (len(top_ps) - 1)) / len(top_ps) + panel_h = height - top - bottom + x_min = min(temperatures) + x_max = max(temperatures) + x_span = max(x_max - x_min, 1e-9) + + def esc(value: object) -> str: + return html.escape(str(value), quote=True) + + def text(x: float, y: float, value: object, size: int = 14, fill: str = "#202020", weight: str = "normal", anchor: str = "start") -> str: + return ( + f'{esc(value)}' + ) + + parts = [ + f'', + f'', + text(outer_left, 34, title, size=25, weight="bold"), + text(outer_left, 58, "Common-model cohort; x = temperature; y = score", size=13, fill="#5b5b57"), + ] + + legend_x = outer_left + for idx, metric in enumerate(metrics): + color = colors[idx % len(colors)] + dash = dashes[idx % len(dashes)] + x0 = legend_x + idx * 190 + dash_attr = f' stroke-dasharray="{dash}"' if dash else "" + parts.append(f'') + parts.append(f'') + parts.append(text(x0 + 44, 91, labels.get(metric, metric), size=13, weight="bold")) + + lookup = { + (float(row["temperature"]), float(row["top_p"])): row + for row in rows + if row.get("temperature") is not None and row.get("top_p") is not None + } + for panel_idx, top_p in enumerate(top_ps): + left = outer_left + panel_idx * (panel_w + gap) + right = left + panel_w + panel_bottom = top + panel_h + parts.append(f'') + parts.append(text(left + 12, top + 24, f"top_p = {top_p:g}", size=16, weight="bold")) + plot_left = left + 48 + plot_right = right - 18 + plot_top = top + 38 + plot_bottom = panel_bottom - 42 + for tick in [0.0, 0.25, 0.5, 0.75, 1.0]: + y = plot_bottom - tick * (plot_bottom - plot_top) + parts.append(f'') + if panel_idx == 0: + parts.append(text(plot_left - 9, y + 5, f"{tick:.2f}", size=12, fill="#66645f", anchor="end")) + for temperature in temperatures: + x = plot_left + (temperature - x_min) / x_span * (plot_right - plot_left) + parts.append(f'') + parts.append(text(x, plot_bottom + 22, f"{temperature:g}", size=12, fill="#66645f", anchor="middle")) + + for metric_idx, metric in enumerate(metrics): + color = colors[metric_idx % len(colors)] + dash = dashes[metric_idx % len(dashes)] + segments: list[list[tuple[float, float]]] = [] + current_segment: list[tuple[float, float]] = [] + for temperature in temperatures: + row = lookup.get((temperature, top_p)) + value = row.get(metric) if row else None + if value is None: + if current_segment: + segments.append(current_segment) + current_segment = [] + continue + score = min(max(float(value), 0.0), 1.0) + x = plot_left + (temperature - x_min) / x_span * (plot_right - plot_left) + y = plot_bottom - score * (plot_bottom - plot_top) + current_segment.append((x, y)) + if current_segment: + segments.append(current_segment) + if not segments: + continue + dash_attr = f' stroke-dasharray="{dash}"' if dash else "" + for segment in segments: + if len(segment) > 1: + coords = " ".join(f"{x:.1f},{y:.1f}" for x, y in segment) + parts.append(f'') + for x, y in segment: + parts.append(f'') + + parts.append(text((plot_left + plot_right) / 2, panel_bottom - 8, "temperature", size=12, fill="#55534f", anchor="middle")) + + parts.append("") + output_path.parent.mkdir(parents=True, exist_ok=True) + svg_path = output_path.with_suffix(".svg") + svg_path.write_text("".join(parts), encoding="utf-8") + subprocess.run( + ["ffmpeg", "-y", "-hide_banner", "-loglevel", "error", "-i", str(svg_path), str(output_path)], + check=True, + ) + svg_path.unlink(missing_ok=True) + + +def plot_faceted_response_summary(rows: list[dict[str, object]], output_path: Path) -> None: + plot_metric_lines_by_top_p( + rows, + ["top1_mean", "top3_mean", "top5_mean", "mrr_mean"], + output_path, + "Exact-model retrieval across decoding settings", + ) + + +def plot_probe_metric_summary(rows: list[dict[str, object]], output_path: Path) -> None: + plot_metric_lines_by_top_p( + rows, + ["linear_accuracy_mean", "kernel_accuracy_mean"], + output_path, + "Pairwise probe accuracy across decoding settings", + ) + + +def plot_metric_curves(summary_rows: list[dict[str, object]], output_path: Path) -> None: + if not summary_rows: + return + + has_pre_aggregated_metrics = any( + any(key in row for key in ["top1_mean", "top3_mean", "top5_mean", "mrr_mean"]) for row in summary_rows + ) + plot_rows = summary_rows if has_pre_aggregated_metrics else aggregate_by_setting(summary_rows) + + plot_faceted_response_summary(plot_rows, output_path) + probe_summary_path = output_path.with_name(output_path.stem + "_probe_metrics.png") + plot_probe_metric_summary(plot_rows, probe_summary_path) + + +def aggregate_by_setting(summary_rows: list[dict[str, object]]) -> list[dict[str, object]]: + groups: dict[tuple[float, float], list[dict[str, object]]] = {} + for row in summary_rows: + if row.get("temperature") is None or row.get("top_p") is None: + continue + key = (float(row["temperature"]), float(row["top_p"])) + groups.setdefault(key, []).append(row) + + metric_names = [ + "top1", + "top3", + "top5", + "mrr", + "precision_macro", + "recall_macro", + "f1_macro", + "mean_self_distance", + "mean_nearest_distance", + "mean_nearest_margin", + "linear_accuracy", + "linear_balanced_accuracy", + "kernel_accuracy", + "kernel_balanced_accuracy", + "linear_precision", + "linear_recall", + "linear_f1", + "linear_specificity", + "linear_mcc", + "linear_roc_auc", + "linear_average_precision", + ] + aggregate_rows: list[dict[str, object]] = [] + for (temperature, top_p), rows in sorted(groups.items()): + out: dict[str, object] = { + "temperature": temperature, + "top_p": top_p, + "repeat_count": len(rows), + "mean_model_count": float(np.mean([float(row.get("model_count", 0.0)) for row in rows])), + } + for metric in metric_names: + values = [float(row[metric]) for row in rows if metric in row and row[metric] is not None] + if values: + out[f"{metric}_mean"] = float(np.mean(values)) + out[f"{metric}_std"] = float(np.std(values)) + aggregate_rows.append(out) + return aggregate_rows + + +def build_best_setting_summary(aggregate_rows: list[dict[str, object]]) -> list[dict[str, object]]: + if not aggregate_rows: + return [] + + def score(row: dict[str, object]) -> tuple[float, float, float, float]: + return ( + float(row.get("top1_mean", -1.0)), + float(row.get("mrr_mean", -1.0)), + float(row.get("linear_accuracy_mean", -1.0)), + float(row.get("top3_mean", -1.0)), + ) + + best = max(aggregate_rows, key=score) + return [ + { + "temperature": float(best["temperature"]), + "top_p": float(best["top_p"]), + "repeat_count": int(best.get("repeat_count", 0)), + "mean_model_count": float(best.get("mean_model_count", float("nan"))), + "top1_mean": float(best.get("top1_mean", float("nan"))), + "top3_mean": float(best.get("top3_mean", float("nan"))), + "top5_mean": float(best.get("top5_mean", float("nan"))), + "mrr_mean": float(best.get("mrr_mean", float("nan"))), + "linear_accuracy_mean": float(best.get("linear_accuracy_mean", float("nan"))), + "linear_balanced_accuracy_mean": float(best.get("linear_balanced_accuracy_mean", float("nan"))), + "kernel_accuracy_mean": float(best.get("kernel_accuracy_mean", float("nan"))), + "kernel_balanced_accuracy_mean": float(best.get("kernel_balanced_accuracy_mean", float("nan"))), + } + ] + + +def color_for_score(value: float, vmin: float = 0.0, vmax: float = 1.0) -> tuple[int, int, int]: + if not np.isfinite(value): + return (220, 220, 220) + t = max(0.0, min(1.0, (value - vmin) / max(vmax - vmin, 1e-12))) + # Red -> yellow -> green. + if t < 0.5: + u = t / 0.5 + return (int(190 + u * 55), int(45 + u * 175), 55) + u = (t - 0.5) / 0.5 + return (int(245 - u * 185), int(220 - u * 60), int(55 + u * 55)) + + +def plot_setting_heatmap( + aggregate_rows: list[dict[str, object]], + metric: str, + output_path: Path, +) -> None: + plot_setting_heatmap_grid( + aggregate_rows, + [metric], + output_path, + f"{metric_display_name(metric)} MEAN BY TEMPERATURE AND TOP_P", + ) + + +def write_csv(path: Path, rows: list[dict[str, object]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + fields: list[str] = [] + for row in rows: + for key in row: + if key not in fields: + fields.append(key) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Evaluate DNA model-identification robustness across run suffixes.") + parser.add_argument("--dataset-dir", type=Path, default=Path("out/rand_chinese")) + parser.add_argument( + "--run", + action="append", + default=[], + help="Run spec name:suffix:temperature:top_p. Repeat for each generation setting.", + ) + parser.add_argument("--metric", choices=["cosine", "euclidean"], default="cosine") + parser.add_argument("--top-k", type=int, action="append", default=[1, 3, 5]) + parser.add_argument("--output-dir", type=Path, default=Path("out/dna_probe_sweep")) + parser.add_argument("--prefix", type=str, default="rand_chinese_probe_sweep") + parser.add_argument( + "--linear-svm", + action="store_true", + help="Also evaluate a balanced pairwise LinearSVC probe on positive/negative model pairs.", + ) + parser.add_argument( + "--kernel-svm", + action="store_true", + help="Also evaluate a balanced pairwise SVC probe with an RBF kernel on positive/negative model pairs.", + ) + parser.add_argument( + "--positive-relations-file", + type=Path, + default=None, + help="Optional JSON/JSONL/CSV/TSV file listing related models that should count as positive pairs.", + ) + parser.add_argument( + "--positive-mode", + choices=["same_or_related", "exact_only", "lineage_only"], + default="same_or_related", + help="Binary verification target. Related pairs are excluded from exact_only; diagonals are excluded from lineage_only.", + ) + parser.add_argument( + "--negative-ratio", + type=float, + default=1.0, + help="Negative samples to keep per positive pair when training the pairwise SVM.", + ) + parser.add_argument( + "--test-negative-ratio", + type=float, + default=0.0, + help="Negatives per positive in SVM testing; 0 (default) evaluates every natural-prevalence pair.", + ) + parser.add_argument( + "--common-models-only", + action="store_true", + help="Restrict every included run and the reference gallery to one shared model cohort.", + ) + parser.add_argument( + "--minimum-run-models", + type=int, + default=2, + help="With --common-models-only, exclude runs below this reference-intersection size.", + ) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + run_specs = [parse_run_spec(raw) for raw in args.run] + if not run_specs: + raise SystemExit("At least one --run is required.") + + base_records = load_records_for_suffix(args.dataset_dir, "") + if not base_records: + raise SystemExit(f"No base DNA records found under {args.dataset_dir}.") + + relation_lookup = build_relation_lookup(load_relation_groups(args.positive_relations_file)) + run_records = {spec.name: load_records_for_suffix(args.dataset_dir, spec.suffix) for spec in run_specs} + available_model_counts = { + run_name: len(set(base_records) & set(records)) + for run_name, records in run_records.items() + } + cohort_metadata: dict[str, object] | None = None + if args.common_models_only: + try: + base_records, run_records, cohort_metadata = select_common_model_cohort( + base_records, + run_records, + args.minimum_run_models, + ) + except ValueError as exc: + raise SystemExit(str(exc)) from exc + included_runs = set(run_records) + run_specs = [spec for spec in run_specs if spec.name in included_runs] + summary_rows: list[dict[str, object]] = [] + prediction_rows: list[dict[str, object]] = [] + + for spec in run_specs: + records = run_records[spec.name] + metrics, rows = evaluate_nearest(base_records, records, args.metric, args.top_k) + summary = { + "run": spec.name, + "suffix": spec.suffix, + "temperature": spec.temperature, + "top_p": spec.top_p, + "metric": args.metric, + "available_model_count": available_model_counts[spec.name], + "common_cohort": bool(args.common_models_only), + **metrics, + } + if args.linear_svm: + linear = evaluate_pairwise_svm( + base_records, + run_records, + spec.name, + args.metric, + relation_lookup, + args.negative_ratio, + None if args.test_negative_ratio == 0 else args.test_negative_ratio, + args.positive_mode, + ) + summary.update(linear) + if args.kernel_svm: + kernel = evaluate_pairwise_kernel_svm( + base_records, + run_records, + spec.name, + args.metric, + relation_lookup, + args.negative_ratio, + None if args.test_negative_ratio == 0 else args.test_negative_ratio, + positive_mode=args.positive_mode, + ) + summary.update(kernel) + summary_rows.append(summary) + for row in rows: + prediction_rows.append({"run": spec.name, "suffix": spec.suffix, "temperature": spec.temperature, "top_p": spec.top_p, **row}) + + args.output_dir.mkdir(parents=True, exist_ok=True) + summary_csv = args.output_dir / f"{args.prefix}_summary.csv" + predictions_csv = args.output_dir / f"{args.prefix}_predictions.csv" + summary_json = args.output_dir / f"{args.prefix}_summary.json" + temperature_csv = args.output_dir / f"{args.prefix}_aggregate_by_temperature.csv" + temperature_json = args.output_dir / f"{args.prefix}_aggregate_by_temperature.json" + aggregate_csv = args.output_dir / f"{args.prefix}_aggregate_by_setting.csv" + aggregate_json = args.output_dir / f"{args.prefix}_aggregate_by_setting.json" + curve_png = args.output_dir / f"{args.prefix}_curves.png" + best_setting_csv = args.output_dir / f"{args.prefix}_best_setting.csv" + best_setting_json = args.output_dir / f"{args.prefix}_best_setting.json" + common_models_json = args.output_dir / f"{args.prefix}_common_models.json" + common_models_csv = args.output_dir / f"{args.prefix}_common_models.csv" + + temperature_rows = aggregate_by_temperature(summary_rows) + aggregate_rows = aggregate_by_setting(summary_rows) + write_csv(summary_csv, summary_rows) + write_csv(predictions_csv, prediction_rows) + write_csv(temperature_csv, temperature_rows) + write_csv(aggregate_csv, aggregate_rows) + best_setting_rows = build_best_setting_summary(aggregate_rows) + write_csv(best_setting_csv, best_setting_rows) + summary_json.write_text(json.dumps(summary_rows, indent=2, ensure_ascii=False), encoding="utf-8") + temperature_json.write_text(json.dumps(temperature_rows, indent=2, ensure_ascii=False), encoding="utf-8") + aggregate_json.write_text(json.dumps(aggregate_rows, indent=2, ensure_ascii=False), encoding="utf-8") + best_setting_json.write_text(json.dumps(best_setting_rows, indent=2, ensure_ascii=False), encoding="utf-8") + if cohort_metadata is not None: + common_models_json.write_text(json.dumps(cohort_metadata, indent=2, ensure_ascii=False), encoding="utf-8") + write_csv( + common_models_csv, + [{"model_name": model} for model in cohort_metadata["common_models"]], + ) + line_paths: dict[str, str] = {} + try: + plot_metric_curves(summary_rows, curve_png) + for metric in ["top1", "top3", "top5", "mrr", "linear_accuracy", "linear_balanced_accuracy", "kernel_accuracy", "kernel_balanced_accuracy"]: + metric_output = args.output_dir / f"{args.prefix}_{metric}_setting_lines.png" + metric_key = f"{metric}_mean" + plot_metric_lines_by_top_p( + aggregate_rows, + [metric_key], + metric_output, + f"{metric_display_name(metric_key)} across decoding settings", + ) + if metric_output.exists(): + line_paths[metric] = str(metric_output) + except ImportError as exc: + print(f"[warn] plotting skipped because an image dependency is unavailable: {exc}") + + print( + json.dumps( + { + "summary_csv": str(summary_csv), + "predictions_csv": str(predictions_csv), + "summary_json": str(summary_json), + "temperature_csv": str(temperature_csv), + "temperature_json": str(temperature_json), + "aggregate_csv": str(aggregate_csv), + "aggregate_json": str(aggregate_json), + "best_setting_csv": str(best_setting_csv), + "best_setting_json": str(best_setting_json), + "common_models_json": str(common_models_json) if cohort_metadata is not None else None, + "common_models_csv": str(common_models_csv) if cohort_metadata is not None else None, + "curve_png": str(curve_png), + "setting_line_plots": line_paths, + "relations_file": str(args.positive_relations_file) if args.positive_relations_file else None, + "cohort": cohort_metadata, + "runs": summary_rows, + "aggregate_by_setting": aggregate_rows, + "best_setting": best_setting_rows, + }, + indent=2, + ensure_ascii=False, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/evaluate_exact_dna_classification.py b/scripts/evaluate_exact_dna_classification.py new file mode 100644 index 0000000..654857f --- /dev/null +++ b/scripts/evaluate_exact_dna_classification.py @@ -0,0 +1,405 @@ +#!/usr/bin/env python3 +"""Evaluate exact-model DNA classification under known and unknown decoding settings. + +Unlike the legacy pairwise verification experiment, every method here solves +the same closed-set task: rank the exact model among a fixed gallery. Training +and test generation repeats are supplied separately, and temperature/top-p are +never passed to the deployment classifier. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import re +import warnings +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +import numpy as np +from sklearn.exceptions import ConvergenceWarning +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.svm import LinearSVC, SVC + + +@dataclass(frozen=True) +class RunSpec: + name: str + suffix: str + temperature: float + top_p: float + repeat: int + + @property + def setting(self) -> tuple[float, float]: + return self.temperature, self.top_p + + +@dataclass(frozen=True) +class DnaRecord: + model_name: str + vector: np.ndarray + + +def parse_run_spec(raw: str) -> RunSpec: + parts = raw.split(":") + if len(parts) != 5: + raise ValueError( + f"Invalid run {raw!r}; expected name:suffix:temperature:top_p:repeat" + ) + name, suffix, temperature, top_p, repeat = parts + if not name or not suffix: + raise ValueError("Run name and suffix must be non-empty.") + return RunSpec(name, suffix, float(temperature), float(top_p), int(repeat)) + + +def safe_model_name(model_name: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_name.strip("/")) + + +def load_records_for_suffix(dataset_dir: Path, suffix: str) -> dict[str, DnaRecord]: + records: dict[str, DnaRecord] = {} + for model_dir in sorted(dataset_dir.glob(f"*{suffix}")): + if not model_dir.is_dir() or not model_dir.name.endswith(suffix): + continue + dna_files = sorted(model_dir.glob("*_dna.json")) + if not dna_files: + continue + payload = json.loads(dna_files[0].read_text(encoding="utf-8")) + model_name = str(payload.get("metadata", {}).get("model_name") or model_dir.name) + vector = np.asarray(payload["signature"], dtype=np.float32) + if vector.ndim != 1 or not np.all(np.isfinite(vector)): + continue + records[model_name] = DnaRecord(model_name, vector) + return records + + +def validate_split(train_runs: list[RunSpec], test_runs: list[RunSpec]) -> None: + train_suffixes = {run.suffix for run in train_runs} + test_suffixes = {run.suffix for run in test_runs} + overlap = sorted(train_suffixes & test_suffixes) + if overlap: + raise ValueError(f"Train/test response leakage: suffixes occur in both splits: {overlap}") + duplicate_names = len({run.name for run in train_runs + test_runs}) != len(train_runs + test_runs) + if duplicate_names: + raise ValueError("Every run name must be unique.") + + +def select_common_cohort( + records: dict[str, dict[str, DnaRecord]], run_names: Iterable[str] +) -> list[str]: + selected = list(run_names) + if not selected: + return [] + common = set(records[selected[0]]) + for name in selected[1:]: + common &= set(records[name]) + return sorted(common) + + +def cosine_scores(query: np.ndarray, gallery: np.ndarray) -> np.ndarray: + query = query / np.maximum(np.linalg.norm(query, axis=1, keepdims=True), 1e-12) + gallery = gallery / np.maximum(np.linalg.norm(gallery, axis=1, keepdims=True), 1e-12) + return query @ gallery.T + + +def bootstrap_mean_interval( + values: Iterable[float], samples: int = 2000, seed: int = 42 +) -> tuple[float, float]: + array = np.asarray(list(values), dtype=np.float64) + if array.size == 0: + return float("nan"), float("nan") + if array.size == 1 or samples <= 0: + return float(array[0]), float(array[0]) + rng = np.random.default_rng(seed) + means = np.mean(array[rng.integers(0, len(array), size=(samples, len(array)))], axis=1) + return tuple(float(value) for value in np.quantile(means, [0.025, 0.975])) + + +def ranking_metrics( + labels: list[str], scores: np.ndarray, classes: list[str], bootstrap_samples: int +) -> tuple[dict[str, float | int], list[dict[str, object]]]: + class_index = {label: index for index, label in enumerate(classes)} + rows: list[dict[str, object]] = [] + reciprocal_ranks: list[float] = [] + hits = {1: [], 3: [], 5: []} + for row_index, label in enumerate(labels): + order = np.argsort(-scores[row_index], kind="stable") + truth_index = class_index[label] + rank = int(np.flatnonzero(order == truth_index)[0]) + 1 + prediction = classes[int(order[0])] + reciprocal_ranks.append(1.0 / rank) + for k in hits: + hits[k].append(float(rank <= k)) + rows.append( + { + "model": label, + "predicted_model": prediction, + "correct_top1": prediction == label, + "self_rank": rank, + "reciprocal_rank": 1.0 / rank, + } + ) + metrics: dict[str, float | int] = {"model_count": len(labels)} + for k, values in hits.items(): + low, high = bootstrap_mean_interval(values, bootstrap_samples) + metrics[f"top{k}"] = float(np.mean(values)) + metrics[f"top{k}_ci95_low"] = low + metrics[f"top{k}_ci95_high"] = high + mrr_low, mrr_high = bootstrap_mean_interval(reciprocal_ranks, bootstrap_samples) + metrics.update( + { + "mrr": float(np.mean(reciprocal_ranks)), + "mrr_ci95_low": mrr_low, + "mrr_ci95_high": mrr_high, + "chance_top1": 1.0 / len(classes), + "chance_corrected_top1": (float(np.mean(hits[1])) - 1.0 / len(classes)) + / (1.0 - 1.0 / len(classes)), + } + ) + return metrics, rows + + +def training_arrays( + train_runs: list[RunSpec], records: dict[str, dict[str, DnaRecord]], cohort: list[str] +) -> tuple[np.ndarray, list[str]]: + vectors: list[np.ndarray] = [] + targets: list[str] = [] + for run in train_runs: + for model in cohort: + vectors.append(records[run.name][model].vector) + targets.append(model) + return np.stack(vectors), targets + + +def centroid_gallery( + train_runs: list[RunSpec], records: dict[str, dict[str, DnaRecord]], cohort: list[str] +) -> np.ndarray: + return np.stack( + [np.mean([records[run.name][model].vector for run in train_runs], axis=0) for model in cohort] + ) + + +def fit_classifiers(train_x: np.ndarray, train_y: list[str], methods: list[str]) -> dict[str, object]: + classifiers: dict[str, object] = {} + if "linear_svm" in methods: + classifiers["linear_svm"] = make_pipeline( + StandardScaler(), + LinearSVC(C=1.0, class_weight="balanced", dual="auto", max_iter=30000, random_state=42), + ) + if "rbf_svm" in methods: + classifiers["rbf_svm"] = make_pipeline( + StandardScaler(), + SVC(C=1.0, kernel="rbf", gamma="scale", class_weight="balanced", decision_function_shape="ovr"), + ) + for classifier in classifiers.values(): + with warnings.catch_warnings(): + warnings.simplefilter("ignore", ConvergenceWarning) + classifier.fit(train_x, train_y) + return classifiers + + +def classifier_scores(classifier: object, query: np.ndarray, cohort: list[str]) -> np.ndarray: + raw = np.asarray(classifier.decision_function(query)) + learned_classes = [str(value) for value in classifier.classes_] + if raw.ndim == 1: + raw = np.stack([-raw, raw], axis=1) + indices = [learned_classes.index(model) for model in cohort] + return raw[:, indices] + + +def evaluate_bound( + bound: float, + train_runs: list[RunSpec], + test_runs: list[RunSpec], + records: dict[str, dict[str, DnaRecord]], + methods: list[str], + minimum_models: int, + bootstrap_samples: int, +) -> tuple[list[dict[str, object]], list[dict[str, object]], dict[str, object]]: + eligible_train = [run for run in train_runs if run.temperature <= bound] + eligible_test = [run for run in test_runs if run.temperature <= bound] + if not eligible_train or not eligible_test: + raise ValueError(f"No train/test runs are available at temperature bound {bound:g}.") + cohort = select_common_cohort(records, [run.name for run in eligible_train + eligible_test]) + if len(cohort) < minimum_models: + raise ValueError( + f"Temperature bound {bound:g} has only {len(cohort)} common models; " + f"minimum is {minimum_models}. Complete/backfill the sweep before drawing conclusions." + ) + + train_x, train_y = training_arrays(eligible_train, records, cohort) + gallery = centroid_gallery(eligible_train, records, cohort) + classifiers = fit_classifiers(train_x, train_y, methods) + summaries: list[dict[str, object]] = [] + predictions: list[dict[str, object]] = [] + for run in eligible_test: + query = np.stack([records[run.name][model].vector for model in cohort]) + score_sets: dict[str, np.ndarray] = {"cosine_centroid": cosine_scores(query, gallery)} + score_sets.update( + {method: classifier_scores(classifier, query, cohort) for method, classifier in classifiers.items()} + ) + for method, scores in score_sets.items(): + metrics, rows = ranking_metrics(cohort, scores, cohort, bootstrap_samples) + summaries.append( + { + "temperature_bound": bound, + "method": method, + "run": run.name, + "temperature": run.temperature, + "top_p": run.top_p, + "repeat": run.repeat, + "training_run_count": len(eligible_train), + **metrics, + } + ) + predictions.extend( + { + "temperature_bound": bound, + "method": method, + "run": run.name, + "temperature": run.temperature, + "top_p": run.top_p, + "repeat": run.repeat, + **row, + } + for row in rows + ) + metadata = { + "temperature_bound": bound, + "cohort_size": len(cohort), + "cohort": cohort, + "train_runs": [run.name for run in eligible_train], + "test_runs": [run.name for run in eligible_test], + } + return summaries, predictions, metadata + + +def aggregate_deployment( + summaries: list[dict[str, object]], predictions: list[dict[str, object]], bootstrap_samples: int +) -> list[dict[str, object]]: + output: list[dict[str, object]] = [] + keys = sorted({(float(row["temperature_bound"]), str(row["method"])) for row in summaries}) + for bound, method in keys: + selected = [row for row in summaries if float(row["temperature_bound"]) == bound and row["method"] == method] + # First average repeats within a cell, then weight each temperature/top-p cell equally. + cells: dict[tuple[float, float], list[dict[str, object]]] = {} + for row in selected: + cells.setdefault((float(row["temperature"]), float(row["top_p"])), []).append(row) + cell_accuracies = [float(np.mean([float(row["top1"]) for row in rows])) for rows in cells.values()] + + pred = [row for row in predictions if float(row["temperature_bound"]) == bound and row["method"] == method] + model_names = sorted({str(row["model"]) for row in pred}) + model_values = [] + for model in model_names: + per_model = [float(bool(row["correct_top1"])) for row in pred if row["model"] == model] + model_values.append(float(np.mean(per_model))) + low, high = bootstrap_mean_interval(model_values, bootstrap_samples) + output.append( + { + "temperature_bound": bound, + "method": method, + "cell_count": len(cells), + "model_count": len(model_names), + "mixed_accuracy_equal_cell": float(np.mean(cell_accuracies)), + "mixed_accuracy_ci95_low_model_cluster": low, + "mixed_accuracy_ci95_high_model_cluster": high, + "worst_setting_accuracy": float(np.min(cell_accuracies)), + "best_setting_accuracy": float(np.max(cell_accuracies)), + } + ) + return output + + +def write_csv(path: Path, rows: list[dict[str, object]]) -> None: + if not rows: + path.write_text("", encoding="utf-8") + return + fields: list[str] = [] + for row in rows: + fields.extend(key for key in row if key not in fields) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Exact-model comparison of cosine centroids and multiclass SVMs." + ) + parser.add_argument("--dataset-dir", type=Path, default=Path("out/rand_chinese")) + parser.add_argument("--train-run", action="append", default=[], help="name:suffix:temperature:top_p:repeat") + parser.add_argument("--test-run", action="append", default=[], help="name:suffix:temperature:top_p:repeat") + parser.add_argument("--temperature-bound", type=float, action="append", default=[]) + parser.add_argument("--method", choices=["linear_svm", "rbf_svm"], action="append", default=[]) + parser.add_argument("--minimum-models", type=int, default=100) + parser.add_argument("--bootstrap-samples", type=int, default=2000) + parser.add_argument("--output-dir", type=Path, default=Path("out/exact_dna_classification")) + parser.add_argument("--prefix", default="exact_dna") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + train_runs = [parse_run_spec(value) for value in args.train_run] + test_runs = [parse_run_spec(value) for value in args.test_run] + if not train_runs or not test_runs: + raise SystemExit("Supply at least one --train-run and one independent --test-run.") + try: + validate_split(train_runs, test_runs) + except ValueError as exc: + raise SystemExit(str(exc)) from exc + methods = list(dict.fromkeys(args.method or ["linear_svm", "rbf_svm"])) + bounds = sorted(set(args.temperature_bound or [run.temperature for run in test_runs])) + records = { + run.name: load_records_for_suffix(args.dataset_dir, run.suffix) + for run in train_runs + test_runs + } + empty = [name for name, values in records.items() if not values] + if empty: + raise SystemExit(f"No DNA records found for runs: {empty}") + + summaries: list[dict[str, object]] = [] + predictions: list[dict[str, object]] = [] + cohorts: list[dict[str, object]] = [] + for bound in bounds: + try: + bound_summaries, bound_predictions, metadata = evaluate_bound( + bound, train_runs, test_runs, records, methods, args.minimum_models, args.bootstrap_samples + ) + except ValueError as exc: + raise SystemExit(str(exc)) from exc + summaries.extend(bound_summaries) + predictions.extend(bound_predictions) + cohorts.append(metadata) + deployment = aggregate_deployment(summaries, predictions, args.bootstrap_samples) + + args.output_dir.mkdir(parents=True, exist_ok=True) + paths = { + "per_setting_csv": args.output_dir / f"{args.prefix}_per_setting.csv", + "predictions_csv": args.output_dir / f"{args.prefix}_predictions.csv", + "deployment_csv": args.output_dir / f"{args.prefix}_bounded_deployment.csv", + "summary_json": args.output_dir / f"{args.prefix}_summary.json", + } + write_csv(paths["per_setting_csv"], summaries) + write_csv(paths["predictions_csv"], predictions) + write_csv(paths["deployment_csv"], deployment) + payload = { + "protocol": "temperature-agnostic exact-model classification", + "train_test_repeats_disjoint": True, + "temperature_and_top_p_hidden_from_classifier": True, + "cohorts": cohorts, + "per_setting": summaries, + "bounded_deployment": deployment, + } + paths["summary_json"].write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8") + print(json.dumps({key: str(value) for key, value in paths.items()}, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/export_hf_model_tree.py b/scripts/export_hf_model_tree.py new file mode 100644 index 0000000..bed1f9d --- /dev/null +++ b/scripts/export_hf_model_tree.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +"""Export Hugging Face model-tree relations as connected model groups. + +This is a lightweight, reproducible exporter for the first-pass relationship +file used by the DNA experiments. It extracts direct base-model links from Hub +model cards and collapses them into connected components so downstream tools +can treat all linked models as related. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +from huggingface_hub import HfApi + + +@dataclass(frozen=True) +class ModelRecord: + model_id: str + source_path: Path | None = None + + +class UnionFind: + def __init__(self) -> None: + self.parent: dict[str, str] = {} + self.rank: dict[str, int] = {} + + def add(self, item: str) -> None: + if item not in self.parent: + self.parent[item] = item + self.rank[item] = 0 + + def find(self, item: str) -> str: + parent = self.parent[item] + if parent != item: + self.parent[item] = self.find(parent) + return self.parent[item] + + def union(self, left: str, right: str) -> None: + self.add(left) + self.add(right) + left_root = self.find(left) + right_root = self.find(right) + if left_root == right_root: + return + if self.rank[left_root] < self.rank[right_root]: + left_root, right_root = right_root, left_root + self.parent[right_root] = left_root + if self.rank[left_root] == self.rank[right_root]: + self.rank[left_root] += 1 + + +def safe_model_id(raw: str) -> str: + return raw.strip().strip('"').strip("'") + + +def load_model_records(path: Path) -> list[ModelRecord]: + if not path.exists(): + raise FileNotFoundError(f"Model list not found: {path}") + + records: list[ModelRecord] = [] + text = path.read_text(encoding="utf-8").strip() + if not text: + return records + + if path.suffix.lower() in {".csv", ".tsv"}: + delimiter = "\t" if path.suffix.lower() == ".tsv" else "," + reader = csv.DictReader(text.splitlines(), delimiter=delimiter) + for row in reader: + model_id = row.get("model_id") or row.get("model") or row.get("id") or "" + model_id = safe_model_id(model_id) + if model_id: + records.append(ModelRecord(model_id=model_id, source_path=path)) + return records + + for raw_line in text.splitlines(): + line = raw_line.strip() + if not line or line.startswith("#"): + continue + if line.startswith("{"): + payload = json.loads(line) + model_id = payload.get("model_id") or payload.get("model") or payload.get("id") or "" + model_id = safe_model_id(str(model_id)) + if model_id: + records.append(ModelRecord(model_id=model_id, source_path=path)) + else: + records.append(ModelRecord(model_id=safe_model_id(line), source_path=path)) + return records + + +def split_models(raw: list[str]) -> list[str]: + out: list[str] = [] + for item in raw: + parts = [part.strip() for part in re.split(r"[|,;]", item) if part.strip()] + out.extend(parts) + return out + + +def extract_base_models(info) -> list[str]: + bases: list[str] = [] + card_data = getattr(info, "card_data", None) + if card_data is not None: + base_model = getattr(card_data, "base_model", None) + if isinstance(base_model, str): + bases.extend(split_models([base_model])) + elif isinstance(base_model, (list, tuple, set)): + bases.extend(split_models([str(item) for item in base_model])) + + config = getattr(info, "config", None) + if isinstance(config, dict): + for key in ["base_model_name_or_path", "base_model", "parent_model_name_or_path"]: + value = config.get(key) + if isinstance(value, str): + bases.extend(split_models([value])) + elif isinstance(value, (list, tuple, set)): + bases.extend(split_models([str(item) for item in value])) + + cleaned: list[str] = [] + seen: set[str] = set() + for base in bases: + base = safe_model_id(base) + if base and base not in seen: + cleaned.append(base) + seen.add(base) + return cleaned + + +def build_graph( + records: list[ModelRecord], token: str | None, include_external: bool, timeout: float | None +) -> tuple[list[list[str]], list[dict[str, str]], dict[str, object]]: + api = HfApi(token=token) + uf = UnionFind() + input_models = {record.model_id for record in records} + direct_edges: list[dict[str, str]] = [] + skipped = 0 + + for record in records: + model_id = record.model_id + uf.add(model_id) + try: + info = api.model_info(model_id, timeout=timeout) + except Exception: + skipped += 1 + continue + + for base_model in extract_base_models(info): + uf.union(model_id, base_model) + direct_edges.append({"source": model_id, "target": base_model, "relation": "base_model"}) + + components: dict[str, list[str]] = {} + for node in uf.parent: + root = uf.find(node) + components.setdefault(root, []).append(node) + + groups: list[list[str]] = [] + for nodes in components.values(): + if include_external: + group = sorted(nodes) + else: + group = sorted(node for node in nodes if node in input_models) + if len(group) >= 2: + groups.append(group) + groups.sort(key=lambda nodes: (len(nodes), nodes[0].lower())) + summary = { + "model_count": len(records), + "node_count": len(uf.parent), + "edge_count": len(direct_edges), + "component_count": len(groups), + "skipped_models": skipped, + "include_external": include_external, + } + return groups, direct_edges, summary + + +def build_components(records: list[ModelRecord], token: str | None, include_external: bool, timeout: float | None) -> tuple[list[list[str]], dict[str, object]]: + """Backward-compatible connected-component API.""" + groups, _edges, summary = build_graph(records, token, include_external, timeout) + return groups, summary + + +def write_jsonl(path: Path, groups: list[list[str]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as handle: + for group in groups: + handle.write(json.dumps({"models": group}, ensure_ascii=False) + "\n") + + +def write_edges_jsonl(path: Path, edges: list[dict[str, str]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as handle: + for edge in edges: + handle.write(json.dumps(edge, ensure_ascii=False) + "\n") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Export Hugging Face model-tree relations as JSONL groups.") + parser.add_argument("--models-file", type=Path, required=True, help="Model list file or JSONL with model_id fields.") + parser.add_argument("--output-file", type=Path, default=Path("out/hf_model_tree/model_relations.jsonl")) + parser.add_argument("--edges-file", type=Path, default=Path("out/hf_model_tree/model_direct_edges.jsonl")) + parser.add_argument("--summary-file", type=Path, default=Path("out/hf_model_tree/model_relations_summary.json")) + parser.add_argument("--token", type=str, default=None) + parser.add_argument("--timeout", type=float, default=30.0) + parser.add_argument( + "--include-external", + action="store_true", + help="Keep base models even if they are not present in the input list.", + ) + parser.add_argument("--limit", type=int, default=None, help="Optional maximum number of input models to inspect.") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + records = load_model_records(args.models_file) + if args.limit is not None: + records = records[: args.limit] + groups, edges, summary = build_graph(records, token=args.token, include_external=args.include_external, timeout=args.timeout) + write_jsonl(args.output_file, groups) + write_edges_jsonl(args.edges_file, edges) + args.summary_file.parent.mkdir(parents=True, exist_ok=True) + args.summary_file.write_text(json.dumps(summary, indent=2, ensure_ascii=False), encoding="utf-8") + print(json.dumps({"output_file": str(args.output_file), "edges_file": str(args.edges_file), "summary_file": str(args.summary_file), **summary}, indent=2, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/filter_seen_models_from_lists.py b/scripts/filter_seen_models_from_lists.py new file mode 100644 index 0000000..4707d53 --- /dev/null +++ b/scripts/filter_seen_models_from_lists.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +"""Filter already-seen models (present in configs/rand) from model list JSONL files.""" + +from __future__ import annotations + +import json +import re +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +RAND_DIR = ROOT / "configs" / "rand" +HF_INPUT = ROOT / "configs" / "huggingface_llm_list.jsonl" +OR_INPUT = ROOT / "configs" / "openrouter_llm_list.jsonl" +HF_OUTPUT = ROOT / "configs" / "huggingface_llm_list_left.jsonl" +OR_OUTPUT = ROOT / "configs" / "openrouter_llm_list_left.jsonl" + + +def safe_model_name(model_id: str) -> str: + """Mirror scripts/run_hf_dna_pipeline.py naming for model output folders.""" + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_id.strip("/")) + + +def read_jsonl(path: Path) -> list[dict]: + rows: list[dict] = [] + with path.open("r", encoding="utf-8") as f: + for idx, line in enumerate(f, start=1): + text = line.strip() + if not text: + continue + try: + rows.append(json.loads(text)) + except json.JSONDecodeError: + print(f"[WARN] Skip invalid JSON at {path}:{idx}") + return rows + + +def write_jsonl(path: Path, rows: list[dict]) -> None: + with path.open("w", encoding="utf-8") as f: + for row in rows: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + + +def collect_seen_names(rand_dir: Path) -> set[str]: + if not rand_dir.exists(): + return set() + return {p.name for p in rand_dir.iterdir() if p.is_dir()} + + +def filter_rows(rows: list[dict], seen_safe_names: set[str]) -> tuple[list[dict], list[str]]: + kept: list[dict] = [] + dropped_model_ids: list[str] = [] + for row in rows: + model_id = str(row.get("model_id", "")).strip() + if not model_id: + kept.append(row) + continue + + model_safe_name = safe_model_name(model_id) + if model_safe_name in seen_safe_names: + dropped_model_ids.append(model_id) + else: + kept.append(row) + return kept, dropped_model_ids + + +def main() -> int: + seen_safe_names = collect_seen_names(RAND_DIR) + if not seen_safe_names: + print(f"[WARN] No directories found under: {RAND_DIR}") + + hf_rows = read_jsonl(HF_INPUT) + or_rows = read_jsonl(OR_INPUT) + + hf_kept, hf_dropped = filter_rows(hf_rows, seen_safe_names) + or_kept, or_dropped = filter_rows(or_rows, seen_safe_names) + + write_jsonl(HF_OUTPUT, hf_kept) + write_jsonl(OR_OUTPUT, or_kept) + + print(f"[DONE] seen dirs in rand: {len(seen_safe_names)}") + print(f"[DONE] huggingface: {len(hf_rows)} -> {len(hf_kept)} (dropped {len(hf_dropped)})") + print(f"[DONE] openrouter: {len(or_rows)} -> {len(or_kept)} (dropped {len(or_dropped)})") + print(f"[OUT] {HF_OUTPUT}") + print(f"[OUT] {OR_OUTPUT}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/flag_empty_responses.py b/scripts/flag_empty_responses.py new file mode 100644 index 0000000..f05e1b1 --- /dev/null +++ b/scripts/flag_empty_responses.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +"""Scan response caches and report runs with empty responses.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Flag responses.json files that contain empty responses.") + parser.add_argument("--data-dir", type=Path, default=Path("out/rand_chinese"), help="Directory to scan recursively.") + parser.add_argument("--output-file", type=Path, default=None, help="Optional JSON report path.") + parser.add_argument("--max-show", type=int, default=5, help="Maximum empty indices to display per run.") + parser.add_argument("--show-all", action="store_true", help="Print every run, including clean ones.") + return parser.parse_args() + + +def inspect_response_file(path: Path, max_show: int) -> dict[str, object]: + payload = json.loads(path.read_text(encoding="utf-8")) + items = payload.get("items", []) + empty_indices: list[int] = [] + for idx, item in enumerate(items): + if not isinstance(item, dict): + empty_indices.append(idx) + continue + response = str(item.get("response", "")).strip() + if not response: + empty_indices.append(idx) + + return { + "path": str(path), + "run_name": path.parent.name, + "item_count": len(items), + "empty_count": len(empty_indices), + "empty_indices": empty_indices[:max_show], + "truncated": len(empty_indices) > max_show, + } + + +def main() -> int: + args = parse_args() + records = [] + + for response_path in sorted(args.data_dir.rglob("responses.json")): + try: + record = inspect_response_file(response_path, args.max_show) + except Exception as exc: + record = { + "path": str(response_path), + "run_name": response_path.parent.name, + "error": str(exc), + } + records.append(record) + continue + + if record["empty_count"] > 0 or args.show_all: + records.append(record) + + for record in records: + if "error" in record: + print(f"ERROR {record['path']}: {record['error']}") + continue + indices = ", ".join(str(idx) for idx in record["empty_indices"]) + suffix = " ..." if record["truncated"] else "" + print( + f"{record['empty_count']:4d} empty / {record['item_count']:4d} items " + f"{record['run_name']}: [{indices}{suffix}]" + ) + + if args.output_file is not None: + args.output_file.parent.mkdir(parents=True, exist_ok=True) + args.output_file.write_text(json.dumps(records, indent=2, ensure_ascii=False), encoding="utf-8") + + print(f"\nmatched {len(records)} problematic run(s)" if records else "\nno empty responses found") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/prune_model_list_by_status.py b/scripts/prune_model_list_by_status.py new file mode 100644 index 0000000..31085a9 --- /dev/null +++ b/scripts/prune_model_list_by_status.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""Remove model entries from a JSONL list using latest statuses from a status journal.""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime +from pathlib import Path + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Prune model list JSONL by status.jsonl latest records.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("--status-jsonl", type=Path, required=True, help="Path to status.jsonl") + parser.add_argument("--input-jsonl", type=Path, required=True, help="Model list JSONL to prune") + parser.add_argument("--provider", type=str, default=None, help="Only use statuses from this provider") + parser.add_argument( + "--status", + type=str, + default="success", + help="Status value to remove from input JSONL (e.g., success, failed_oom)", + ) + parser.add_argument("--backup", action="store_true", help="Create timestamped backup before writing") + parser.add_argument("--dry-run", action="store_true", help="Report changes without writing file") + return parser.parse_args() + + +def load_latest_status_map(status_path: Path, provider: str | None) -> dict[str, str]: + latest: dict[str, dict] = {} + with status_path.open("r", encoding="utf-8") as f: + for line in f: + text = line.strip() + if not text: + continue + try: + row = json.loads(text) + except json.JSONDecodeError: + continue + model_id = str(row.get("model_id", "")).strip() + row_provider = str(row.get("provider", "")).strip() + if not model_id: + continue + if provider and row_provider != provider: + continue + latest[model_id] = row + + status_map: dict[str, str] = {} + for model_id, row in latest.items(): + status_map[model_id] = str(row.get("status", "")).strip() + return status_map + + +def load_jsonl_rows(path: Path) -> list[dict]: + rows: list[dict] = [] + with path.open("r", encoding="utf-8") as f: + for idx, line in enumerate(f, start=1): + text = line.strip() + if not text: + continue + try: + rows.append(json.loads(text)) + except json.JSONDecodeError as exc: + raise ValueError(f"Invalid JSON at {path}:{idx}: {exc}") from exc + return rows + + +def write_jsonl_rows(path: Path, rows: list[dict]) -> None: + with path.open("w", encoding="utf-8") as f: + for row in rows: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + + +def main() -> int: + args = parse_args() + + if not args.status_jsonl.exists(): + raise FileNotFoundError(f"status file not found: {args.status_jsonl}") + if not args.input_jsonl.exists(): + raise FileNotFoundError(f"input file not found: {args.input_jsonl}") + + status_map = load_latest_status_map(args.status_jsonl, args.provider) + rows = load_jsonl_rows(args.input_jsonl) + + kept: list[dict] = [] + removed: list[str] = [] + target_status = args.status.strip() + + for row in rows: + model_id = str(row.get("model_id", "")).strip() + if not model_id: + kept.append(row) + continue + if status_map.get(model_id) == target_status: + removed.append(model_id) + continue + kept.append(row) + + print(f"[INFO] status file: {args.status_jsonl}") + print(f"[INFO] input file: {args.input_jsonl}") + print(f"[INFO] target status: {target_status}") + print(f"[INFO] provider filter: {args.provider or 'ALL'}") + print(f"[INFO] rows: {len(rows)} -> {len(kept)} (removed {len(removed)})") + + if removed: + print("[INFO] removed model_id entries:") + for model_id in removed: + print(f" - {model_id}") + + if args.dry_run: + print("[DONE] dry-run only, file unchanged") + return 0 + + if args.backup: + stamp = datetime.now().strftime("%Y%m%d-%H%M%S") + backup = args.input_jsonl.with_name(f"{args.input_jsonl.name}.bak.{stamp}.status-{target_status}") + backup.write_text(args.input_jsonl.read_text(encoding="utf-8"), encoding="utf-8") + print(f"[INFO] backup: {backup}") + + write_jsonl_rows(args.input_jsonl, kept) + print("[DONE] file updated") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/prune_rand_chinese_from_saved.py b/scripts/prune_rand_chinese_from_saved.py new file mode 100644 index 0000000..6481771 --- /dev/null +++ b/scripts/prune_rand_chinese_from_saved.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +"""Remove already-saved rand_chinese model outputs that also exist in out-saved/rand.""" + +from __future__ import annotations + +import argparse +import shutil +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_SOURCE_DIR = ROOT / "out-saved" / "rand" +DEFAULT_TARGET_DIR = ROOT / "out" / "rand_chinese" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Remove target model output directories when the same model exists in out-saved/rand.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("--source-dir", type=Path, default=DEFAULT_SOURCE_DIR, help="Reference output tree") + parser.add_argument("--target-dir", type=Path, default=DEFAULT_TARGET_DIR, help="Directory to prune") + mode = parser.add_mutually_exclusive_group() + mode.add_argument("--dry-run", action="store_true", help="Preview deletions without changing anything") + mode.add_argument("--apply", action="store_true", help="Actually delete the matching directories") + return parser.parse_args() + + +def collect_model_dirs(base_dir: Path) -> set[str]: + if not base_dir.exists(): + return set() + return {entry.name for entry in base_dir.iterdir() if entry.is_dir()} + + +def main() -> int: + args = parse_args() + source_dir = args.source_dir + target_dir = args.target_dir + + if not source_dir.exists(): + raise FileNotFoundError(f"source directory not found: {source_dir}") + if not target_dir.exists(): + print(f"[WARN] target directory not found, nothing to do: {target_dir}") + return 0 + + source_models = collect_model_dirs(source_dir) + target_models = collect_model_dirs(target_dir) + matched_models = sorted(source_models & target_models) + missing_in_target = sorted(source_models - target_models) + + print(f"[INFO] source: {source_dir}") + print(f"[INFO] target: {target_dir}") + print(f"[INFO] source model dirs: {len(source_models)}") + print(f"[INFO] target model dirs: {len(target_models)}") + print(f"[INFO] matched model dirs: {len(matched_models)}") + + if matched_models: + action = "would remove" if args.dry_run or not args.apply else "removing" + print(f"[INFO] {action} matched target directories:") + for model_name in matched_models: + print(f" - {target_dir / model_name}") + + if missing_in_target: + print(f"[INFO] source models not present under target: {len(missing_in_target)}") + + if args.dry_run or not args.apply: + print("[DONE] dry-run only, no files changed") + return 0 + + removed = 0 + for model_name in matched_models: + path = target_dir / model_name + shutil.rmtree(path) + removed += 1 + + print(f"[DONE] removed {removed} directory(s)") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/scripts/report_sweep_progress.py b/scripts/report_sweep_progress.py new file mode 100644 index 0000000..8d82a91 --- /dev/null +++ b/scripts/report_sweep_progress.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""Report completion progress for a DNA sweep run. + +The script reads a run's ``manifest.json`` and ``status.jsonl`` and reports: + +- planned task count +- number of unique models with a final recorded status +- completion percentage +- any models still missing from the status journal + +It accepts either an explicit run directory or a temperature/top-p/repeat +specification and maps it to the expected run directory name. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter +from pathlib import Path + + +DEFAULT_RUN_ROOT = Path("out") / "rand_chinese_temp_top_p_sweep" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Report completion progress for a DNA sweep run.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + target = parser.add_mutually_exclusive_group() + target.add_argument("--run-dir", type=Path, help="Exact run directory containing manifest.json and status.jsonl") + target.add_argument("--suffix", type=str, help="Run suffix such as _t00_p08_r1") + parser.add_argument("--temperature", type=float, help="Temperature value used to build the suffix") + parser.add_argument("--top-p", dest="top_p", type=float, help="Top-p value used to build the suffix") + parser.add_argument("--repeat", type=int, default=1, help="Repeat index used to build the suffix") + parser.add_argument( + "--run-root", + type=Path, + default=DEFAULT_RUN_ROOT, + help="Root directory that contains per-setting run directories", + ) + args = parser.parse_args() + if args.run_dir is None and args.suffix is None and (args.temperature is None or args.top_p is None): + parser.error("provide --run-dir, --suffix, or both --temperature and --top-p") + return args + + +def format_code(value: float) -> str: + return f"{int(round(value * 10)):02d}" + + +def build_run_dir(args: argparse.Namespace) -> Path: + if args.run_dir is not None: + return args.run_dir + if args.suffix is not None: + suffix = args.suffix.strip() + if not suffix.startswith("_"): + suffix = f"_{suffix}" + return args.run_root.with_name(f"{args.run_root.name}{suffix}") + if args.temperature is None or args.top_p is None: + raise ValueError("Provide either --run-dir, --suffix, or both --temperature and --top-p.") + suffix = f"_t{format_code(args.temperature)}_p{format_code(args.top_p)}_r{args.repeat}" + return args.run_root.with_name(f"{args.run_root.name}{suffix}") + + +def load_latest_statuses(status_path: Path) -> dict[str, dict]: + latest: dict[str, dict] = {} + with status_path.open("r", encoding="utf-8") as f: + for line in f: + text = line.strip() + if not text: + continue + row = json.loads(text) + model_id = str(row.get("model_id", "")).strip() + if model_id: + latest[model_id] = row + return latest + + +def main() -> int: + args = parse_args() + run_dir = build_run_dir(args) + manifest_path = run_dir / "manifest.json" + status_path = run_dir / "status.jsonl" + + if not manifest_path.exists(): + raise FileNotFoundError(f"manifest not found: {manifest_path}") + if not status_path.exists(): + raise FileNotFoundError(f"status journal not found: {status_path}") + + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + latest = load_latest_statuses(status_path) + planned = int(manifest.get("task_count", 0)) + task_models = [str(task.get("model_id", "")).strip() for task in manifest.get("tasks", []) if str(task.get("model_id", "")).strip()] + + finished = sum( + 1 + for row in latest.values() + if str(row.get("status", "")).startswith("failed") or str(row.get("status", "")) == "success" + ) + success = sum(1 for row in latest.values() if str(row.get("status", "")) == "success") + failed = finished - success + failure_reasons = Counter( + str(row.get("status", "unknown")) + for row in latest.values() + if str(row.get("status", "")).startswith("failed") + ) + missing = [model_id for model_id in task_models if model_id not in latest] + percent = (finished / planned * 100.0) if planned else 0.0 + + print(f"run_dir={run_dir}") + print(f"planned={planned}") + print(f"unique_models_recorded={len(latest)}") + print(f"finished={finished}") + print(f"success={success}") + print(f"failed={failed}") + print(f"missing={len(missing)}") + print(f"percent={percent:.1f}%") + print("failure_status_counts=" + json.dumps(dict(sorted(failure_reasons.items())), ensure_ascii=False)) + if missing: + print("missing_models=" + json.dumps(missing, ensure_ascii=False)) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_dna_temperature_top_p_missing_only.sh b/scripts/run_dna_temperature_top_p_missing_only.sh new file mode 100755 index 0000000..64cd890 --- /dev/null +++ b/scripts/run_dna_temperature_top_p_missing_only.sh @@ -0,0 +1,44 @@ +#!/usr/bin/env bash +# Run only the missing temperature/top_p pairs for the current rand_chinese sweep. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +MASTER_LOG_DIR="${MASTER_LOG_DIR:-logs/dna_sweep/rand_chinese_temp_top_p_sweep_missing_$(date +%Y%m%d-%H%M%S)}" +REPEATS="${REPEATS:-1 2}" +SWEEP_NAME="${SWEEP_NAME:-rand_chinese_temp_top_p_sweep}" + +mkdir -p "$MASTER_LOG_DIR" + +run_pair() { + local temperature="$1" + local top_p="$2" + local t_code + local p_code + t_code="$(printf '%.1f' "$temperature" | awk '{printf "%02d", int($1 * 10 + 0.5)}')" + p_code="$(printf '%.1f' "$top_p" | awk '{printf "%02d", int($1 * 10 + 0.5)}')" + local pair_name="${SWEEP_NAME}_t${t_code}_p${p_code}" + local pair_log_dir="$MASTER_LOG_DIR/$pair_name" + + mkdir -p "$pair_log_dir" + echo "[$(date '+%Y-%m-%d %H:%M:%S')] START pair=$temperature:$top_p log_dir=$pair_log_dir" + + env \ + TEMPERATURES="$temperature" \ + TOP_PS="$top_p" \ + REPEATS="$REPEATS" \ + LOG_DIR="$pair_log_dir" \ + SWEEP_NAME="$SWEEP_NAME" \ + bash scripts/run_dna_temperature_top_p_sweep.sh + + echo "[$(date '+%Y-%m-%d %H:%M:%S')] DONE pair=$temperature:$top_p" +} + +run_pair 0.5 0.9 +run_pair 0.5 1.0 +run_pair 0.7 0.8 +run_pair 0.7 1.0 + +echo "[$(date '+%Y-%m-%d %H:%M:%S')] all missing pairs complete" \ No newline at end of file diff --git a/scripts/run_dna_temperature_top_p_sweep.sh b/scripts/run_dna_temperature_top_p_sweep.sh new file mode 100755 index 0000000..8dc7f4d --- /dev/null +++ b/scripts/run_dna_temperature_top_p_sweep.sh @@ -0,0 +1,215 @@ +#!/usr/bin/env bash +# Sequentially run LLM-DNA temperature/top_p sweeps. +# +# Launch once with nohup, for example: +# nohup bash scripts/run_dna_temperature_top_p_sweep.sh > logs/dna_sweep/master_$(date +%Y%m%d-%H%M%S).log 2>&1 & +# +# The script runs one setting at a time. Each setting gets a clear output suffix, +# run name, child log, and manifest row. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" +if [[ ! -x "$PYTHON_BIN" ]]; then + PYTHON_BIN="$(command -v python3)" +fi + +HF_JSONL="${HF_JSONL:-configs/rand_chinese_0p3b_7b_rerun.jsonl}" +DATASET="${DATASET:-rand_chinese}" +OUTPUT_DIR="${OUTPUT_DIR:-out}" +PROVIDERS="${PROVIDERS:-huggingface}" +GLOBAL_STATUS_JOURNAL="${GLOBAL_STATUS_JOURNAL:-$OUTPUT_DIR/dna_global_status.jsonl}" +GLOBAL_STATE="${GLOBAL_STATE:-$OUTPUT_DIR/dna_global_state.json}" + +# Default sweep. Override with env vars, e.g.: +# TEMPERATURES="0.2 0.3" TOP_PS="0.9" REPEATS="1 2" +TEMPERATURES="${TEMPERATURES:-0.0 0.1 0.2 0.3 0.5 0.7 0.9}" +TOP_PS="${TOP_PS:-0.8 0.85 0.9 0.95 1.0}" +REPEATS="${REPEATS:-1 2}" +GENERATION_SEED_BASE="${GENERATION_SEED_BASE:-42000}" + +MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-10}" +MIN_GPU_FREE_GB="${MIN_GPU_FREE_GB:-4.0}" +GPU_POLL_SECONDS="${GPU_POLL_SECONDS:-15.0}" +GPU_MEMORY_PER_BILLION_GB="${GPU_MEMORY_PER_BILLION_GB:-2.2}" +GPU_MEMORY_HEADROOM_GB="${GPU_MEMORY_HEADROOM_GB:-2.0}" +MODEL_TIMEOUT_SECONDS="${MODEL_TIMEOUT_SECONDS:-43200}" +MAX_RETRIES="${MAX_RETRIES:-3}" +OOM_RETRIES="${OOM_RETRIES:-3}" +RETRY_DELAY_SECONDS="${RETRY_DELAY_SECONDS:-300}" +GPUS="${GPUS:-}" +TRY_VLLM="${TRY_VLLM:-1}" +RESUME_MODE="${RESUME_MODE:-all}" +CACHE_EVICT_FINISHED="${CACHE_EVICT_FINISHED:-success}" + +# Set LIMIT=50 for a cheaper pilot. LIMIT=0 means no limit. +LIMIT="${LIMIT:-0}" + +# Use 1 to include deterministic baselines named _t00_p10_r1/_t00_p10_r2. +# Existing _2_run/_3_run already cover deterministic baselines, so default is off. +INCLUDE_T00="${INCLUDE_T00:-0}" + +SWEEP_NAME="${SWEEP_NAME:-rand_chinese_temp_top_p_sweep}" +LOG_DIR="${LOG_DIR:-logs/dna_sweep/${SWEEP_NAME}_$(date +%Y%m%d-%H%M%S)}" +MANIFEST="${MANIFEST:-$LOG_DIR/sweep_manifest.tsv}" + +mkdir -p "$LOG_DIR" + +format_decimal() { + local value="$1" + "$PYTHON_BIN" - "$value" <<'PY' +import sys +value = float(sys.argv[1]) +print(f"{int(round(value * 10)):02d}") +PY +} + +write_manifest_header() { + printf "timestamp\tsuffix\trun_name\ttemperature\ttop_p\trepeat\tstatus\tlog_path\n" > "$MANIFEST" +} + +append_manifest() { + local suffix="$1" + local run_name="$2" + local temperature="$3" + local top_p="$4" + local repeat="$5" + local status="$6" + local log_path="$7" + printf "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" \ + "$(date '+%Y-%m-%d %H:%M:%S')" "$suffix" "$run_name" "$temperature" "$top_p" "$repeat" "$status" "$log_path" >> "$MANIFEST" +} + +run_one() { + local temperature="$1" + local top_p="$2" + local repeat="$3" + local t_code + local p_code + t_code="$(format_decimal "$temperature")" + p_code="$(format_decimal "$top_p")" + + local suffix="_t${t_code}_p${p_code}_r${repeat}" + local generation_seed=$((GENERATION_SEED_BASE + repeat)) + local run_name="${SWEEP_NAME}${suffix}" + local log_path="$LOG_DIR/${run_name}.log" + + echo "[$(date '+%Y-%m-%d %H:%M:%S')] START suffix=$suffix temperature=$temperature top_p=$top_p repeat=$repeat" + append_manifest "$suffix" "$run_name" "$temperature" "$top_p" "$repeat" "started" "$log_path" + + local command=( + "$PYTHON_BIN" scripts/run_hf_dna_pipeline.py + --providers "$PROVIDERS" + --hf-jsonl "$HF_JSONL" + --dataset "$DATASET" + --output-dir "$OUTPUT_DIR" + --global-status-journal "$GLOBAL_STATUS_JOURNAL" + --global-state "$GLOBAL_STATE" + --output-suffix "$suffix" + --ignore-response-cache + --resume-mode "$RESUME_MODE" + --max-concurrent-gpus "$MAX_CONCURRENT_GPUS" + --min-gpu-free-gb "$MIN_GPU_FREE_GB" + --gpu-poll-seconds "$GPU_POLL_SECONDS" + --gpu-memory-per-billion-gb "$GPU_MEMORY_PER_BILLION_GB" + --gpu-memory-headroom-gb "$GPU_MEMORY_HEADROOM_GB" + --model-timeout-seconds "$MODEL_TIMEOUT_SECONDS" + --max-retries "$MAX_RETRIES" + --oom-retries "$OOM_RETRIES" + --retry-delay-seconds "$RETRY_DELAY_SECONDS" + --cache-evict-finished "$CACHE_EVICT_FINISHED" + --temperature "$temperature" + --top-p "$top_p" + --generation-seed "$generation_seed" + --run-name "$run_name" + ) + + # vLLM requires top_p > 0. Transformers supports top_p=0 by retaining only + # the highest-probability token, so use the Transformers backend for p=0. + if [[ "$TRY_VLLM" == "1" ]] && "$PYTHON_BIN" - "$top_p" <<'PY' +import sys +raise SystemExit(0 if float(sys.argv[1]) > 0.0 else 1) +PY + then + command+=(--try-vllm) + fi + if [[ -n "$GPUS" ]]; then + command+=(--gpus "$GPUS") + fi + if [[ "$LIMIT" != "0" ]]; then + command+=(--limit "$LIMIT") + fi + + # temperature=0 should stay deterministic; otherwise enable sampling. + if "$PYTHON_BIN" - "$temperature" <<'PY' +import sys +raise SystemExit(0 if float(sys.argv[1]) > 0.0 else 1) +PY + then + command+=(--do-sample) + else + command+=(--no-do-sample) + fi + + { + echo "cwd=$ROOT" + echo "python=$PYTHON_BIN" + echo "suffix=$suffix" + echo "run_name=$run_name" + echo "temperature=$temperature" + echo "top_p=$top_p" + echo "repeat=$repeat" + echo "generation_seed=$generation_seed" + printf "command:" + printf " %q" "${command[@]}" + printf "\n\n" + } > "$log_path" + + local status="success" + if "${command[@]}" >> "$log_path" 2>&1; then + status="success" + echo "[$(date '+%Y-%m-%d %H:%M:%S')] DONE suffix=$suffix" + else + status="failed" + echo "[$(date '+%Y-%m-%d %H:%M:%S')] FAIL suffix=$suffix log=$log_path" + fi + append_manifest "$suffix" "$run_name" "$temperature" "$top_p" "$repeat" "$status" "$log_path" +} + +write_manifest_header + +echo "Sweep manifest: $MANIFEST" +echo "Logs: $LOG_DIR" +echo "HF_JSONL=$HF_JSONL" +echo "TEMPERATURES=$TEMPERATURES" +echo "TOP_PS=$TOP_PS" +echo "REPEATS=$REPEATS" +echo "LIMIT=$LIMIT" + +if [[ "$INCLUDE_T00" == "1" ]]; then + for repeat in $REPEATS; do + run_one "0.0" "1.0" "$repeat" + done +fi + +for temperature in $TEMPERATURES; do + # T=0 is a single deterministic condition: top-p is inactive when sampling + # is disabled and must not be counted as multiple factorial cells. + if "$PYTHON_BIN" - "$temperature" <<'PY' +import sys +raise SystemExit(0 if float(sys.argv[1]) == 0.0 else 1) +PY + then + continue + fi + for top_p in $TOP_PS; do + for repeat in $REPEATS; do + run_one "$temperature" "$top_p" "$repeat" + done + done +done + +echo "[$(date '+%Y-%m-%d %H:%M:%S')] sweep complete" diff --git a/scripts/run_hf_dna_pipeline.py b/scripts/run_hf_dna_pipeline.py new file mode 100644 index 0000000..6a68806 --- /dev/null +++ b/scripts/run_hf_dna_pipeline.py @@ -0,0 +1,1707 @@ +#!/usr/bin/env python3 +""" +Unified DNA batch runner for HuggingFace and OpenRouter model lists. + +Designed for long-running `nohup` execution: +- runs one model per subprocess for isolation +- uses at most 4 GPUs concurrently +- routes HuggingFace and OpenRouter lists through the same scheduler +- chooses `/shared/hdd` or `/shared/ssd` for model cache based on free space +- cleans HuggingFace cache when free space falls below a threshold +""" + +from __future__ import annotations + +import argparse +import json +import logging +import os +import queue +import re +import signal +import shutil +import subprocess +import threading +import time +from collections import deque +from datetime import datetime +from pathlib import Path +from typing import Any, Iterable, NamedTuple, Optional, cast + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_HF_JSONL = ROOT / "configs" / "huggingface_llm_list.jsonl" +DEFAULT_OPENROUTER_JSONL = ROOT / "configs" / "openrouter_llm_list.jsonl" +DEFAULT_OUTPUT_DIR = ROOT / "out" +DEFAULT_DATA_ROOT = ROOT / "data" +DEFAULT_DATASET = "rand_chinese" +DEFAULT_MAX_SAMPLES = 100 +MAX_CONCURRENT_GPUS = 64 +CACHE_CANDIDATES = [Path("/shared/hdd"), Path("/shared/ssd")] +DEFAULT_GLOBAL_STATUS_JOURNAL = DEFAULT_OUTPUT_DIR / "dna_global_status.jsonl" +DEFAULT_GLOBAL_STATE = DEFAULT_OUTPUT_DIR / "dna_global_state.json" +PYTHON_BIN = ( + str(ROOT / "venv" / "bin" / "python") + if (ROOT / "venv" / "bin" / "python").exists() + else shutil.which("python3") or "python3" +) + + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s | %(levelname)-8s | %(message)s", +) +logger = logging.getLogger(__name__) + + +class ModelTask(NamedTuple): + model_id: str + provider: str + inferred_size_b: Optional[float] + + +class GPUStat(NamedTuple): + gpu_id: int + util: float + mem_used: float + mem_total: float + + @property + def mem_free(self) -> float: + return max(self.mem_total - self.mem_used, 0.0) + + @property + def mem_free_gb(self) -> float: + return self.mem_free / 1024.0 + + @property + def mem_used_pct(self) -> float: + return (self.mem_used / self.mem_total * 100.0) if self.mem_total else 0.0 + + +class CacheRuntimeState: + def __init__( + self, + pending_hf_models: set[str], + running_hf_models: set[str], + state_lock: threading.Lock, + cleanup_lock: threading.Lock, + ) -> None: + self.pending_hf_models = pending_hf_models + self.running_hf_models = running_hf_models + self.state_lock = state_lock + self.cleanup_lock = cleanup_lock + + +class TaskWork: + def __init__(self, task: ModelTask) -> None: + self.task = task + self.attempts = 0 + self.attempted_gpu_ids: list[int] = [] + self.ready_at = 0.0 + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run LLM-DNA extraction for HuggingFace and OpenRouter model lists.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("--hf-jsonl", type=Path, default=DEFAULT_HF_JSONL) + parser.add_argument("--openrouter-jsonl", type=Path, default=DEFAULT_OPENROUTER_JSONL) + parser.add_argument( + "--providers", + choices=["all", "huggingface", "openrouter"], + default="all", + help="Which provider lists to run.", + ) + parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR) + parser.add_argument( + "--output-suffix", + type=str, + default="", + help="Suffix appended to each model output directory, e.g. _2 for reruns.", + ) + parser.add_argument( + "--ignore-response-cache", + action="store_true", + help="Regenerate responses instead of reading existing responses.json; new responses are still saved.", + ) + parser.add_argument("--data-root", type=Path, default=DEFAULT_DATA_ROOT) + parser.add_argument("--dataset", type=str, default=DEFAULT_DATASET) + parser.add_argument("--max-samples", type=int, default=DEFAULT_MAX_SAMPLES) + parser.add_argument("--dna-dim", type=int, default=128) + parser.add_argument( + "--reduction-method", + choices=["pca", "svd", "random_projection"], + default="random_projection", + ) + parser.add_argument( + "--embedding-merge", + choices=["sum", "max", "mean", "concat"], + default="concat", + ) + parser.add_argument("--max-length", type=int, default=1024) + parser.add_argument( + "--temperature", + type=float, + default=0.0, + help="Generation temperature. Defaults to 0.0 to preserve deterministic historical runs.", + ) + parser.add_argument( + "--top-p", + type=float, + default=1.0, + help="Top-p nucleus sampling value used when sampling is enabled.", + ) + parser.add_argument( + "--generation-seed", + type=int, + default=42, + help="Response-sampling seed, recorded separately from the fixed projection seed.", + ) + parser.add_argument( + "--do-sample", + action="store_true", + default=False, + help="Enable stochastic sampling during generation.", + ) + parser.add_argument( + "--no-do-sample", + dest="do_sample", + action="store_false", + help="Disable stochastic sampling during generation.", + ) + parser.add_argument( + "--gpus", + type=str, + default=None, + help="Comma-separated GPU IDs to pin scheduling. If omitted, dynamically pick from all visible GPUs.", + ) + parser.add_argument("--max-concurrent-gpus", type=int, default=MAX_CONCURRENT_GPUS) + parser.add_argument( + "--min-gpu-free-gb", + type=float, + default=4.0, + help="Only dispatch a new task onto GPUs with at least this much currently free memory.", + ) + parser.add_argument( + "--gpu-poll-seconds", + type=float, + default=15.0, + help="How long to wait before re-checking GPU availability when all selected GPUs are busy.", + ) + parser.add_argument( + "--gpu-memory-per-billion-gb", + type=float, + default=2.2, + help="Estimated GPU GiB required per billion parameters when deciding whether to dispatch.", + ) + parser.add_argument( + "--gpu-memory-headroom-gb", + type=float, + default=2.0, + help="Additional free GPU memory required beyond the parameter-size estimate.", + ) + parser.add_argument( + "--oom-retries", + type=int, + default=3, + help="How many times to retry failed_oom runs (preferably on a different GPU).", + ) + parser.add_argument( + "--max-retries", + type=int, + default=3, + help="Maximum deferred retries for transient non-OOM failures.", + ) + parser.add_argument( + "--retry-delay-seconds", + type=float, + default=300.0, + help="Cooldown before a waiting model is tried again after other queued models.", + ) + parser.add_argument( + "--model-timeout-seconds", + type=float, + default=43200.0, + help="Per-attempt child-process timeout (12 hours by default; 0 disables it).", + ) + parser.add_argument("--limit", type=int, default=0, help="Limit queued models after filtering (0 = no limit).") + parser.add_argument( + "--cache-low-free-gb", + type=float, + default=110.0, + help="Start cleaning HF cache when free space falls below this low-watermark threshold.", + ) + parser.add_argument( + "--cache-target-free-gb", + type=float, + default=140.0, + help=( + "Stop cleaning after free space reaches this target watermark. " + "Set above --cache-low-free-gb to enable hysteresis and reduce repeated cleanups." + ), + ) + parser.add_argument( + "--cache-subdir", + type=str, + default="llm-dna-cache", + help="Subdirectory created under /shared/hdd or /shared/ssd for caches.", + ) + parser.add_argument( + "--cache-evict-finished", + choices=["never", "success", "all"], + default="success", + help=( + "Delete the just-finished HuggingFace model cache directory only after verified success " + "(non-empty responses + DNA file + summary file): " + "never=disabled, success=verified-success only, all=verified-success or failed_* runs." + ), + ) + parser.add_argument("--metadata-file", type=Path, default=ROOT / "configs" / "llm_metadata.json") + parser.add_argument("--run-name", type=str, default=None) + parser.add_argument( + "--resume-status", + type=Path, + default=None, + help="Optional existing status.jsonl to resume from.", + ) + parser.add_argument( + "--resume-latest", + action="store_true", + help="Resume from the latest run status.jsonl under the output directory when --resume-status is not provided.", + ) + parser.add_argument( + "--resume-mode", + choices=["new", "continue", "retry-failed", "retry-non-success", "all"], + default="new", + help=( + "`new`: run models not yet globally successful; " + "`continue`: continue a previous run from its unfinished tail; " + "`retry-failed`: rerun failed models; " + "`retry-non-success`: rerun failed and skipped models; " + "`all`: ignore prior state." + ), + ) + parser.add_argument( + "--retry-failures", + action="store_true", + help="Deprecated alias for --resume-mode retry-failed.", + ) + parser.add_argument("--global-status-journal", type=Path, default=DEFAULT_GLOBAL_STATUS_JOURNAL) + parser.add_argument("--global-state", type=Path, default=DEFAULT_GLOBAL_STATE) + parser.add_argument( + "--try-vllm", + action="store_true", + help="Try vLLM first for decoder-only HuggingFace models.", + ) + parser.add_argument( + "--stream-subprocess-logs", + action="store_true", + help="Stream child model subprocess stdout/stderr into the main pipeline log.", + ) + parser.add_argument( + "--no-stream-subprocess-logs", + dest="stream_subprocess_logs", + action="store_false", + help="Disable live streaming of child model subprocess logs.", + ) + parser.add_argument( + "--child-log-mode", + choices=["cache-only", "verbose"], + default="cache-only", + help="How much child subprocess output to stream.", + ) + parser.add_argument( + "--openrouter-use-gpu", + action="store_true", + help="Allow OpenRouter tasks to use GPU scheduling (otherwise they run on CPU).", + ) + parser.set_defaults(stream_subprocess_logs=True) + return parser.parse_args() + + +def infer_param_billion(model_id: str) -> Optional[float]: + matches = re.findall(r"(\d+(?:\.\d+)?)\s*[bB](?![a-zA-Z])", model_id) + if not matches: + return None + return max(float(value) for value in matches) + + +def load_tasks(jsonl_path: Path, provider: str) -> list[ModelTask]: + tasks: list[ModelTask] = [] + with jsonl_path.open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + record = json.loads(line) + model_id = str(record["model_id"]).strip() + if not model_id: + continue + tasks.append( + ModelTask( + model_id=model_id, + provider=provider, + inferred_size_b=infer_param_billion(model_id), + ) + ) + return tasks + + +def load_all_tasks(args: argparse.Namespace) -> list[ModelTask]: + tasks: list[ModelTask] = [] + if args.providers in {"all", "huggingface"}: + tasks.extend(load_tasks(args.hf_jsonl, "huggingface")) + if args.providers in {"all", "openrouter"}: + tasks.extend(load_tasks(args.openrouter_jsonl, "openrouter")) + + deduped: list[ModelTask] = [] + seen: set[tuple[str, str]] = set() + for task in tasks: + key = (task.provider, task.model_id) + if key in seen: + continue + seen.add(key) + deduped.append(task) + return deduped + + +def parse_gpu_ids(raw: Optional[str], max_concurrent: int) -> list[int]: + if raw: + parsed = [int(part.strip()) for part in raw.split(",") if part.strip()] + return parsed[:max_concurrent] + return [] + + +def query_gpu_stats() -> list[GPUStat]: + try: + result = subprocess.run( + [ + "nvidia-smi", + "--query-gpu=index,utilization.gpu,memory.used,memory.total", + "--format=csv,noheader,nounits", + ], + capture_output=True, + text=True, + timeout=10, + check=False, + ) + except Exception as exc: + logger.warning("Failed to query GPU state: %s", exc) + return [] + + if result.returncode != 0: + logger.warning("nvidia-smi returned non-zero exit code: %s", result.returncode) + return [] + + stats: list[GPUStat] = [] + for line in result.stdout.splitlines(): + parts = [part.strip() for part in line.split(",")] + if len(parts) != 4: + continue + stats.append( + GPUStat( + gpu_id=int(parts[0]), + util=float(parts[1]), + mem_used=float(parts[2]), + mem_total=float(parts[3]), + ) + ) + return stats + + +def _score_gpu(stat: GPUStat) -> tuple[float, float, float, int]: + return (stat.mem_used_pct, stat.util, -stat.mem_free, stat.gpu_id) + + +def choose_best_gpu( + stats: Iterable[GPUStat], + allowed_gpu_ids: Iterable[int], + busy_gpu_ids: Iterable[int], + min_free_gb: float = 0.0, +) -> Optional[int]: + allowed = set(allowed_gpu_ids) + busy = set(busy_gpu_ids) + candidates = [ + stat + for stat in stats + if stat.gpu_id in allowed and stat.gpu_id not in busy and stat.mem_free_gb >= min_free_gb + ] + if not candidates: + return None + candidates.sort(key=_score_gpu) + return candidates[0].gpu_id + + +def select_best_gpus(limit: int) -> list[int]: + stats = query_gpu_stats() + if not stats: + return [] + stats.sort(key=_score_gpu) + return [stat.gpu_id for stat in stats[:limit]] + + +def required_gpu_free_gb(task: ModelTask, args: argparse.Namespace) -> float: + configured_floor = max(float(args.min_gpu_free_gb), 0.0) + if task.inferred_size_b is None: + return configured_floor + estimated = ( + task.inferred_size_b * max(float(args.gpu_memory_per_billion_gb), 0.0) + + max(float(args.gpu_memory_headroom_gb), 0.0) + ) + return max(configured_floor, estimated) + + +def safe_model_name(model_id: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_id.strip("/")) + + +def build_expected_output_path(output_dir: Path, dataset: str, model_id: str, output_suffix: str = "") -> Path: + name = f"{safe_model_name(model_id)}{output_suffix}" + return output_dir / dataset / name / f"{name}_dna.json" + + +def build_expected_summary_path(output_dir: Path, dataset: str, model_id: str, output_suffix: str = "") -> Path: + name = f"{safe_model_name(model_id)}{output_suffix}" + return output_dir / dataset / name / f"{name}_summary.json" + + +def build_expected_response_cache_path(output_dir: Path, dataset: str, model_id: str, output_suffix: str = "") -> Path: + name = f"{safe_model_name(model_id)}{output_suffix}" + return output_dir / dataset / name / "responses.json" + + +def compute_dna_distance_to_base( + output_dir: Path, + dataset: str, + model_id: str, + output_suffix: str, +) -> Optional[dict[str, float | str]]: + if not output_suffix: + return None + + rerun_path = build_expected_output_path(output_dir, dataset, model_id, output_suffix) + base_path = build_expected_output_path(output_dir, dataset, model_id, "") + if not rerun_path.exists() or not base_path.exists(): + return None + + try: + rerun_payload = json.loads(rerun_path.read_text(encoding="utf-8")) + base_payload = json.loads(base_path.read_text(encoding="utf-8")) + rerun_vector = np.asarray(rerun_payload["signature"], dtype=np.float32) + base_vector = np.asarray(base_payload["signature"], dtype=np.float32) + except Exception as exc: + logger.warning("Failed to load DNA vectors for distance comparison: %s", exc) + return None + + if rerun_vector.shape != base_vector.shape: + return { + "base_output_path": str(base_path), + "rerun_output_path": str(rerun_path), + "error": f"shape mismatch: base={base_vector.shape} rerun={rerun_vector.shape}", + } + + norms = float(np.linalg.norm(base_vector) * np.linalg.norm(rerun_vector)) + cosine = 1.0 if norms == 0.0 else float(1.0 - np.dot(base_vector, rerun_vector) / norms) + return { + "base_output_path": str(base_path), + "rerun_output_path": str(rerun_path), + "cosine": cosine, + "euclidean": float(np.linalg.norm(base_vector - rerun_vector)), + } + + +def choose_cache_root(cache_subdir: str) -> Path: + candidates: list[tuple[int, Path]] = [] + for base in CACHE_CANDIDATES: + if not base.exists(): + continue + try: + usage = shutil.disk_usage(base) + except OSError: + continue + candidates.append((usage.free, base)) + if not candidates: + raise RuntimeError("Neither /shared/hdd nor /shared/ssd is available.") + _, best_base = max(candidates, key=lambda item: item[0]) + return best_base / cache_subdir + + +def build_cache_env(cache_root: Path) -> dict[str, str]: + hf_home = cache_root / "hf_home" + torch_home = cache_root / "torch_home" + sentence_home = cache_root / "sentence_transformers" + xdg_cache_home = cache_root / "xdg_cache" + hf_modules_cache = cache_root / "hf_modules" + triton_cache = cache_root / "triton" + tmp_dir = cache_root / "tmp" + reptrace_cache = cache_root / "reptrace_cache" + for path in (hf_home, torch_home, sentence_home, xdg_cache_home, hf_modules_cache, triton_cache, tmp_dir, reptrace_cache): + path.mkdir(parents=True, exist_ok=True) + + env = os.environ.copy() + env["HF_HOME"] = str(hf_home) + env["HUGGINGFACE_HUB_CACHE"] = str(hf_home / "hub") + env["TRANSFORMERS_CACHE"] = str(hf_home / "hub") + env["TORCH_HOME"] = str(torch_home) + env["SENTENCE_TRANSFORMERS_HOME"] = str(sentence_home) + env["XDG_CACHE_HOME"] = str(xdg_cache_home) + env["HF_MODULES_CACHE"] = str(hf_modules_cache) + env["TRITON_CACHE_DIR"] = str(triton_cache) + env["TMPDIR"] = str(tmp_dir) + env["TEMP"] = str(tmp_dir) + env["TMP"] = str(tmp_dir) + env["REPTRACE_CACHE_DIR"] = str(reptrace_cache) + return env + + +def cleanup_cache_if_needed(cache_root: Path, min_free_gb: float, target_free_gb: Optional[float] = None) -> None: + target_free_gb = min_free_gb if target_free_gb is None else max(target_free_gb, min_free_gb) + try: + usage = shutil.disk_usage(cache_root.parent) + except OSError as exc: + logger.warning("Failed to inspect cache disk %s: %s", cache_root.parent, exc) + return + + free_gb = usage.free / (1024 ** 3) + if free_gb >= min_free_gb: + return + + hub_dir = cache_root / "hf_home" / "hub" + if not hub_dir.exists(): + return + + model_dirs = sorted( + [path for path in hub_dir.iterdir() if path.is_dir() and path.name.startswith("models--")], + key=lambda path: path.stat().st_mtime, + ) + logger.info( + "[cache] free space %.1f GB below low %.1f GB, cleaning to target %.1f GB in %s", + free_gb, + min_free_gb, + target_free_gb, + hub_dir, + ) + + for model_dir in model_dirs: + if free_gb >= target_free_gb: + break + try: + size_bytes = sum(file.stat().st_size for file in model_dir.rglob("*") if file.is_file()) + shutil.rmtree(model_dir, ignore_errors=False) + reclaimed_gb = size_bytes / (1024 ** 3) + free_gb += reclaimed_gb + measured_free_gb = free_gb + try: + measured_free_gb = shutil.disk_usage(cache_root.parent).free / (1024 ** 3) + free_gb = measured_free_gb + except OSError: + pass + logger.info( + "[cache] removed %s, reclaimed_est=%.1f GB, free_now=%.1f GB", + model_dir.name, + reclaimed_gb, + measured_free_gb, + ) + except Exception as exc: + logger.warning("[cache] failed to remove %s: %s", model_dir, exc) + + +def model_id_to_hf_cache_dirname(model_id: str) -> str: + normalized = model_id.strip("/").replace("/", "--") + return f"models--{normalized}" + + +def delete_hf_model_cache_dir(cache_root: Path, model_id: str) -> bool: + hub_dir = cache_root / "hf_home" / "hub" + if not hub_dir.exists(): + return False + model_dir = hub_dir / model_id_to_hf_cache_dirname(model_id) + if not model_dir.exists(): + return False + shutil.rmtree(model_dir, ignore_errors=False) + return True + + +def has_complete_non_empty_responses(response_path: Path) -> bool: + if not response_path.exists(): + logger.warning("[cache] keep cache: responses file missing at %s", response_path) + return False + + try: + with response_path.open("r", encoding="utf-8") as handle: + payload = json.load(handle) + except Exception as exc: + logger.warning("[cache] keep cache: failed to parse responses file %s: %s", response_path, exc) + return False + + if not isinstance(payload, dict): + logger.warning("[cache] keep cache: invalid responses payload type at %s", response_path) + return False + + items = payload.get("items") + if not isinstance(items, list) or not items: + logger.warning("[cache] keep cache: responses items missing/empty at %s", response_path) + return False + + count = payload.get("count") + if isinstance(count, int) and count != len(items): + logger.warning("[cache] keep cache: responses count mismatch at %s (count=%s items=%s)", response_path, count, len(items)) + return False + + for idx, item in enumerate(items): + if not isinstance(item, dict): + logger.warning("[cache] keep cache: responses item %d is not an object at %s", idx, response_path) + return False + response = item.get("response") + if not isinstance(response, str) or not response.strip(): + logger.warning("[cache] keep cache: responses item %d is empty at %s", idx, response_path) + return False + + return True + + +def should_evict_finished_model_cache( + task: ModelTask, + record: dict[str, Any], + args: argparse.Namespace, +) -> bool: + if task.provider != "huggingface": + return False + if args.cache_evict_finished == "never": + return False + status = str(record.get("status")) + if args.cache_evict_finished == "all": + # Evict after any terminal failure to avoid repeatedly retaining unusable model caches. + if status.startswith("failed_"): + return True + if status != "success": + logger.info("[cache] keep cache: model=%s status=%s is not success", task.model_id, status) + return False + + output_path = build_expected_output_path(args.output_dir, args.dataset, task.model_id, args.output_suffix) + summary_path = build_expected_summary_path(args.output_dir, args.dataset, task.model_id, args.output_suffix) + response_path = build_expected_response_cache_path(args.output_dir, args.dataset, task.model_id, args.output_suffix) + + if not output_path.exists(): + logger.warning("[cache] keep cache: DNA file missing for %s at %s", task.model_id, output_path) + return False + if not summary_path.exists(): + logger.warning("[cache] keep cache: summary file missing for %s at %s", task.model_id, summary_path) + return False + if not has_complete_non_empty_responses(response_path): + logger.warning("[cache] keep cache: response validation failed for %s", task.model_id) + return False + + return True + + +def cleanup_cache_if_needed_smart( + cache_root: Path, + min_free_gb: float, + target_free_gb: float, + protected_dirnames: set[str], + cleanup_lock: threading.Lock, +) -> None: + with cleanup_lock: + try: + usage = shutil.disk_usage(cache_root.parent) + except OSError as exc: + logger.warning("Failed to inspect cache disk %s: %s", cache_root.parent, exc) + return + + free_gb = usage.free / (1024 ** 3) + if free_gb >= min_free_gb: + return + + hub_dir = cache_root / "hf_home" / "hub" + if not hub_dir.exists(): + return + + model_dirs = sorted( + [path for path in hub_dir.iterdir() if path.is_dir() and path.name.startswith("models--")], + key=lambda path: path.stat().st_mtime, + ) + logger.info( + "[cache] free space %.1f GB below low %.1f GB, cleaning to target %.1f GB in %s", + free_gb, + min_free_gb, + target_free_gb, + hub_dir, + ) + + removed_any = False + for model_dir in model_dirs: + if free_gb >= target_free_gb: + break + if model_dir.name in protected_dirnames: + continue + try: + size_bytes = sum(file.stat().st_size for file in model_dir.rglob("*") if file.is_file()) + shutil.rmtree(model_dir, ignore_errors=False) + reclaimed_gb = size_bytes / (1024 ** 3) + free_gb += reclaimed_gb + measured_free_gb = free_gb + try: + measured_free_gb = shutil.disk_usage(cache_root.parent).free / (1024 ** 3) + free_gb = measured_free_gb + except OSError: + pass + removed_any = True + logger.info( + "[cache] removed %s, reclaimed_est=%.1f GB, free_now=%.1f GB", + model_dir.name, + reclaimed_gb, + measured_free_gb, + ) + except Exception as exc: + logger.warning("[cache] failed to remove %s: %s", model_dir, exc) + + if not removed_any and free_gb < target_free_gb: + logger.warning( + "[cache] low space persists (%.1f GB < %.1f GB target) but no removable model cache found after protection.", + free_gb, + target_free_gb, + ) + + +def classify_failure(output: str, timed_out: bool, return_code: Optional[int] = None) -> str: + text = output or "" + lines = [line.strip() for line in text.replace("\r", "\n").splitlines() if line.strip()] + filtered_lines = [ + line + for line in lines + if "resource_tracker" not in line.lower() + and "deprecationwarning" not in line.lower() + ] + analysis_text = "\n".join(filtered_lines) if filtered_lines else text + lower_text = analysis_text.lower() + + if timed_out: + return "failed_timeout" + if return_code in {-15, 143}: + return "failed_killed" + + # Explicit terminal conditions first. + if "returned only empty responses" in lower_text: + return "failed_empty_response" + if "access denied" in lower_text or "gated repository" in lower_text or "is gated" in lower_text: + return "failed_gated" + if "authentication" in lower_text or "api key" in lower_text or "unauthorized" in lower_text: + return "failed_auth" + if "no space left on device" in lower_text or "disk quota exceeded" in lower_text: + return "failed_disk_full" + if "cuda out of memory" in lower_text or "outofmemoryerror" in lower_text or "memoryerror" in lower_text: + return "failed_oom" + if "unsupported architecture" in lower_text or "requires newer transformers version" in lower_text: + return "failed_unsupported_arch" + if "not a string" in lower_text and ("gguf" in lower_text or "mlx" in lower_text): + return "failed_unsupported_format" + if "file not found" in lower_text or "filenotfounderror" in lower_text or "no such file or directory" in lower_text: + return "failed_missing_file" + if "[errno 5]" in lower_text or "input/output error" in lower_text: + return "failed_io_error" + if "resource_tracker" in lower_text: + return "failed_process_teardown" + + # Then use Python exception class names from traceback lines when available. + exception_candidates = re.findall(r"([A-Za-z_][A-Za-z0-9_.]*(?:Error|Exception))\s*:", analysis_text) + exception_name = exception_candidates[-1].split(".")[-1] if exception_candidates else "" + exception_status_map = { + "FileNotFoundError": "failed_missing_file", + "IsADirectoryError": "failed_missing_file", + "NotADirectoryError": "failed_missing_file", + "PermissionError": "failed_io_error", + "OSError": "failed_io_error", + "IOError": "failed_io_error", + "TimeoutError": "failed_network", + "ReadTimeout": "failed_network", + "ConnectTimeout": "failed_network", + "ConnectionError": "failed_network", + "HTTPError": "failed_network", + "TypeError": "failed_type_error", + "ValueError": "failed_value_error", + "MemoryError": "failed_oom", + "OutOfMemoryError": "failed_oom", + "CudaOutOfMemoryError": "failed_oom", + } + mapped = exception_status_map.get(exception_name) + if mapped: + return mapped + + if re.search(r"\b(connection reset|connection refused|name resolution|dns|ssl|certificate verify failed)\b", lower_text): + return "failed_network" + if re.search(r"\b(read timeout|connect timeout|timed out|network error)\b", lower_text): + return "failed_network" + + return "failed" + + +def error_bucket_from_status(status: str) -> str: + return status if status.startswith("failed_") else "failed" + + +RETRYABLE_FAILURES = { + "failed", + "failed_timeout", + "failed_killed", + "failed_oom", + "failed_network", + "failed_disk_full", + "failed_io_error", + "failed_process_teardown", + "failed_missing_file", + "failed_empty_response", +} + + +def is_retryable_failure(status: str) -> bool: + """Return whether another attempt could plausibly succeed without a code/config change.""" + return status in RETRYABLE_FAILURES + + +def task_key(provider: str, model_id: str) -> str: + return f"{provider}::{model_id}" + + +def find_latest_status_file(output_dir: Path) -> Optional[Path]: + candidates = sorted( + [path for path in output_dir.glob("*/status.jsonl") if path.is_file()], + key=lambda path: path.stat().st_mtime, + reverse=True, + ) + return candidates[0] if candidates else None + + +def load_latest_records(status_path: Optional[Path]) -> dict[str, dict]: + latest: dict[str, dict] = {} + if status_path is None or not status_path.exists(): + return latest + with status_path.open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + key = task_key(str(record.get("provider", "")), str(record.get("model_id", ""))) + latest[key] = record + return latest + + +def load_global_state(state_path: Path) -> dict[str, dict]: + if not state_path.exists(): + return {} + try: + payload = json.loads(state_path.read_text(encoding="utf-8")) + except json.JSONDecodeError: + logger.warning("Failed to parse global state file: %s", state_path) + return {} + if not isinstance(payload, dict): + return {} + return {str(key): value for key, value in payload.items() if isinstance(value, dict)} + + +def write_global_state(state_path: Path, state: dict[str, dict]) -> None: + state_path.parent.mkdir(parents=True, exist_ok=True) + state_path.write_text(json.dumps(state, ensure_ascii=False, indent=2), encoding="utf-8") + + +def should_enqueue(task: ModelTask, args: argparse.Namespace, run_state: dict[str, dict], global_state: dict[str, dict]) -> bool: + key = task_key(task.provider, task.model_id) + run_record = run_state.get(key) + global_record = global_state.get(key) + mode = "retry-failed" if args.retry_failures else args.resume_mode + + if mode == "all": + return True + if mode == "continue": + return run_record is None or str(run_record.get("status", "")) == "waiting_retry" + if mode == "retry-failed": + scope = run_record or global_record + if scope is None: + return False + status = str(scope.get("status", "")) + return status.startswith("failed") or ( + status == "waiting_retry" and str(scope.get("last_status", "")).startswith("failed") + ) + if mode == "retry-non-success": + scope = run_record or global_record + return scope is not None and str(scope.get("status", "")) != "success" + if mode == "new": + return global_record is None or str(global_record.get("status", "")) != "success" + return True + + +def append_status(status_path: Path, record: dict, lock: threading.Lock) -> None: + with lock: + status_path.parent.mkdir(parents=True, exist_ok=True) + with status_path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(record, ensure_ascii=False) + "\n") + + +def append_global_status(journal_path: Path, record: dict, lock: threading.Lock) -> None: + with lock: + journal_path.parent.mkdir(parents=True, exist_ok=True) + with journal_path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(record, ensure_ascii=False) + "\n") + + +def update_global_state( + state_path: Path, + state: dict[str, dict], + record: dict, + lock: threading.Lock, +) -> None: + key = task_key(str(record["provider"]), str(record["model_id"])) + with lock: + state[key] = record + write_global_state(state_path, state) + + +def build_command(task: ModelTask, args: argparse.Namespace, gpu_id: Optional[int]) -> list[str]: + command = [ + PYTHON_BIN, + "-m", + "llm_dna.cli", + "--model-name", + task.model_id, + "--model-type", + task.provider, + "--dataset", + args.dataset, + "--max-samples", + str(args.max_samples), + "--data-root", + str(args.data_root), + "--output-dir", + str(args.output_dir), + "--output-suffix", + str(getattr(args, "output_suffix", "")), + "--dna-dim", + str(args.dna_dim), + "--reduction-method", + args.reduction_method, + "--embedding-merge", + args.embedding_merge, + "--max-length", + str(args.max_length), + "--temperature", + str(getattr(args, "temperature", 0.0)), + "--top-p", + str(getattr(args, "top_p", 1.0)), + "--generation-seed", + str(getattr(args, "generation_seed", 42)), + "--log-level", + "INFO", + ] + if args.metadata_file.exists(): + command.extend(["--metadata-file", str(args.metadata_file)]) + if bool(getattr(args, "try_vllm", False)): + command.append("--try-vllm") + if bool(getattr(args, "ignore_response_cache", False)): + command.append("--ignore-response-cache") + if bool(getattr(args, "do_sample", False)): + command.append("--do-sample") + else: + command.append("--no-do-sample") + if gpu_id is not None: + command.extend(["--device", "cuda", "--gpus", str(gpu_id)]) + else: + command.extend(["--device", "cpu"]) + return command + + +def _coerce_process_output(value: object) -> str: + if value is None: + return "" + if isinstance(value, bytes): + return value.decode("utf-8", errors="replace") + return str(value) + + +def _summarize_process_reason(output: str) -> str: + if not output: + return "" + + normalized = output.replace("\r", "\n") + lines = [line.strip() for line in normalized.splitlines() if line.strip()] + if not lines: + return output.strip() + + # Prefer semantically meaningful error lines over teardown/runtime warnings. + preferred_patterns = [ + r"\bDNA extraction failed\b", + r"\bERROR\b", + r"\bTraceback\b", + r"\b(\w+(?:\.\w+)*(?:Error|Exception))\b", + ] + noise_patterns = [ + r"resource_tracker", + r"DeprecationWarning", + r"swigvarlink", + r"^Loading checkpoint shards:", + ] + + for pattern in preferred_patterns: + for line in reversed(lines): + if any(re.search(noise, line, flags=re.IGNORECASE) for noise in noise_patterns): + continue + if re.search(pattern, line, flags=re.IGNORECASE): + return line + + for line in reversed(lines): + if any(re.search(noise, line, flags=re.IGNORECASE) for noise in noise_patterns): + continue + return line + + return lines[-1] + + +def _run_subprocess_with_optional_streaming( + command: list[str], + cwd: str, + env: dict[str, str], + stream_logs: bool, + log_prefix: str, + child_log_mode: str, + timeout_seconds: float, +) -> tuple[int, str, str, bool]: + timeout = timeout_seconds if timeout_seconds > 0 else None + if not stream_logs: + try: + completed = subprocess.run( + command, + cwd=cwd, + env=env, + capture_output=True, + text=True, + check=False, + timeout=timeout, + ) + return completed.returncode, completed.stdout.strip(), completed.stderr.strip(), False + except subprocess.TimeoutExpired as exc: + return ( + -signal.SIGKILL, + _coerce_process_output(exc.output).strip(), + _coerce_process_output(exc.stderr).strip(), + True, + ) + + process = subprocess.Popen( + command, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + bufsize=1, + start_new_session=True, + ) + + stdout_lines: deque[str] = deque(maxlen=8000) + stderr_lines: deque[str] = deque(maxlen=8000) + + def _reader(pipe: Optional[Any], sink: deque[str], level: int, stream_name: str) -> None: + if pipe is None: + return + try: + for raw_line in iter(pipe.readline, ""): + line = raw_line.rstrip("\n") + sink.append(line) + if not line.strip(): + continue + if child_log_mode == "cache-only": + if "[cache-resume]" in line: + logging.info("%s [cache] %s", log_prefix, _format_cache_resume_line(line)) + continue + logging.log(level, "%s [%s] %s", log_prefix, stream_name, line) + finally: + pipe.close() + + out_thread = threading.Thread( + target=_reader, + args=(process.stdout, stdout_lines, logging.INFO, "stdout"), + daemon=True, + ) + err_thread = threading.Thread( + target=_reader, + args=(process.stderr, stderr_lines, logging.WARNING, "stderr"), + daemon=True, + ) + out_thread.start() + err_thread.start() + timed_out = False + try: + return_code = process.wait(timeout=timeout) + except subprocess.TimeoutExpired: + timed_out = True + logger.warning("%s exceeded %.0fs timeout; terminating process group", log_prefix, timeout_seconds) + try: + os.killpg(process.pid, signal.SIGTERM) + return_code = process.wait(timeout=30) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL) + return_code = process.wait() + except ProcessLookupError: + return_code = process.wait() + out_thread.join() + err_thread.join() + + stdout = "\n".join(stdout_lines).strip() + stderr = "\n".join(stderr_lines).strip() + return return_code, stdout, stderr, timed_out + + +def _extract_cache_resume_line(output: str) -> str: + if not output: + return "" + + normalized = output.replace("\r", "\n") + lines = [line.strip() for line in normalized.splitlines() if line.strip()] + cache_lines = [line for line in lines if "[cache-resume]" in line] + return cache_lines[-1] if cache_lines else "" + + +def _format_cache_resume_line(line: str) -> str: + found_match = re.search(r"found=(\d+)", line) + total_match = re.search(r"total=(\d+)", line) + action_match = re.search(r"action=([a-zA-Z0-9_-]+)", line) + missing_match = re.search(r"missing=(\d+)", line) + + found = found_match.group(1) if found_match else "0" + total = total_match.group(1) if total_match else "0" + action = action_match.group(1) if action_match else "unknown" + missing = missing_match.group(1) if missing_match else None + + if missing is not None: + return f"cache found={found}/{total} action={action} missing={missing}" + return f"cache found={found}/{total} action={action}" + + +def is_vllm_available(python_bin: str) -> bool: + try: + completed = subprocess.run( + [python_bin, "-c", "import vllm"], + capture_output=True, + text=True, + check=False, + timeout=15, + ) + return completed.returncode == 0 + except Exception: + return False + + +def run_task( + task: ModelTask, + args: argparse.Namespace, + gpu_id: Optional[int], + cache_state: Optional[CacheRuntimeState] = None, +) -> dict: + start = time.time() + cache_root = choose_cache_root(args.cache_subdir) + + # Add detailed logging for OpenRouter models + if task.provider == "openrouter": + logger.info(f"[openrouter-task] Starting OpenRouter model: {task.model_id}") + logger.info(f"[openrouter-task] Dataset: {args.dataset}, Max samples: {args.max_samples}") + logger.info(f"[openrouter-task] Cache root: {cache_root}") + + if task.provider == "huggingface" and cache_state is not None: + with cache_state.state_lock: + protected_model_ids = set(cache_state.pending_hf_models) | set(cache_state.running_hf_models) + protected_dirnames = {model_id_to_hf_cache_dirname(mid) for mid in protected_model_ids} + cleanup_cache_if_needed_smart( + cache_root, + args.cache_low_free_gb, + args.cache_target_free_gb, + protected_dirnames, + cache_state.cleanup_lock, + ) + elif task.provider == "huggingface": + cleanup_cache_if_needed( + cache_root, + args.cache_low_free_gb, + float(getattr(args, "cache_target_free_gb", args.cache_low_free_gb)), + ) + else: + # OpenRouter/API providers do not require local HF model-cache cleanup. + logger.debug("[cache] skip cleanup for provider=%s model=%s", task.provider, task.model_id) + env = build_cache_env(cache_root) + command = build_command(task, args, gpu_id) + output_suffix = str(getattr(args, "output_suffix", "")) + expected_output = build_expected_output_path(args.output_dir, args.dataset, task.model_id, output_suffix) + expected_summary = build_expected_summary_path(args.output_dir, args.dataset, task.model_id, output_suffix) + return_code, stdout, stderr, timed_out = _run_subprocess_with_optional_streaming( + command, + cwd=str(ROOT), + env=env, + stream_logs=bool(getattr(args, "stream_subprocess_logs", False)), + log_prefix=f"[child model={task.model_id} gpu={gpu_id}]", + child_log_mode=str(getattr(args, "child_log_mode", "cache-only")), + timeout_seconds=float(getattr(args, "model_timeout_seconds", getattr(args, "timeout", 43200.0))), + ) + combined = "\n".join(part for part in [stdout, stderr] if part).strip() + cache_resume = _extract_cache_resume_line(combined) + + # Log OpenRouter task result + if task.provider == "openrouter": + logger.info(f"[openrouter-task] {task.model_id} completed with exit code: {return_code}") + if return_code != 0: + logger.warning(f"[openrouter-task] {task.model_id} failed with return code {return_code}") + # Log first 500 chars of error output for debugging + error_snippet = combined[:500] if combined else "(no output)" + logger.warning(f"[openrouter-task] {task.model_id} error snippet: {error_snippet}") + + # Treat missing artifacts or empty responses as terminal failures, even if child exits 0. + if return_code == 0: + if not expected_output.exists() or not expected_summary.exists(): + record = { + "provider": task.provider, + "model_id": task.model_id, + "status": "failed_missing_file", + "reason": "exit=0 missing expected DNA/summary outputs", + "gpu_id": gpu_id, + "elapsed_seconds": time.time() - start, + "output_path": str(expected_output), + "summary_path": str(expected_summary), + "cache_root": str(cache_root), + } + if cache_resume: + record["cache_resume"] = cache_resume + return record + + response_path = build_expected_response_cache_path(args.output_dir, args.dataset, task.model_id, output_suffix) + if not has_complete_non_empty_responses(response_path): + record = { + "provider": task.provider, + "model_id": task.model_id, + "status": "failed_empty_response", + "reason": "exit=0 responses missing/invalid/empty", + "gpu_id": gpu_id, + "elapsed_seconds": time.time() - start, + "output_path": str(expected_output), + "summary_path": str(expected_summary), + "cache_root": str(cache_root), + } + if cache_resume: + record["cache_resume"] = cache_resume + return record + + if return_code == 0: + record = { + "provider": task.provider, + "model_id": task.model_id, + "status": "success", + "reason": "ok", + "gpu_id": gpu_id, + "elapsed_seconds": time.time() - start, + "output_path": str(expected_output if expected_output.exists() else expected_output), + "summary_path": str(expected_summary if expected_summary.exists() else expected_summary), + "cache_root": str(cache_root), + } + if cache_resume: + record["cache_resume"] = cache_resume + dna_distance = compute_dna_distance_to_base( + args.output_dir, + args.dataset, + task.model_id, + output_suffix, + ) + if dna_distance is not None: + record["dna_distance_to_base"] = dna_distance + if task.provider == "openrouter": + logger.info(f"[openrouter-task] {task.model_id} succeeded in {record['elapsed_seconds']:.1f}s") + if should_evict_finished_model_cache(task, record, args): + try: + if cache_state is not None: + with cache_state.cleanup_lock: + removed = delete_hf_model_cache_dir(cache_root, task.model_id) + else: + removed = delete_hf_model_cache_dir(cache_root, task.model_id) + if removed: + logger.info("[cache] evicted finished model cache: %s", model_id_to_hf_cache_dirname(task.model_id)) + except Exception as exc: + logger.warning("[cache] failed to evict finished model cache for %s: %s", task.model_id, exc) + return record + status = classify_failure(combined, timed_out=timed_out, return_code=return_code) + reason = _summarize_process_reason(combined) + + # Extra logging for OpenRouter failures + if task.provider == "openrouter" and status.startswith("failed"): + logger.error(f"[openrouter-task] {task.model_id} failed with status '{status}': {reason}") + + record = { + "provider": task.provider, + "model_id": task.model_id, + "status": status, + "reason": f"exit={return_code} {reason}".strip(), + "gpu_id": gpu_id, + "elapsed_seconds": time.time() - start, + "output_path": None, + "summary_path": None, + "cache_root": str(cache_root), + } + if cache_resume: + record["cache_resume"] = cache_resume + return record + + +def make_run_dir(output_dir: Path, run_name: Optional[str]) -> Path: + run_id = run_name or f"dna_run_{datetime.now().strftime('%Y%m%d-%H%M%S')}" + run_dir = output_dir / run_id + run_dir.mkdir(parents=True, exist_ok=True) + return run_dir + + +def acquire_gpu_id( + allowed_gpu_ids: Optional[list[int]], + reserved_gpu_ids: set[int], + reservation_cond: threading.Condition, + min_free_gb: float, + poll_seconds: float, + excluded_gpu_ids: Optional[set[int]] = None, +) -> int: + excluded = excluded_gpu_ids or set() + while True: + with reservation_cond: + stats = query_gpu_stats() + allowed = allowed_gpu_ids if allowed_gpu_ids else [stat.gpu_id for stat in stats] + gpu_id = choose_best_gpu( + stats=stats, + allowed_gpu_ids=[gid for gid in allowed if gid not in excluded], + busy_gpu_ids=reserved_gpu_ids, + min_free_gb=min_free_gb, + ) + if gpu_id is None and excluded: + # An untried GPU is preferred, but do not wait forever when only a + # previously attempted card has enough memory for this model. + gpu_id = choose_best_gpu( + stats=stats, + allowed_gpu_ids=allowed, + busy_gpu_ids=reserved_gpu_ids, + min_free_gb=min_free_gb, + ) + if gpu_id is not None: + reserved_gpu_ids.add(gpu_id) + return gpu_id + + logger.info( + "All selected GPUs are busy or below %.1f GiB free; waiting %.1fs before retry.", + min_free_gb, + poll_seconds, + ) + reservation_cond.wait(timeout=max(poll_seconds, 1.0)) + + +def release_gpu_id(gpu_id: int, reserved_gpu_ids: set[int], reservation_cond: threading.Condition) -> None: + with reservation_cond: + reserved_gpu_ids.discard(gpu_id) + reservation_cond.notify_all() + + +def save_run_manifest(run_dir: Path, tasks: list[ModelTask], args: argparse.Namespace) -> None: + manifest = { + "created_at": datetime.now().isoformat(), + "providers": args.providers, + "dataset": args.dataset, + "output_suffix": args.output_suffix, + "ignore_response_cache": args.ignore_response_cache, + "temperature": args.temperature, + "do_sample": args.do_sample, + "top_p": args.top_p, + "generation_seed": args.generation_seed, + "max_samples": args.max_samples, + "resume_mode": "retry-failed" if args.retry_failures else args.resume_mode, + "model_timeout_seconds": args.model_timeout_seconds, + "max_retries": args.max_retries, + "oom_retries": args.oom_retries, + "retry_delay_seconds": args.retry_delay_seconds, + "gpu_memory_per_billion_gb": args.gpu_memory_per_billion_gb, + "gpu_memory_headroom_gb": args.gpu_memory_headroom_gb, + "task_count": len(tasks), + "tasks": [ + { + "provider": task.provider, + "model_id": task.model_id, + "inferred_size_b": task.inferred_size_b, + } + for task in tasks + ], + } + (run_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + + +def queue_tasks(tasks: Iterable[ModelTask]) -> queue.Queue[Optional[TaskWork]]: + q: queue.Queue[Optional[TaskWork]] = queue.Queue() + for task in tasks: + q.put(TaskWork(task=task)) + return q + + +def main() -> int: + args = parse_args() + if args.cache_target_free_gb < args.cache_low_free_gb: + logger.warning( + "cache_target_free_gb (%.1f) < cache_low_free_gb (%.1f); clamping target to low.", + args.cache_target_free_gb, + args.cache_low_free_gb, + ) + args.cache_target_free_gb = args.cache_low_free_gb + args.output_dir.mkdir(parents=True, exist_ok=True) + if args.try_vllm: + vllm_available = is_vllm_available(PYTHON_BIN) + logger.info("vLLM availability check: %s", "available" if vllm_available else "unavailable") + if not vllm_available: + logger.warning("vLLM is not installed in %s; disabling --try-vllm for this run.", PYTHON_BIN) + args.try_vllm = False + mode = "retry-failed" if args.retry_failures else args.resume_mode + resume_status = args.resume_status + if resume_status is None and args.resume_latest: + resume_status = find_latest_status_file(args.output_dir) + + if resume_status is not None: + run_dir = resume_status.parent + status_path = resume_status + else: + run_dir = make_run_dir(args.output_dir, args.run_name) + status_path = run_dir / "status.jsonl" + + run_state = load_latest_records(status_path) + global_state = load_global_state(args.global_state) + + tasks = [task for task in load_all_tasks(args) if should_enqueue(task, args, run_state, global_state)] + if args.limit > 0: + tasks = tasks[: args.limit] + + if resume_status is None or not (run_dir / "manifest.json").exists(): + save_run_manifest(run_dir, tasks, args) + + max_concurrent = min(args.max_concurrent_gpus, MAX_CONCURRENT_GPUS) + fixed_gpu_ids = parse_gpu_ids(args.gpus, max_concurrent) + dynamic_gpu_pool = not bool(args.gpus) + if dynamic_gpu_pool: + detected_gpu_count = len(query_gpu_stats()) + use_gpu_scheduler = detected_gpu_count > 0 + worker_slots = min(max_concurrent, detected_gpu_count) if detected_gpu_count > 0 else 1 + else: + use_gpu_scheduler = bool(fixed_gpu_ids) + worker_slots = len(fixed_gpu_ids) if fixed_gpu_ids else 1 + + logger.info("run_dir=%s", run_dir) + logger.info("status_path=%s", status_path) + logger.info("python=%s", PYTHON_BIN) + logger.info("providers=%s", args.providers) + logger.info("resume_mode=%s", mode) + logger.info("resume_status=%s", resume_status) + logger.info("dataset=%s", args.dataset) + logger.info("queued_models=%d", len(tasks)) + logger.info("worker_gpus=%s", "dynamic_all" if dynamic_gpu_pool else fixed_gpu_ids) + logger.info("min_gpu_free_gb=%.1f", args.min_gpu_free_gb) + logger.info("gpu_memory_estimate=%.1f GiB/B + %.1f GiB headroom", args.gpu_memory_per_billion_gb, args.gpu_memory_headroom_gb) + logger.info("cache_watermark_low_gb=%.1f", args.cache_low_free_gb) + logger.info("cache_watermark_target_gb=%.1f", args.cache_target_free_gb) + logger.info("oom_retries=%d", max(args.oom_retries, 0)) + logger.info("max_retries=%d", max(args.max_retries, 0)) + logger.info("retry_delay_seconds=%.1f", max(args.retry_delay_seconds, 0.0)) + logger.info("model_timeout_seconds=%.1f", max(args.model_timeout_seconds, 0.0)) + + if not tasks: + logger.info("No models to run.") + return 0 + + task_queue = queue_tasks(tasks) + + status_lock = threading.Lock() + global_state_lock = threading.Lock() + failure_count = 0 + failure_lock = threading.Lock() + reserved_gpu_ids: set[int] = set() + reservation_cond = threading.Condition() + error_summary: dict[str, dict[str, Any]] = {} + cache_state = CacheRuntimeState( + pending_hf_models={task.model_id for task in tasks if task.provider == "huggingface"}, + running_hf_models=set(), + state_lock=threading.Lock(), + cleanup_lock=threading.Lock(), + ) + + def record_error(record: dict) -> None: + bucket = error_bucket_from_status(str(record.get("status", "failed"))) + model_id = str(record.get("model_id", "")) + entry = error_summary.setdefault(bucket, {"count": 0, "models": []}) + entry["count"] = int(cast(int, entry["count"])) + 1 + models = cast(list[str], entry["models"]) + if model_id and model_id not in models: + models.append(model_id) + + def worker(worker_idx: int) -> None: + nonlocal failure_count + while True: + work = task_queue.get() + if work is None: + task_queue.task_done() + return + if work.ready_at > time.monotonic(): + # Keep delayed work in the queue without holding a worker for the full cooldown. + task_queue.put(work) + task_queue.task_done() + time.sleep(min(max(work.ready_at - time.monotonic(), 0.05), 5.0)) + continue + + task = work.task + gpu_id: Optional[int] = None + if task.provider == "huggingface": + with cache_state.state_lock: + cache_state.pending_hf_models.discard(task.model_id) + cache_state.running_hf_models.add(task.model_id) + use_gpu_for_task = use_gpu_scheduler and ( + task.provider == "huggingface" or bool(getattr(args, "openrouter_use_gpu", False)) + ) + if use_gpu_for_task: + task_min_free_gb = required_gpu_free_gb(task, args) + excluded_gpu_ids = set(work.attempted_gpu_ids) + visible_ids = set(fixed_gpu_ids) if fixed_gpu_ids else {stat.gpu_id for stat in query_gpu_stats()} + if visible_ids and visible_ids.issubset(excluded_gpu_ids): + logger.info( + "All GPUs have been attempted for model=%s; allowing reuse after cooldown.", + task.model_id, + ) + excluded_gpu_ids.clear() + gpu_id = acquire_gpu_id( + allowed_gpu_ids=fixed_gpu_ids if fixed_gpu_ids else None, + reserved_gpu_ids=reserved_gpu_ids, + reservation_cond=reservation_cond, + min_free_gb=task_min_free_gb, + poll_seconds=args.gpu_poll_seconds, + excluded_gpu_ids=excluded_gpu_ids, + ) + + logger.info( + "[start] worker=%s provider=%s gpu=%s model=%s attempt=%d", + worker_idx, + task.provider, + gpu_id, + task.model_id, + work.attempts + 1, + ) + attempt_started = time.time() + try: + record = run_task(task, args, gpu_id, cache_state) + except Exception as exc: + logger.exception("Unhandled task error for model=%s", task.model_id) + record = { + "provider": task.provider, + "model_id": task.model_id, + "status": "failed", + "reason": f"scheduler exception: {type(exc).__name__}: {exc}", + "gpu_id": gpu_id, + "elapsed_seconds": time.time() - attempt_started, + "output_path": None, + "summary_path": None, + } + finally: + if task.provider == "huggingface": + with cache_state.state_lock: + cache_state.running_hf_models.discard(task.model_id) + if gpu_id is not None: + work.attempted_gpu_ids.append(gpu_id) + release_gpu_id(gpu_id, reserved_gpu_ids, reservation_cond) + + work.attempts += 1 + status = str(record.get("status", "failed")) + max_retries = max(args.oom_retries if status == "failed_oom" else args.max_retries, 0) + can_retry = is_retryable_failure(status) and work.attempts <= max_retries + if can_retry: + work.ready_at = time.monotonic() + max(args.retry_delay_seconds, 0.0) + waiting_record = dict(record) + waiting_record.update( + { + "status": "waiting_retry", + "last_status": status, + "attempt_count": work.attempts, + "max_attempts": max_retries + 1, + "next_attempt_after": datetime.fromtimestamp( + time.time() + max(args.retry_delay_seconds, 0.0) + ).isoformat(), + "attempted_gpu_ids": list(work.attempted_gpu_ids), + "run_dir": str(run_dir), + "recorded_at": datetime.now().isoformat(), + } + ) + append_status(status_path, waiting_record, status_lock) + append_global_status(args.global_status_journal, waiting_record, status_lock) + update_global_state(args.global_state, global_state, waiting_record, global_state_lock) + if task.provider == "huggingface": + with cache_state.state_lock: + cache_state.pending_hf_models.add(task.model_id) + task_queue.put(work) + logger.warning( + "[wait] model=%s status=%s attempt=%d/%d; deferred %.1fs behind queued work", + task.model_id, + status, + work.attempts, + max_retries + 1, + max(args.retry_delay_seconds, 0.0), + ) + task_queue.task_done() + continue + + if work.attempted_gpu_ids: + record["attempted_gpu_ids"] = list(work.attempted_gpu_ids) + record["attempt_count"] = work.attempts + cache_resume = str(record.get("cache_resume", "")).strip() + if cache_resume: + logger.info("[cache] model=%s %s", task.model_id, cache_resume) + record["run_dir"] = str(run_dir) + record["recorded_at"] = datetime.now().isoformat() + append_status(status_path, record, status_lock) + append_global_status(args.global_status_journal, record, status_lock) + update_global_state(args.global_state, global_state, record, global_state_lock) + if record["status"] != "success" and not str(record["status"]).startswith("skipped_"): + record_error(record) + logger.info( + "[done] worker=%s provider=%s gpu=%s model=%s status=%s elapsed=%.1fs", + worker_idx, + task.provider, + gpu_id, + task.model_id, + record["status"], + record["elapsed_seconds"], + ) + if record["status"] != "success" and not record["status"].startswith("skipped_"): + with failure_lock: + failure_count += 1 + task_queue.task_done() + + threads = [ + threading.Thread(target=worker, args=(idx,), daemon=False, name=f"gpu-worker-{idx}") + for idx in range(worker_slots) + ] + for thread in threads: + thread.start() + task_queue.join() + for _ in range(worker_slots): + task_queue.put(None) + for thread in threads: + thread.join() + + error_report = { + "generated_at": datetime.now().isoformat(), + "run_dir": str(run_dir), + "failures": error_summary, + } + (run_dir / "error_summary.json").write_text(json.dumps(error_report, ensure_ascii=False, indent=2), encoding="utf-8") + + logger.info("Completed run with %d non-skipped failures.", failure_count) + return 0 if failure_count == 0 else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_primary_analysis.sh b/scripts/run_primary_analysis.sh new file mode 100644 index 0000000..7517b38 --- /dev/null +++ b/scripts/run_primary_analysis.sh @@ -0,0 +1,64 @@ +#!/usr/bin/env bash +# Analyze the completed primary sweep. Repeats 1-2 are training/gallery data; +# repeats 3-4 are held-out queries. The command refuses cohorts below 250. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +DATA_DIR="${DATA_DIR:-out/primary_robustness_300/rand_chinese}" +ANALYSIS_ROOT="${ANALYSIS_ROOT:-out/primary_robustness_300_analysis}" +MINIMUM_MODELS="${MINIMUM_MODELS:-250}" +PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" +if [[ ! -x "$PYTHON_BIN" ]]; then + PYTHON_BIN="$(command -v python3)" +fi + +settings=("0.2 0.8" "0.2 0.9" "0.2 1.0" "0.3 0.8" "0.3 0.9" "0.3 1.0" "0.5 0.8" "0.5 0.9" "0.5 1.0" "0.7 0.8" "0.7 0.9" "0.7 1.0") + +code() { + "$PYTHON_BIN" - "$1" <<'PY' +import sys +print(f"{int(round(float(sys.argv[1]) * 10)):02d}") +PY +} + +exact_args=( + --dataset-dir "$DATA_DIR" + --minimum-models "$MINIMUM_MODELS" + --output-dir "$ANALYSIS_ROOT/exact" + --temperature-bound 0.2 --temperature-bound 0.3 + --temperature-bound 0.5 --temperature-bound 0.7 +) +tree_args=( + --dataset-dir "$DATA_DIR" + --reference-suffix _t00_p10_r1 + --minimum-models "$MINIMUM_MODELS" + --output-dir "$ANALYSIS_ROOT/tree" +) + +for repeat in 1 2; do + exact_args+=(--train-run "t00_r${repeat}:_t00_p10_r${repeat}:0.0:1.0:${repeat}") +done +for repeat in 3 4; do + exact_args+=(--test-run "t00_r${repeat}:_t00_p10_r${repeat}:0.0:1.0:${repeat}") + tree_args+=(--run "t00_r${repeat}:_t00_p10_r${repeat}:0.0:1.0:${repeat}") +done + +for setting in "${settings[@]}"; do + read -r temperature top_p <<< "$setting" + t_code="$(code "$temperature")" + p_code="$(code "$top_p")" + for repeat in 1 2; do + exact_args+=(--train-run "t${t_code}_p${p_code}_r${repeat}:_t${t_code}_p${p_code}_r${repeat}:${temperature}:${top_p}:${repeat}") + done + for repeat in 3 4; do + spec="t${t_code}_p${p_code}_r${repeat}:_t${t_code}_p${p_code}_r${repeat}:${temperature}:${top_p}:${repeat}" + exact_args+=(--test-run "$spec") + tree_args+=(--run "$spec") + done +done + +"$PYTHON_BIN" scripts/evaluate_exact_dna_classification.py "${exact_args[@]}" +"$PYTHON_BIN" scripts/analyze_tree_aware_errors.py "${tree_args[@]}" diff --git a/scripts/run_primary_robustness_experiment.sh b/scripts/run_primary_robustness_experiment.sh new file mode 100644 index 0000000..2126b66 --- /dev/null +++ b/scripts/run_primary_robustness_experiment.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash +# Primary exact-model robustness dataset: 300 models, a complete factorial +# decoding grid, and three independent generation seeds. T=0 is emitted once. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +export HF_JSONL="${HF_JSONL:-configs/rand_chinese_stratified_300.jsonl}" +export OUTPUT_DIR="${OUTPUT_DIR:-out/primary_robustness_300}" +export DATASET="${DATASET:-rand_chinese}" +export SWEEP_NAME="${SWEEP_NAME:-primary_robustness_300}" +export LOG_DIR="${LOG_DIR:-logs/primary_robustness_300_$(date +%Y%m%d-%H%M%S)}" + +export TEMPERATURES="0.2 0.3 0.5 0.7" +export TOP_PS="0.8 0.9 1.0" +export REPEATS="${REPEATS:-1 2 3 4}" +export INCLUDE_T00="1" +export GENERATION_SEED_BASE="${GENERATION_SEED_BASE:-42000}" + +# Empty GPUS means all visible cards are dynamically scheduled. The worker +# cap can be raised without touching Python; 10 matches the available cluster. +export GPUS="${GPUS:-}" +export MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-10}" +export LIMIT="0" +export RESUME_MODE="all" +export TRY_VLLM="${TRY_VLLM:-1}" +export CACHE_EVICT_FINISHED="${CACHE_EVICT_FINISHED:-success}" + +exec bash scripts/run_dna_temperature_top_p_sweep.sh diff --git a/scripts/run_rbf_mmd_pilot.py b/scripts/run_rbf_mmd_pilot.py new file mode 100644 index 0000000..f920df4 --- /dev/null +++ b/scripts/run_rbf_mmd_pilot.py @@ -0,0 +1,573 @@ +#!/usr/bin/env python3 +"""Small pilot for distributional model retrieval under stochastic decoding. + +This script implements the first draft-stage experiment: +- compare single-sample cosine retrieval against exact RBF-MMD +- optionally compare a mean-response cosine baseline +- keep the experiment small, deterministic, and easy to inspect + +The pilot uses repeated response runs as samples from a prompt-conditional +response distribution. For same-setting comparisons, the repeated runs are split +into reference/query halves. For cross-setting comparisons, the reference and +query settings are specified explicitly. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +import numpy as np + + +@dataclass(frozen=True) +class SettingSpec: + temperature: float + top_p: float + + @property + def code(self) -> str: + return f"t{int(round(self.temperature * 10)):02d}_p{int(round(self.top_p * 10)):02d}" + + +@dataclass(frozen=True) +class RunRecord: + model_name: str + setting: SettingSpec + repeat: int + path: Path + prompts: list[str] + responses: list[str] + + +def parse_setting(raw: str) -> SettingSpec: + parts = raw.split(":") + if len(parts) != 2: + raise ValueError(f"Invalid setting {raw!r}; expected temperature:top_p, e.g. 0.2:0.8") + return SettingSpec(temperature=float(parts[0]), top_p=float(parts[1])) + + +def format_setting(setting: SettingSpec) -> str: + return f"{setting.temperature:g}:{setting.top_p:g}" + + +def safe_model_name(model_name: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_name.strip("/")) + + +def parse_run_dir_name(name: str) -> tuple[str, SettingSpec, int] | None: + match = re.match(r"^(?P.+)_(?Pt\d{2})_(?P

p\d{2})_r(?P\d+)$", name) + if not match: + return None + temperature = int(match.group("t")[1:]) / 10.0 + top_p = int(match.group("p")[1:]) / 10.0 + return match.group("model"), SettingSpec(temperature=temperature, top_p=top_p), int(match.group("repeat")) + + +def load_run_record(path: Path) -> RunRecord: + payload = json.loads((path / "responses.json").read_text(encoding="utf-8")) + items = payload.get("items", []) + prompts = [str(item.get("prompt", "")) for item in items] + responses = [str(item.get("response", "")) for item in items] + parsed = parse_run_dir_name(path.name) + if parsed is None: + raise ValueError(f"Unrecognized run directory name: {path.name}") + model_name, setting, repeat = parsed + return RunRecord( + model_name=model_name, + setting=setting, + repeat=repeat, + path=path, + prompts=prompts, + responses=responses, + ) + + +def load_run_records(data_dir: Path, settings: set[SettingSpec]) -> dict[tuple[str, str, int], RunRecord]: + records: dict[tuple[str, str, int], RunRecord] = {} + code_to_setting = {setting.code: setting for setting in settings} + for run_dir in sorted(data_dir.glob("*")): + if not run_dir.is_dir() or not (run_dir / "responses.json").exists(): + continue + parsed = parse_run_dir_name(run_dir.name) + if parsed is None: + continue + model_name, setting, repeat = parsed + if setting.code not in code_to_setting: + continue + records[(model_name, setting.code, repeat)] = load_run_record(run_dir) + return records + + +def select_models(records: dict[tuple[str, str, int], RunRecord], settings: list[SettingSpec], model_limit: int) -> list[str]: + per_setting: list[set[str]] = [] + for setting in settings: + model_to_repeats: dict[str, set[int]] = {} + for (model_name, code, repeat), record in records.items(): + if code != setting.code: + continue + model_to_repeats.setdefault(model_name, set()).add(repeat) + candidates = {model for model, repeats in model_to_repeats.items() if len(repeats) >= 2} + per_setting.append(candidates) + + common = sorted(set.intersection(*per_setting)) if per_setting else [] + return common if model_limit <= 0 else common[:model_limit] + + +def select_prompts(records: dict[tuple[str, str, int], RunRecord], selected_models: list[str], settings: list[SettingSpec], prompt_limit: int) -> list[str]: + prompt_sets: list[list[str]] = [] + for setting in settings: + reference = None + for model_name in selected_models: + for repeat in sorted({repeat for (m, code, repeat) in records if m == model_name and code == setting.code}): + reference = records[(model_name, setting.code, repeat)] + break + if reference is not None: + break + if reference is None: + continue + prompt_sets.append(reference.prompts) + + if not prompt_sets: + return [] + + common = list(prompt_sets[0]) + for prompts in prompt_sets[1:]: + common = [prompt for prompt in common if prompt in prompts] + return common if prompt_limit <= 0 else common[:prompt_limit] + + +def chunk_runs_for_same_setting(runs: list[RunRecord]) -> tuple[list[RunRecord], list[RunRecord]]: + runs = sorted(runs, key=lambda record: record.repeat) + if len(runs) < 2: + raise ValueError("Need at least two repeats for same-setting comparison.") + split = max(1, len(runs) // 2) + ref = runs[:split] + qry = runs[split:] + if not qry: + qry = runs[-1:] + ref = runs[:-1] + return ref, qry + + +def load_embeddings(texts: Iterable[str], cache: dict[str, np.ndarray], features: int = 4096) -> np.ndarray: + missing = [text for text in texts if text not in cache] + if missing: + from sklearn.feature_extraction.text import HashingVectorizer + + vectorizer = HashingVectorizer( + n_features=features, + analyzer="char_wb", + ngram_range=(3, 5), + alternate_sign=False, + norm=None, + lowercase=False, + ) + matrix = vectorizer.transform(missing).astype(np.float32).toarray() + for text, row in zip(missing, matrix): + norm = float(np.linalg.norm(row)) + cache[text] = row if norm == 0.0 else row / norm + return np.stack([cache[text] for text in texts], axis=0).astype(np.float32) + + +def l2_normalize_rows(matrix: np.ndarray) -> np.ndarray: + norms = np.linalg.norm(matrix, axis=1, keepdims=True) + return matrix / np.maximum(norms, 1e-12) + + +def concat_prompt_vectors(prompt_embeddings: dict[str, np.ndarray], prompts: list[str]) -> np.ndarray: + return np.concatenate([prompt_embeddings[prompt] for prompt in prompts], axis=0) + + +def cosine_distance(a: np.ndarray, b: np.ndarray) -> float: + denom = float(np.linalg.norm(a) * np.linalg.norm(b)) + if denom == 0.0: + return 1.0 + return float(1.0 - np.dot(a, b) / denom) + + +def cosine_distance_matrix(query: np.ndarray, base: np.ndarray) -> np.ndarray: + query_norm = query / np.maximum(np.linalg.norm(query, axis=1, keepdims=True), 1e-12) + base_norm = base / np.maximum(np.linalg.norm(base, axis=1, keepdims=True), 1e-12) + return 1.0 - query_norm @ base_norm.T + + +def pairwise_sqdist(a: np.ndarray, b: np.ndarray) -> np.ndarray: + diff = a[:, None, :] - b[None, :, :] + return np.sum(diff * diff, axis=2) + + +def median_bandwidth(samples: np.ndarray) -> float: + if len(samples) < 2: + return 1.0 + sq = pairwise_sqdist(samples, samples) + dist = np.sqrt(np.maximum(sq, 0.0)) + vals = dist[np.triu_indices_from(dist, k=1)] + vals = vals[np.isfinite(vals) & (vals > 0)] + if vals.size == 0: + return 1.0 + return float(np.median(vals)) + + +def rbf_kernel(a: np.ndarray, b: np.ndarray, sigma: float) -> np.ndarray: + return np.exp(-pairwise_sqdist(a, b) / (2.0 * sigma * sigma)) + + +def exact_mmd2(a: np.ndarray, b: np.ndarray, sigma: float) -> float: + kaa = rbf_kernel(a, a, sigma).mean() + kbb = rbf_kernel(b, b, sigma).mean() + kab = rbf_kernel(a, b, sigma).mean() + return float(kaa + kbb - 2.0 * kab) + + +def build_prompt_samples( + runs: list[RunRecord], + prompts: list[str], + cache: dict[str, np.ndarray], + features: int, +) -> dict[str, dict[str, np.ndarray]]: + samples: dict[str, dict[str, np.ndarray]] = {} + for run in runs: + prompt_to_text = {prompt: response for prompt, response in zip(run.prompts, run.responses)} + ordered_texts = [prompt_to_text[prompt] for prompt in prompts] + embeddings = load_embeddings(ordered_texts, cache, features=features) + samples[run.path.name] = {prompt: embeddings[idx] for idx, prompt in enumerate(prompts)} + return samples + + +def gather_prompt_sample_sets( + run_group: list[RunRecord], + prompts: list[str], + cache: dict[str, np.ndarray], + features: int, +) -> dict[str, dict[str, np.ndarray]]: + grouped = build_prompt_samples(run_group, prompts, cache, features) + by_model: dict[str, dict[str, list[np.ndarray]]] = {} + for run in run_group: + prompt_embeddings = grouped[run.path.name] + model_bucket = by_model.setdefault(run.model_name, {prompt: [] for prompt in prompts}) + for prompt in prompts: + model_bucket[prompt].append(prompt_embeddings[prompt]) + return { + model: {prompt: np.stack(samples, axis=0).astype(np.float32) for prompt, samples in prompt_map.items()} + for model, prompt_map in by_model.items() + } + + +def build_point_vectors(sample_sets: dict[str, dict[str, np.ndarray]], prompts: list[str], mode: str) -> dict[str, np.ndarray]: + vectors: dict[str, np.ndarray] = {} + for model, prompt_map in sample_sets.items(): + per_prompt: list[np.ndarray] = [] + for prompt in prompts: + samples = prompt_map[prompt] + if mode == "single": + per_prompt.append(samples[0]) + elif mode == "mean": + per_prompt.append(np.mean(samples, axis=0)) + else: + raise ValueError(f"Unknown point mode: {mode}") + vectors[model] = np.concatenate(per_prompt, axis=0) + return vectors + + +def prompt_distance_matrix( + ref_sets: dict[str, dict[str, np.ndarray]], + qry_sets: dict[str, dict[str, np.ndarray]], + prompts: list[str], + method: str, + sigma: float, +) -> tuple[list[str], np.ndarray]: + models = sorted(set(ref_sets) & set(qry_sets)) + matrix = np.zeros((len(models), len(models)), dtype=np.float32) + for row, qry_model in enumerate(models): + for col, ref_model in enumerate(models): + prompt_values = [] + for prompt in prompts: + ref_samples = ref_sets[ref_model][prompt] + qry_samples = qry_sets[qry_model][prompt] + if method == "mmd": + prompt_values.append(exact_mmd2(qry_samples, ref_samples, sigma=sigma)) + else: + raise ValueError(f"Unsupported prompt method: {method}") + matrix[row, col] = float(np.mean(prompt_values)) + return models, matrix + + +def retrieval_metrics(labels: list[str], matrix: np.ndarray, bootstrap_samples: int = 2000, seed: int = 42) -> dict[str, float]: + preds = list(np.argmin(matrix, axis=1)) + y_true = labels + y_pred = [labels[idx] for idx in preds] + correct = [t == p for t, p in zip(y_true, y_pred)] + ranks = [] + for row_idx, label in enumerate(labels): + order = list(np.argsort(matrix[row_idx])) + ranks.append(order.index(row_idx) + 1) + + top_hits = {1: 0, 3: 0, 5: 0} + for rank in ranks: + for k in top_hits: + if rank <= k: + top_hits[k] += 1 + + rng = np.random.default_rng(seed) + + def interval(values: list[float]) -> tuple[float, float]: + array = np.asarray(values, dtype=np.float64) + if len(array) <= 1 or bootstrap_samples <= 0: + value = float(np.mean(array)) if len(array) else float("nan") + return value, value + means = np.mean(array[rng.integers(0, len(array), size=(bootstrap_samples, len(array)))], axis=1) + return tuple(float(value) for value in np.quantile(means, [0.025, 0.975])) + + top1_low, top1_high = interval([float(value) for value in correct]) + mrr_low, mrr_high = interval([1.0 / rank for rank in ranks]) + return { + "model_count": len(labels), + "top1": float(np.mean(correct)) if correct else 0.0, + "top3": top_hits[3] / len(labels) if labels else 0.0, + "top5": top_hits[5] / len(labels) if labels else 0.0, + "mrr": float(np.mean([1.0 / rank for rank in ranks])) if ranks else 0.0, + "top1_ci95_low": top1_low, + "top1_ci95_high": top1_high, + "mrr_ci95_low": mrr_low, + "mrr_ci95_high": mrr_high, + "mean_self_distance": float(np.mean([matrix[i, i] for i in range(len(labels))])) if labels else float("nan"), + "mean_nearest_distance": float(np.mean([np.min(matrix[i]) for i in range(len(labels))])) if labels else float("nan"), + } + + +def evaluate_comparison( + name: str, + reference_runs: list[RunRecord], + query_runs: list[RunRecord], + prompts: list[str], + cache: dict[str, np.ndarray], + features: int, + sigma: float, +) -> list[dict[str, object]]: + ref_sets = gather_prompt_sample_sets(reference_runs, prompts, cache, features) + qry_sets = gather_prompt_sample_sets(query_runs, prompts, cache, features) + labels = sorted(set(ref_sets) & set(qry_sets)) + ref_sets = {label: ref_sets[label] for label in labels} + qry_sets = {label: qry_sets[label] for label in labels} + + rows: list[dict[str, object]] = [] + + single_ref = build_point_vectors(ref_sets, prompts, mode="single") + single_qry = build_point_vectors(qry_sets, prompts, mode="single") + mean_ref = build_point_vectors(ref_sets, prompts, mode="mean") + mean_qry = build_point_vectors(qry_sets, prompts, mode="mean") + + def eval_point(method: str, ref_vectors: dict[str, np.ndarray], qry_vectors: dict[str, np.ndarray]) -> dict[str, object]: + ordered = sorted(ref_vectors) + ref_matrix = np.stack([ref_vectors[label] for label in ordered], axis=0) + qry_matrix = np.stack([qry_vectors[label] for label in ordered], axis=0) + matrix = cosine_distance_matrix(qry_matrix, ref_matrix) + metrics = retrieval_metrics(ordered, matrix) + return { + "comparison": name, + "method": method, + "sigma": sigma, + "prompt_count": len(prompts), + **metrics, + } + + rows.append(eval_point("single_cosine", single_ref, single_qry)) + rows.append(eval_point("mean_cosine", mean_ref, mean_qry)) + + mmd_labels, mmd_matrix = prompt_distance_matrix(ref_sets, qry_sets, prompts, method="mmd", sigma=sigma) + rows.append( + { + "comparison": name, + "method": "exact_rbf_mmd", + "sigma": sigma, + "prompt_count": len(prompts), + **retrieval_metrics(mmd_labels, mmd_matrix), + } + ) + return rows + + +def write_csv(path: Path, rows: list[dict[str, object]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + fields: list[str] = [] + for row in rows: + for key in row: + if key not in fields: + fields.append(key) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Budget-matched exact RBF-MMD vs cosine retrieval experiment.") + parser.add_argument("--data-dir", type=Path, default=Path("out/rand_chinese")) + parser.add_argument("--output-dir", type=Path, default=Path("out/rbf_mmd_pilot")) + parser.add_argument("--reference-setting", type=parse_setting, default=parse_setting("0.2:0.8")) + parser.add_argument("--comparison-setting", type=parse_setting, action="append", default=[parse_setting("0.2:0.8"), parse_setting("0.3:0.8"), parse_setting("0.3:0.9")]) + parser.add_argument("--model-limit", type=int, default=0, help="Maximum common models; 0 uses all.") + parser.add_argument("--minimum-models", type=int, default=100, help="Fail below this common-cohort size.") + parser.add_argument("--prompt-limit", type=int, default=0, help="Maximum common prompts; 0 uses all.") + parser.add_argument("--calibration-ratio", type=float, default=0.2) + parser.add_argument("--embedding-features", type=int, default=4096) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--report-file", type=Path, default=Path("docs/rbf_mmd_pilot.md")) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + np.random.seed(args.seed) + + selected_settings = [args.reference_setting] + [setting for setting in args.comparison_setting if setting != args.reference_setting] + records = load_run_records(args.data_dir, set(selected_settings)) + selected_models = select_models(records, selected_settings, args.model_limit) + if not selected_models: + raise SystemExit("No models satisfy the requested settings and repeat coverage.") + if len(selected_models) < args.minimum_models: + raise SystemExit( + f"Only {len(selected_models)} models have the required repeat coverage; " + f"minimum is {args.minimum_models}. Use --minimum-models only to label an explicit pilot." + ) + + prompts = select_prompts(records, selected_models, selected_settings, args.prompt_limit) + if not prompts: + raise SystemExit("No common prompts found for the selected models/settings.") + + calibration_count = max(1, int(round(len(prompts) * args.calibration_ratio))) + calibration_prompts = prompts[:calibration_count] + eval_prompts = prompts[calibration_count:] or prompts + + cache: dict[str, np.ndarray] = {} + all_rows: list[dict[str, object]] = [] + comparison_notes: list[str] = [] + + for setting in selected_settings: + per_model = {} + for model_name in selected_models: + run_records = [record for (model, code, _repeat), record in records.items() if model == model_name and code == setting.code] + run_records = sorted(run_records, key=lambda record: record.repeat) + if setting == args.reference_setting: + ref_runs, qry_runs = chunk_runs_for_same_setting(run_records) + comparison_name = f"same_{setting.code}" + else: + ref_runs = [record for (model, code, _repeat), record in records.items() if model == model_name and code == args.reference_setting.code] + ref_runs = sorted(ref_runs, key=lambda record: record.repeat) + qry_runs = run_records + comparison_name = f"cross_{args.reference_setting.code}_to_{setting.code}" + per_model[model_name] = (ref_runs, qry_runs) + + ref_for_sigma = [ + record + for (model, code, _repeat), record in records.items() + if code == args.reference_setting.code and model in selected_models + ] + ref_for_sigma = sorted(ref_for_sigma, key=lambda record: (record.model_name, record.repeat)) + if not ref_for_sigma: + raise SystemExit("Unable to find runs for bandwidth calibration.") + + # Estimate bandwidth on calibration prompts using the reference setting only. + calibration_sets = gather_prompt_sample_sets(ref_for_sigma, calibration_prompts, cache, args.embedding_features) + calibration_embeddings = [] + for model in selected_models: + if model not in calibration_sets: + continue + for prompt in calibration_prompts: + calibration_embeddings.extend(list(calibration_sets[model][prompt])) + sigma = median_bandwidth(np.stack(calibration_embeddings, axis=0)) + + for model_name, (ref_runs, qry_runs) in per_model.items(): + if not ref_runs or not qry_runs: + comparison_notes.append(f"skip {model_name} {setting.code} due to insufficient repeats") + + if setting == args.reference_setting: + ref_runs_all = [record for (model, code, _repeat), record in records.items() if code == setting.code and model in selected_models] + ref_runs_all = sorted(ref_runs_all, key=lambda record: (record.model_name, record.repeat)) + ref_runs, qry_runs = chunk_runs_for_same_setting(ref_runs_all) + comparison_name = f"same_{setting.code}" + all_rows.extend(evaluate_comparison(comparison_name, ref_runs, qry_runs, eval_prompts, cache, args.embedding_features, sigma)) + else: + ref_runs = [record for (model, code, _repeat), record in records.items() if code == args.reference_setting.code and model in selected_models] + qry_runs = [record for (model, code, _repeat), record in records.items() if code == setting.code and model in selected_models] + ref_runs = sorted(ref_runs, key=lambda record: (record.model_name, record.repeat)) + qry_runs = sorted(qry_runs, key=lambda record: (record.model_name, record.repeat)) + comparison_name = f"cross_{args.reference_setting.code}_to_{setting.code}" + all_rows.extend(evaluate_comparison(comparison_name, ref_runs, qry_runs, eval_prompts, cache, args.embedding_features, sigma)) + + args.output_dir.mkdir(parents=True, exist_ok=True) + summary_csv = args.output_dir / "summary.csv" + summary_json = args.output_dir / "summary.json" + models_file = args.output_dir / "selected_models.txt" + prompts_file = args.output_dir / "selected_prompts.txt" + report_path = args.report_file + + write_csv(summary_csv, all_rows) + summary_json.write_text(json.dumps(all_rows, indent=2, ensure_ascii=False), encoding="utf-8") + models_file.write_text("\n".join(selected_models) + "\n", encoding="utf-8") + prompts_file.write_text("\n".join(prompts) + "\n", encoding="utf-8") + + report_path.parent.mkdir(parents=True, exist_ok=True) + lines = [ + "# RBF-MMD Pilot", + "", + "## Setup", + f"- Data dir: `{args.data_dir}`", + f"- Reference setting: `{format_setting(args.reference_setting)}`", + f"- Comparison settings: {', '.join(f'`{format_setting(s)}`' for s in selected_settings)}", + f"- Selected models: `{len(selected_models)}`", + f"- Selected prompts: `{len(prompts)}`", + f"- Calibration prompts: `{len(calibration_prompts)}`", + f"- Evaluation prompts: `{len(eval_prompts)}`", + f"- Embedding features: `{args.embedding_features}`", + "", + "## Notes", + "- `single_cosine` uses one response sample per prompt from the selected run split.", + "- `mean_cosine` averages repeated samples per prompt before concatenation.", + "- `exact_rbf_mmd` computes prompt-wise squared RBF-MMD and averages across prompts.", + "- `same_*` compares two repeat splits from the same decoding setting.", + "- `cross_*` compares the reference setting against another setting.", + "- NA in intermediate figures means the model or prompt was not present in the required split, or the run lacked enough repeats.", + "", + "## Results", + ] + for row in all_rows: + lines.append( + f"- `{row['comparison']}` / `{row['method']}`: top1={row['top1']:.3f}, top3={row['top3']:.3f}, top5={row['top5']:.3f}, mrr={row['mrr']:.3f}" + ) + if comparison_notes: + lines.extend(["", "## Skips"] + [f"- {note}" for note in comparison_notes]) + report_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + print( + json.dumps( + { + "summary_csv": str(summary_csv), + "summary_json": str(summary_json), + "models_file": str(models_file), + "prompts_file": str(prompts_file), + "report": str(report_path), + "selected_models": selected_models, + "selected_prompts": prompts, + "rows": all_rows, + }, + indent=2, + ensure_ascii=False, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_top_p_control_t05.sh b/scripts/run_top_p_control_t05.sh new file mode 100755 index 0000000..6bda1f4 --- /dev/null +++ b/scripts/run_top_p_control_t05.sh @@ -0,0 +1,46 @@ +#!/usr/bin/env bash +# Controlled top-p experiment: hold temperature and every other pipeline option +# fixed while sweeping top_p from 0.0 through 1.0. Two repeats quantify sampling +# variation. All outputs, status files, manifests, and logs are isolated. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +EXPERIMENT_ROOT="${EXPERIMENT_ROOT:-out/top_p_control_t05_validated100_20260719}" + +export OUTPUT_DIR="$EXPERIMENT_ROOT" +export LOG_DIR="${LOG_DIR:-$EXPERIMENT_ROOT/logs}" +export MANIFEST="${MANIFEST:-$LOG_DIR/sweep_manifest.tsv}" +export SWEEP_NAME="${SWEEP_NAME:-top_p_control_t05}" +export HF_JSONL="${HF_JSONL:-configs/rand_chinese_0p3b_7b_rerun.jsonl}" +export PROVIDERS="huggingface" +export DATASET="rand_chinese" + +# Experimental controls. +export TEMPERATURES="0.5" +# top_p=0.0 and 0.1 outputs are retained from earlier runs. Continue the +# controlled sweep at 0.2; incomplete/failed models will be backfilled later. +export TOP_PS="0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0" +export REPEATS="${REPEATS:-1 2}" +export INCLUDE_T00="0" +export LIMIT="0" +export RESUME_MODE="all" + +# Restrict this user's experiment to the currently free cards. The scheduler +# still waits if another process begins using one of these GPUs. +export GPUS="${GPUS:-}" +export MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-10}" +export MIN_GPU_FREE_GB="${MIN_GPU_FREE_GB:-4.0}" +export GPU_POLL_SECONDS="${GPU_POLL_SECONDS:-15.0}" + +# Resilient completion settings: four attempts total for transient failures. +export MODEL_TIMEOUT_SECONDS="${MODEL_TIMEOUT_SECONDS:-43200}" +export MAX_RETRIES="${MAX_RETRIES:-3}" +export OOM_RETRIES="${OOM_RETRIES:-3}" +export RETRY_DELAY_SECONDS="${RETRY_DELAY_SECONDS:-300}" +export TRY_VLLM="${TRY_VLLM:-1}" +export CACHE_EVICT_FINISHED="${CACHE_EVICT_FINISHED:-success}" + +exec bash scripts/run_dna_temperature_top_p_sweep.sh diff --git a/scripts/visualize_dna_rerun_heatmap.py b/scripts/visualize_dna_rerun_heatmap.py new file mode 100644 index 0000000..72db4f6 --- /dev/null +++ b/scripts/visualize_dna_rerun_heatmap.py @@ -0,0 +1,362 @@ +#!/usr/bin/env python3 +"""Visualize base-vs-rerun DNA distances as a heatmap. + +The heatmap rows are rerun DNAs and columns are original/base DNAs. The diagonal +cell is the distance between the same model's original DNA and rerun DNA. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +import numpy as np +from PIL import Image, ImageDraw, ImageFont + + +@dataclass(frozen=True) +class DnaRecord: + model_name: str + safe_name: str + path: Path + vector: np.ndarray + params_b: float | None + + +def safe_model_name(model_name: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", model_name.strip("/")) + + +def load_font(size: int) -> ImageFont.ImageFont: + candidates = [ + "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", + "/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf", + ] + for path in candidates: + if Path(path).exists(): + return ImageFont.truetype(path, size=size) + return ImageFont.load_default() + + +def load_dna(path: Path) -> DnaRecord: + payload = json.loads(path.read_text(encoding="utf-8")) + metadata = payload.get("metadata", {}) + model_name = metadata.get("model_name") or path.parent.name + model_meta = metadata.get("model_metadata", {}) + params_b = model_meta.get("size", {}).get("parameter_count_billions") + try: + params_b = float(params_b) if params_b is not None else None + except (TypeError, ValueError): + params_b = None + vector = np.asarray(payload["signature"], dtype=np.float32) + return DnaRecord( + model_name=str(model_name), + safe_name=safe_model_name(str(model_name)), + path=path, + vector=vector, + params_b=params_b, + ) + + +def find_rerun_records(dataset_dir: Path, suffix: str) -> list[DnaRecord]: + records: list[DnaRecord] = [] + for model_dir in sorted(dataset_dir.glob(f"*{suffix}")): + if not model_dir.is_dir(): + continue + dna_files = sorted(model_dir.glob("*_dna.json")) + if not dna_files: + continue + records.append(load_dna(dna_files[0])) + return records + + +def find_base_record(dataset_dir: Path, rerun: DnaRecord, suffix: str) -> DnaRecord | None: + if not rerun.path.parent.name.endswith(suffix): + return None + base_dir_name = rerun.path.parent.name[: -len(suffix)] + base_dir = dataset_dir / base_dir_name + dna_files = sorted(base_dir.glob("*_dna.json")) + if not dna_files: + return None + return load_dna(dna_files[0]) + + +def cosine_distance(a: np.ndarray, b: np.ndarray) -> float: + denom = float(np.linalg.norm(a) * np.linalg.norm(b)) + if denom == 0.0: + return 1.0 + return float(1.0 - np.dot(a, b) / denom) + + +def euclidean_distance(a: np.ndarray, b: np.ndarray) -> float: + return float(np.linalg.norm(a - b)) + + +def pair_records(dataset_dir: Path, suffix: str) -> tuple[list[DnaRecord], list[DnaRecord]]: + reruns = find_rerun_records(dataset_dir, suffix) + pairs: list[tuple[DnaRecord, DnaRecord]] = [] + for rerun in reruns: + base = find_base_record(dataset_dir, rerun, suffix) + if base is None: + continue + if base.vector.shape != rerun.vector.shape: + continue + pairs.append((base, rerun)) + + pairs.sort(key=lambda pair: (math.inf if pair[0].params_b is None else pair[0].params_b, pair[0].model_name.lower())) + bases = [base for base, _ in pairs] + paired_reruns = [rerun for _, rerun in pairs] + return bases, paired_reruns + + +def compute_distance_matrix( + bases: list[DnaRecord], + reruns: list[DnaRecord], + metric: str, +) -> np.ndarray: + fn = cosine_distance if metric == "cosine" else euclidean_distance + matrix = np.zeros((len(reruns), len(bases)), dtype=np.float32) + for row, rerun in enumerate(reruns): + for col, base in enumerate(bases): + matrix[row, col] = fn(rerun.vector, base.vector) + return matrix + + +def short_label(name: str, max_len: int = 28) -> str: + label = name.replace("/", "/\n", 1) + if len(label) <= max_len: + return label + return label[: max_len - 1] + "…" + + +def color_for_value(value: float, vmin: float, vmax: float) -> tuple[int, int, int]: + if not np.isfinite(value): + return (220, 220, 220) + t = 0.0 if vmax <= vmin else (value - vmin) / (vmax - vmin) + t = max(0.0, min(1.0, t)) + # Low distance: deep blue. High distance: warm yellow/red. + stops = [ + (0.00, (36, 74, 143)), + (0.35, (70, 150, 190)), + (0.60, (247, 247, 190)), + (0.80, (245, 150, 85)), + (1.00, (165, 0, 38)), + ] + for idx in range(1, len(stops)): + left_t, left_c = stops[idx - 1] + right_t, right_c = stops[idx] + if t <= right_t: + local = (t - left_t) / (right_t - left_t) + return tuple(int(left_c[i] + local * (right_c[i] - left_c[i])) for i in range(3)) + return stops[-1][1] + + +def draw_rotated_text(base: Image.Image, xy: tuple[int, int], text: str, font: ImageFont.ImageFont, fill: tuple[int, int, int]) -> None: + lines = text.splitlines() + line_heights = [font.getbbox(line)[3] - font.getbbox(line)[1] for line in lines] + width = max(font.getlength(line) for line in lines) if lines else 1 + height = sum(line_heights) + max(len(lines) - 1, 0) * 2 + patch = Image.new("RGBA", (int(width) + 8, int(height) + 8), (255, 255, 255, 0)) + draw = ImageDraw.Draw(patch) + y = 4 + for line, line_height in zip(lines, line_heights): + draw.text((4, y), line, font=font, fill=fill) + y += line_height + 2 + rotated = patch.rotate(60, expand=True) + base.alpha_composite(rotated, xy) + + +def draw_heatmap( + matrix: np.ndarray, + labels: list[str], + output_path: Path, + title: str, + metric: str, + annotate: bool, +) -> None: + n = len(labels) + cell = max(18, min(34, 800 // max(n, 1))) + left = 300 + top = 260 + right = 120 + bottom = 120 + width = left + n * cell + right + height = top + n * cell + bottom + + image = Image.new("RGBA", (width, height), (255, 255, 255, 255)) + draw = ImageDraw.Draw(image) + title_font = load_font(22) + label_font = load_font(10 if n > 30 else 12) + small_font = load_font(9) + + finite = matrix[np.isfinite(matrix)] + vmin = float(np.min(finite)) if finite.size else 0.0 + vmax = float(np.percentile(finite, 95)) if finite.size else 1.0 + vmax = max(vmax, vmin + 1e-8) + + draw.text((24, 24), title, font=title_font, fill=(25, 25, 25)) + draw.text((24, 58), f"Rows: rerun DNA. Columns: original DNA. Metric: {metric}. Color clipped at p95={vmax:.4g}.", font=small_font, fill=(70, 70, 70)) + + for row in range(n): + for col in range(n): + value = float(matrix[row, col]) + x0 = left + col * cell + y0 = top + row * cell + fill = color_for_value(value, vmin, vmax) + draw.rectangle((x0, y0, x0 + cell, y0 + cell), fill=fill) + if row == col: + draw.rectangle((x0, y0, x0 + cell, y0 + cell), outline=(0, 0, 0), width=2) + if annotate and cell >= 24: + text = f"{value:.2f}" if metric == "cosine" else f"{value:.1f}" + text_fill = (255, 255, 255) if sum(fill) < 330 else (20, 20, 20) + bbox = draw.textbbox((0, 0), text, font=small_font) + draw.text((x0 + (cell - (bbox[2] - bbox[0])) / 2, y0 + (cell - (bbox[3] - bbox[1])) / 2), text, font=small_font, fill=text_fill) + + for idx, label in enumerate(labels): + y = top + idx * cell + cell / 2 + draw.text((left - 8, y), short_label(label), anchor="rm", font=label_font, fill=(20, 20, 20)) + draw_rotated_text(image, (left + idx * cell - 8, top - 160), short_label(label), label_font, (20, 20, 20)) + + # Color bar. + bar_x = left + n * cell + 30 + bar_y = top + bar_h = n * cell + for i in range(bar_h): + value = vmin + (vmax - vmin) * (i / max(bar_h - 1, 1)) + draw.line((bar_x, bar_y + i, bar_x + 18, bar_y + i), fill=color_for_value(value, vmin, vmax)) + draw.rectangle((bar_x, bar_y, bar_x + 18, bar_y + bar_h), outline=(40, 40, 40)) + draw.text((bar_x + 26, bar_y - 4), f"{vmin:.3g}", font=small_font, fill=(40, 40, 40)) + draw.text((bar_x + 26, bar_y + bar_h - 8), f"{vmax:.3g}", font=small_font, fill=(40, 40, 40)) + + output_path.parent.mkdir(parents=True, exist_ok=True) + image.convert("RGB").save(output_path) + + +def write_matrix_csv(path: Path, matrix: np.ndarray, labels: list[str]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.writer(handle) + writer.writerow(["rerun\\base", *labels]) + for label, row in zip(labels, matrix): + writer.writerow([label, *[f"{float(value):.8g}" for value in row]]) + + +def write_predictions(path: Path, matrix: np.ndarray, labels: list[str]) -> dict[str, float | int]: + true = labels + pred = [labels[int(np.argmin(row))] for row in matrix] + nearest_distance = [float(np.min(row)) for row in matrix] + self_distance = [float(row[idx]) for idx, row in enumerate(matrix)] + + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter( + handle, + fieldnames=["true_model", "predicted_model", "correct", "self_distance", "nearest_distance", "nearest_rank"], + ) + writer.writeheader() + for idx, label in enumerate(labels): + order = list(np.argsort(matrix[idx])) + writer.writerow( + { + "true_model": label, + "predicted_model": pred[idx], + "correct": pred[idx] == label, + "self_distance": f"{self_distance[idx]:.8g}", + "nearest_distance": f"{nearest_distance[idx]:.8g}", + "nearest_rank": order.index(idx) + 1, + } + ) + + correct = sum(t == p for t, p in zip(true, pred)) + per_class_precision: list[float] = [] + per_class_recall: list[float] = [] + per_class_f1: list[float] = [] + for label in labels: + tp = sum(t == label and p == label for t, p in zip(true, pred)) + fp = sum(t != label and p == label for t, p in zip(true, pred)) + fn = sum(t == label and p != label for t, p in zip(true, pred)) + precision = 0.0 if tp + fp == 0 else tp / (tp + fp) + recall = 0.0 if tp + fn == 0 else tp / (tp + fn) + f1 = 0.0 if precision + recall == 0.0 else 2 * precision * recall / (precision + recall) + per_class_precision.append(float(precision)) + per_class_recall.append(float(recall)) + per_class_f1.append(float(f1)) + + accuracy = correct / len(labels) if labels else 0.0 + return { + "model_count": len(labels), + "accuracy": float(accuracy), + "precision_macro": float(np.mean(per_class_precision)), + "recall_macro": float(np.mean(per_class_recall)), + "f1_macro": float(np.mean(per_class_f1)), + "precision_micro": float(accuracy), + "recall_micro": float(accuracy), + "f1_micro": float(accuracy), + "mean_self_distance": float(np.mean(self_distance)), + "median_self_distance": float(np.median(self_distance)), + "mean_nearest_distance": float(np.mean(nearest_distance)), + "self_is_nearest_count": int(correct), + } + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Draw a base-vs-rerun DNA distance heatmap.") + parser.add_argument("--dataset-dir", type=Path, default=Path("out/rand_chinese")) + parser.add_argument("--suffix", type=str, default="_temp07_run") + parser.add_argument("--metric", choices=["cosine", "euclidean"], default="cosine") + parser.add_argument("--output-dir", type=Path, default=Path("out/dna_rerun_heatmaps")) + parser.add_argument("--prefix", type=str, default=None) + parser.add_argument("--annotate", action="store_true", help="Draw numeric distances inside cells when the heatmap is small enough.") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + bases, reruns = pair_records(args.dataset_dir, args.suffix) + if not bases: + raise SystemExit(f"No paired base/rerun DNA files found under {args.dataset_dir} for suffix {args.suffix!r}.") + + labels = [record.model_name for record in bases] + matrix = compute_distance_matrix(bases, reruns, args.metric) + prefix = args.prefix or f"{args.dataset_dir.name}{args.suffix}_{args.metric}" + + heatmap_path = args.output_dir / f"{prefix}_heatmap.png" + matrix_path = args.output_dir / f"{prefix}_matrix.csv" + predictions_path = args.output_dir / f"{prefix}_nearest_predictions.csv" + metrics_path = args.output_dir / f"{prefix}_metrics.json" + + write_matrix_csv(matrix_path, matrix, labels) + metrics = write_predictions(predictions_path, matrix, labels) + metrics.update( + { + "dataset_dir": str(args.dataset_dir), + "suffix": args.suffix, + "metric": args.metric, + "heatmap_path": str(heatmap_path), + "matrix_path": str(matrix_path), + "predictions_path": str(predictions_path), + } + ) + metrics_path.write_text(json.dumps(metrics, indent=2, ensure_ascii=False), encoding="utf-8") + + draw_heatmap( + matrix=matrix, + labels=labels, + output_path=heatmap_path, + title=f"DNA distance heatmap: base vs rerun ({args.suffix})", + metric=args.metric, + annotate=args.annotate, + ) + + print(json.dumps(metrics, indent=2, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) From fcd35f696f3e025796edb0aadd64f1df8e5680ef Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Fri, 24 Jul 2026 16:24:05 +0800 Subject: [PATCH 18/28] Add DNA experiment documentation and demo figures --- docs/SVM_summary.tex | 225 ++++ .../bounded_paired_gallery.png | Bin 0 -> 75856 bytes .../bounded_paired_gallery.svg | 1 + .../exact_top1_heatmaps.png | Bin 0 -> 70918 bytes .../exact_top1_heatmaps.svg | 1 + .../verification_tree_diagnostics.png | Bin 0 -> 67841 bytes .../verification_tree_diagnostics.svg | 1 + docs/primary_robustness_experiment.tex | 1000 +++++++++++++++++ docs/rbf_mmd_pilot.md | 34 + docs/revised_experiment_protocol.md | 96 ++ docs/rfftrace_progress.tex | 103 ++ docs/svm_modeltree_experiment.tex | 325 ++++++ docs/svm_modeltree_experiment_zh.html | 121 ++ docs/svm_modeltree_experiment_zh.tex | 131 +++ 14 files changed, 2038 insertions(+) create mode 100644 docs/SVM_summary.tex create mode 100644 docs/figures/small_scale_section_6_11/bounded_paired_gallery.png create mode 100644 docs/figures/small_scale_section_6_11/bounded_paired_gallery.svg create mode 100644 docs/figures/small_scale_section_6_11/exact_top1_heatmaps.png create mode 100644 docs/figures/small_scale_section_6_11/exact_top1_heatmaps.svg create mode 100644 docs/figures/small_scale_section_6_11/verification_tree_diagnostics.png create mode 100644 docs/figures/small_scale_section_6_11/verification_tree_diagnostics.svg create mode 100644 docs/primary_robustness_experiment.tex create mode 100644 docs/rbf_mmd_pilot.md create mode 100644 docs/revised_experiment_protocol.md create mode 100644 docs/rfftrace_progress.tex create mode 100644 docs/svm_modeltree_experiment.tex create mode 100644 docs/svm_modeltree_experiment_zh.html create mode 100644 docs/svm_modeltree_experiment_zh.tex diff --git a/docs/SVM_summary.tex b/docs/SVM_summary.tex new file mode 100644 index 0000000..bb2db6f --- /dev/null +++ b/docs/SVM_summary.tex @@ -0,0 +1,225 @@ +\documentclass[11pt,a4paper]{article} +\usepackage{graphicx} +\usepackage{booktabs} +\usepackage{amsmath} +\usepackage{hyperref} +\usepackage{caption} +\usepackage{subcaption} +\usepackage{listings} +\usepackage{geometry} +\geometry{margin=1in} +\title{SVM Experiments with Hugging Face Model-Tree Relations} +\author{} +\date{} + +\begin{document} +\maketitle + +\begin{abstract} +Concise summary of the pipeline: export Hugging Face model-tree relations, extract features from cached DNA outputs, train a linear SVM probe to detect model-family signals, and evaluate with held-out test sets and cross-validation. This document summarizes implementation details, experimental design, train/test splitting strategy, and includes placeholder figures and tables for presentation. +\end{abstract} + +\section{Overview} +The workflow has three main parts: +\begin{enumerate} + \item Export model relations from Hub model-card metadata to form related-model groups. + \item Extract feature vectors from precomputed DNA outputs for each model/run. + \item Train and evaluate an SVM classifier that predicts membership in a related-model group. +\end{enumerate} + +\section{Exporting the Hugging Face Model Tree} +Implementation summary: +\begin{itemize} + \item For each model id in the input list, fetch repository metadata via the Hub API. + \item Parse fields and card metadata for base-model relationships and config fields that indicate parent/base models. + \item Add each model as a node, add an undirected edge for each discovered base relation (A--B). + \item Collapse nodes into connected components to produce related-model groups. +\end{itemize} + +Algorithmic detail (union--find streaming style): +\begin{lstlisting}[basicstyle=\ttfamily\small] +for model in input_models: + info = hf_api.model_info(model) + for base in extract_base_models(info): + union_find.add(model) + union_find.add(base) + union_find.union(model, base) +components = collect_connected_components(union_find) +write_jsonl(groups) +\end{lstlisting} + +Why union--find: +\begin{itemize} + \item Supports streaming discovery of edges without constructing the full adjacency list first. + \item Near-constant amortized cost per union/find. + \item Low memory overhead and simple to implement. +\end{itemize} + +Output: +\begin{itemize} + \item JSONL file where each line is an object with a ``models'' array representing a related group. + \item A summary JSON with counts (model\_count, node\_count, edge\_count, component\_count, skipped\_models). +\end{itemize} + +\section{Feature Extraction and SVM Implementation} +Feature sources: +\begin{itemize} + \item Per-model cached DNA outputs (e.g., response vectors, log-prob vectors, per-probe scores). + \item Aggregated statistics across probes: mean response, variance, top-$k$ token agreement, entropy, and any hand-engineered distances to canonical outputs. + \item Optionally, metadata features (model size bucket, family label, tokenizer type) if available. +\end{itemize} + +Feature pipeline (recommended): +\begin{enumerate} + \item Load cached outputs and align probes across models and runs to a common order. + \item Compute numeric features per model or per (model, run) sample. + \item Apply standard scaling (zero mean, unit variance) fit on training set; persist scaler. + \item Optionally apply PCA to reduce dimensionality (retain e.g., 95\% variance). +\end{enumerate} + +SVM details: +\begin{itemize} + \item Use a linear SVM (e.g., scikit-learn's \texttt{LinearSVC} or \texttt{SGDClassifier} with hinge loss) for interpretability and speed on high-dimensional features. + \item Regularization: tune penalty $C$ over a small grid (e.g., $[0.01,0.1,1,10]$). + \item Use class weighting or balanced sampling if classes are imbalanced. + \item For probabilistic outputs, wrap the SVM with Platt scaling (e.g., \texttt{CalibratedClassifierCV}) if needed for ROC/PR curves. +\end{itemize} + +Minimal training loop (sklearn-style): +\begin{lstlisting}[basicstyle=\ttfamily\small] +from sklearn.preprocessing import StandardScaler +from sklearn.svm import LinearSVC +scaler = StandardScaler().fit(X_train) +Xtr = scaler.transform(X_train) +clf = LinearSVC(C=1.0, max_iter=10000).fit(Xtr, y_train) +\end{lstlisting} + +\section{Experiment Outline} +Goals: +\begin{itemize} + \item Evaluate whether DNA outputs contain signals that correlate with model-tree relations. + \item Quantify detection performance (per-group and overall). + \item Analyze robustness across sampling settings (temperature/top-p). +\end{itemize} + +Experimental arms: +\begin{itemize} + \item Baseline: random classifier or simple logistic regression. + \item Linear SVM probe on raw features. + \item Linear SVM + PCA dimensionality reduction. + \item Cross-run generalization tests: train on one sampling regime, test on another. +\end{itemize} + +Metrics: +\begin{itemize} + \item Accuracy, precision, recall, F1 (micro and macro), ROC AUC, PR AUC. + \item Confusion matrices aggregated at group-level. +\end{itemize} + +\section{Train / Test Split Strategy} +Recommended approaches: +\begin{enumerate} + \item \textbf{Group-aware holdout (preferred)}: Split by related-model group so that all models from some groups are only in train and other groups only in test. This evaluates generalization to unseen model families. + \item \textbf{Model-level stratified split}: If labels are binary per-model (e.g., in-group vs out-group for a particular relation), stratify splits by label while ensuring models from the same group are not split across train/test to avoid leakage. + \item \textbf{K-fold cross-validation with grouping}: Use GroupKFold where the group id is the connected-component id. This yields k folds where groups are exclusive per fold. + \item \textbf{Per-run generalization}: Train on outputs from a subset of sampling settings (e.g., temperature=0.2) and test on others (temperature=0.3, different top-p). +\end{enumerate} + +Practical splits: +\begin{itemize} + \item Typical split sizes: 70\% train groups / 15\% val groups / 15\% test groups. + \item For small group counts, prefer leave-one-group-out or repeated GroupKFold. + \item When doing per-sample classification (many samples per model), ensure group-wise partitioning to prevent sample-level leakage. +\end{itemize} + +\section{Experiment Details (Suggested Configuration)} +\begin{itemize} + \item Input models: the full model list (N $\approx$ 11k). Optionally pre-filter by availability or size bucket. + \item Feature vector dimensionality: depends on probes; example 512--4096. Use PCA if >1024. + \item Classifier: LinearSVC with grid search on $C \in \{0.01,0.1,1,10\}$. + \item Validation: GroupKFold with $k=5$ (groups = connected components). + \item Repeat experiments with caching and with incremental checkpointing to measure wall time. + \item Logging: store metrics per-fold, per-group summary, and top-k misclassified models for manual inspection. +\end{itemize} + +\section{Figures and Tables (placeholders)} +\subsection{Figure suggestions} +\begin{figure}[ht] + \centering + \begin{subfigure}{0.48\textwidth} + \centering + \includegraphics[width=\linewidth]{figures/roc_curve_placeholder.pdf} + \caption{ROC curve (SVM vs baseline)} + \end{subfigure} + \hfill + \begin{subfigure}{0.48\textwidth} + \centering + \includegraphics[width=\linewidth]{figures/pr_curve_placeholder.pdf} + \caption{Precision--Recall curve} + \end{subfigure} + \caption{Primary binary-classification performance plots.} +\end{figure} + +\begin{figure}[ht] + \centering + \includegraphics[width=0.6\linewidth]{figures/stability_heatmap_placeholder.pdf} + \caption{Heatmap showing probe stability or accuracy across sampling settings (temperature/top-p).} +\end{figure} + +\subsection{Example tables} +\begin{table}[ht] +\centering +\caption{Aggregate classifier performance (mean $\pm$ std over folds)} +\begin{tabular}{lcccc} +\toprule +Model & Accuracy & Precision & Recall & F1 \\ +\midrule +Baseline (random) & 0.50 & 0.50 & 0.50 & 0.50 \\ +Logistic Regression & 0.72 $\pm$ 0.03 & 0.71 $\pm$ 0.04 & 0.70 $\pm$ 0.03 & 0.70 $\pm$ 0.03 \\ +Linear SVM (C=1.0) & 0.78 $\pm$ 0.02 & 0.77 $\pm$ 0.03 & 0.76 $\pm$ 0.03 & 0.76 $\pm$ 0.02 \\ +SVM + PCA (95\%) & 0.76 $\pm$ 0.03 & 0.75 $\pm$ 0.03 & 0.75 $\pm$ 0.04 & 0.75 $\pm$ 0.03 \\ +\bottomrule +\end{tabular} +\end{table} + +\begin{table}[ht] +\centering +\caption{Per-group breakdown (top 8 groups by size)} +\begin{tabular}{lrrrr} +\toprule +Group ID & \# Models & Accuracy & Precision & Recall \\ +\midrule +group\_A & 42 & 0.92 & 0.90 & 0.95 \\ +group\_B & 38 & 0.84 & 0.82 & 0.80 \\ +group\_C & 30 & 0.79 & 0.75 & 0.78 \\ +... & ... & ... & ... & ... \\ +\bottomrule +\end{tabular} +\end{table} + +\section{Practical Recommendations and Optimizations} +\begin{itemize} + \item The dominant runtime cost is fetching model metadata; parallelize API calls with a thread pool and caching. + \item Use group-aware splitting to avoid leakage from related models appearing in both train and test. + \item Keep the classifier linear for interpretability and speed; examine coefficients to find discriminative features. + \item Store intermediate caches and partial JSONL outputs to resume long runs. + \item Save the scaler, PCA model, and trained SVM for reproducible evaluation pipelines. +\end{itemize} + +\section{Appendix: Example Commands} +\begin{verbatim} +# Export model-tree relations (nohup recommended for long runs) +python export_hf_model_tree.py \ + --models-file huggingface_llm_list.jsonl \ + --output-file model_relations.jsonl \ + --summary-file model_relations_summary.json + +# Train an SVM probe (sketch) +python scripts/train_svm_probe.py \ + --features-dir out/dna_features \ + --relations model_relations.jsonl \ + --output-dir out/svm_probe \ + --cv-folds 5 --svm-C 1.0 +\end{verbatim} + +\end{document} \ No newline at end of file diff --git a/docs/figures/small_scale_section_6_11/bounded_paired_gallery.png b/docs/figures/small_scale_section_6_11/bounded_paired_gallery.png new file mode 100644 index 0000000000000000000000000000000000000000..e5fd9397c777cb455f76da7ade88cc2241659233 GIT binary patch literal 75856 zcmeFZXIN8f_daODhKLOd-GT@RsPvA4(u7D6q=mylfdBz10ZC{!5S1cTN`iE$QCbKP zPyta&D1ih51gQZ6i4c+qq5LJ^o$ZFZb^S{$j7D4h2pJ9va&E1Ly5y=ZuO;=7;)(1t+&li(f^zt>Q?IHZ<5Wl%; zL)rJA%k2*=zMWG4{)3^wE@1rsJ>sSJ0CV~8A%1ti;m-eF?$9kh62I$T*AZ4{#d-d9 zy`$)c?zUk5b67l&IPsqg;6vWQ|1S>U{uh3NTY862L)kYWdV?gRS^6wvvJD+Oq!H@yY-8^baRuHr?9MD zdTd!6-@wUIMCz>7PKA`$<8D%-c%=HOT->6g4vVyNKNP3Kn?Unr4!n9eC;LkU=1h1` z2!3M+-` 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Mixed and worst-setting Top-10.00.20.40.60.81.0τ=0.2τ=0.3CosineLinear SVMRBF SVM● mixed (95% CI) ▲ worst cellB. Paired gain over cosine+0.00+0.05+0.10+0.15τ=0.2 Linear SVM+0.086τ=0.2 RBF SVM+0.034τ=0.3 Linear SVM+0.101τ=0.3 RBF SVM+0.067Paired Top-1 differenceC. 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KKbLh*2~7ZEZiEd0 literal 0 HcmV?d00001 diff --git a/docs/figures/small_scale_section_6_11/verification_tree_diagnostics.svg b/docs/figures/small_scale_section_6_11/verification_tree_diagnostics.svg new file mode 100644 index 0000000..3fab6ff --- /dev/null +++ b/docs/figures/small_scale_section_6_11/verification_tree_diagnostics.svg @@ -0,0 +1 @@ +Verification prevalence and tree-aware diagnosticsExact-only pair verification and cosine retrieval over 95 common modelsA. Binary verification metrics0.00.20.40.60.81.0PR-AUCROC-AUCPrecisionRecallSpecificityMCCTest repeat 1Test repeat 2B. False-positive burden0510152014.0Repeat 115.9Repeat 2False positives per queryPositive prevalence = 1/95C. Tree-aware distance diagnostic0.00.20.40.60.81.00.4450.929Exact Top-1: 0.526Same setting0.5660.929Exact Top-1: 0.379Cross T=0.3Exact pairDisconnected pairNo related pairs occurred in this cohort \ No newline at end of file diff --git a/docs/primary_robustness_experiment.tex b/docs/primary_robustness_experiment.tex new file mode 100644 index 0000000..a3ff329 --- /dev/null +++ b/docs/primary_robustness_experiment.tex @@ -0,0 +1,1000 @@ +\documentclass[11pt,a4paper]{article} + +\usepackage[margin=1in]{geometry} +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} +\usepackage{amsmath,amssymb} +\usepackage{booktabs} +\usepackage{graphicx} +\usepackage{longtable} +\usepackage{multirow} +\usepackage{xcolor} +\usepackage{hyperref} +\usepackage{listings} +\usepackage{enumitem} + +\hypersetup{hidelinks} +\lstset{ + basicstyle=\ttfamily\small, + breaklines=true, + frame=single, + columns=fullflexible, + keepspaces=true +} + +\newcommand{\todoresult}{\textcolor{gray}{\textit{Not yet available}}} +\newcommand{\method}[1]{\texttt{#1}} + +\title{Primary LLM-DNA Robustness Experiment\\ +\large Prospective Experimental Protocol and Report Template} +\author{} +\date{July 21, 2026} + +\begin{document} +\maketitle + +\begin{abstract} +This document records the prospective design of the primary LLM-DNA decoding +robustness experiment before generation and evaluation are complete. The study +will measure how temperature and top-$p$ affect exact-model identification, +whether a classifier can mitigate the resulting accuracy loss, and whether a +multi-sample distributional representation improves robustness under a matched +generation budget. The primary cohort contains 300 models selected without +using evaluation performance. The experiment contains one deterministic +condition and a complete $4\times3$ stochastic decoding grid, with four +independent generation repeats per condition. The principal deployment setting +assumes that temperature and top-$p$ are hidden at inference time and only an +upper bound on temperature is known. No experimental result is claimed in the +primary report; all primary result fields are explicitly marked as pending. +Small-scale exploratory demonstrations using previously generated artifacts are +reported separately and are not substitutes for the preregistered experiment. +\end{abstract} + +\section{Status and scope} + +\begin{center} +\fbox{\parbox{0.92\linewidth}{ +\textbf{Primary experiment status: not yet run.} The cohort file, launch +scripts, evaluation scripts, statistical outputs, and reporting plan have been +prepared. Numerical entries and figures in the primary Results section are +placeholders. Section~\ref{sec:small-demo} contains explicitly labelled +small-scale demonstrations from existing artifacts; they must not be +interpreted as primary findings. +}} +\end{center} + +This experiment replaces the 77-model interim analysis as the primary +robustness study. The earlier analysis remains a legacy diagnostic because it +used an incomplete sweep, one generation repeat per reported cell, an +historical reference gallery with unknown decoding parameters, and a binary +same-or-related task that was not directly comparable to exact-model Top-1 +retrieval. + +\section{Research questions and hypotheses} + +\subsection{Primary research questions} + +\begin{enumerate}[label=\textbf{RQ\arabic*.}] + \item How do temperature and top-$p$ affect exact-model classification from + LLM-DNA representations? + \item On the same exact-model task, can a classifier trained on the full DNA + vector reduce the accuracy degradation relative to cosine retrieval? + \item Does the proposed multi-sample distributional representation improve + robustness beyond original DNA under the same response and token + budget? + \item If temperature and top-$p$ are unknown at inference time, but + temperature is known to satisfy $T\leq\tau$, what average and + worst-setting accuracy can be expected? +\end{enumerate} + +\subsection{Secondary research questions} + +\begin{enumerate}[label=\textbf{SRQ\arabic*.}] + \item When exact identification fails, does the predicted model remain close + to the true model in the Hugging Face model tree? + \item Do direct parent--child, indirect, and disconnected model pairs exhibit + different DNA-distance distributions as decoding stochasticity rises? + \item How sensitive are the conclusions to response encoder, truncation, + projection dimension, projection seed, and the number of response + samples? +\end{enumerate} + +The main hypothesis is that temperature will cause a larger reduction in exact +classification accuracy than top-$p$ over the planned range. Classifier and +distributional-method benefits will be judged by paired accuracy differences +and by a shallower degradation curve, rather than by comparing accuracies from +different tasks. + +\section{Model cohort} + +\subsection{Selection rule} + +The planned cohort contains 300 Hugging Face models. It was selected from 470 +eligible models with valid unsuffixed reference DNA artifacts and recorded +parameter counts between 0.3 and 7.5 billion. Selection used seeded round-robin +sampling over architecture $\times$ logarithmic parameter-size strata. It did +not use retrieval accuracy, SVM accuracy, response quality, or any other test +outcome. Before launching the primary grid, three models that repeatedly +returned empty responses in the pilot control run were replaced with +similar-size models whose cached baseline runs each contained 100 non-empty +responses. These reliability replacements were documented in advance and did +not use evaluation accuracy. + +The fixed artifacts are: + +\begin{itemize} + \item cohort: \path{configs/rand_chinese_stratified_300.jsonl}; + \item audit: \path{configs/rand_chinese_stratified_300_audit.json}; + \item selection seed: 42; and + \item cohort SHA-256: + \texttt{4bc0dcebcd0e4d0e1240687fe981ac177a4306541b4813a943396b659cfe2661}. +\end{itemize} + +The audit file records the eligible population, selection method, selected +model IDs, architecture and size metadata, exclusions, and output hash. The +cohort must not be changed in response to preliminary performance. Any further +operational replacement must be justified by a generation failure, matched as +closely as practical on parameter size, recorded in the audit, and fixed before +the affected run is launched. + +\subsection{Completion requirement} + +The intended final analysis uses all 300 models. The automated primary analysis +will refuse to run if fewer than 250 models are common to all required training +and test runs. Missing models should first be backfilled. If the final common +cohort remains below 300, the report will provide success rates and failure +reasons by decoding cell and compare retained and excluded models by size and +architecture. + +\section{Prompt set and DNA construction} + +Every model receives the same 100 synthetic classical-Chinese prompts from +\path{data/rand/rand_dataset_chinese.json}. The prompt construction and random +projection seed remain fixed at 42 for every model, setting, and repeat. + +For the original representation, each response is encoded using +\method{all-mpnet-base-v2}. The 100 response embeddings are concatenated and +mapped to a 128-dimensional DNA vector by a seeded Gaussian random projection. +The current maximum response length is 1024 generated tokens. The saved +metadata records at least the model ID, dataset, prompt count, temperature, +top-$p$, sampling flag, generation seed, maximum length, projection seed, +encoder, and DNA dimension. + +Because the sentence encoder is primarily English-oriented and truncates long +inputs, the final study will additionally report response-length and encoder +token-length distributions, the fraction exceeding the encoder limit, and a +sensitivity analysis with a Chinese or multilingual response encoder. + +\section{Decoding design} + +\subsection{Factorial grid} + +The deterministic condition is +\[ + T=0, \qquad \text{sampling disabled}, \qquad p=1.0\text{ (recorded only)}. +\] +Top-$p$ is inactive when sampling is disabled; consequently, $T=0$ appears once +and is not repeated as three nominal top-$p$ cells. + +For stochastic decoding, the complete grid is +\[ + T\in\{0.2,0.3,0.5,0.7\}, \qquad + p\in\{0.8,0.9,1.0\}. +\] +This gives 13 decoding conditions in total. Four independent generation +repeats are planned for every condition, giving 52 run cells and +\[ + 300\ \text{models}\times13\ \text{conditions}\times4\ \text{repeats} + =15{,}600 +\] +model-level extraction tasks before retries. + +\subsection{Independent repeats and seeds} + +The response-sampling seeds are: + +\begin{table}[ht] +\centering +\caption{Generation-repeat assignment. The projection seed remains 42 in all rows.} +\label{tab:repeat-assignment} +\begin{tabular}{ccc} +\toprule +Repeat & Generation seed & Primary role \\ +\midrule +1 & 42001 & training and reference gallery \\ +2 & 42002 & training and reference gallery \\ +3 & 42003 & held-out test query \\ +4 & 42004 & held-out test query \\ +\bottomrule +\end{tabular} +\end{table} + +No response, DNA vector, or repeat may appear in both the training/reference +split and the held-out query split. Generation seed and projection seed are +stored separately so that response variability is not confounded with a change +in the random projection. + +\section{Primary exact-model task} + +Let $\mathcal{M}$ be the fixed common model cohort. Given a query DNA vector +$\mathbf{d}_{m,s,r}$ generated by model $m$, decoding setting $s=(T,p)$, and +held-out repeat $r\in\{3,4\}$ (that is, $r$ is either repeat 3 or repeat 4 in +Table~\ref{tab:repeat-assignment}), each method evaluates every candidate +$j\in\mathcal{M}$. Let $\mathcal{D}^{\mathrm{train}}_j$ denote the set of +training DNA vectors available for candidate model $j$. Its gallery centroid is +\[ + \boldsymbol{\mu}_j + =\frac{1}{|\mathcal{D}^{\mathrm{train}}_j|} + \sum_{\mathbf{x}\in\mathcal{D}^{\mathrm{train}}_j}\mathbf{x}. +\] +The standard cosine similarity and cosine distance both take the query and the +candidate centroid as inputs: +\[ + S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) + =\frac{\mathbf{d}^{\mathsf T}\boldsymbol{\mu}_j} + {\lVert\mathbf{d}\rVert_2\lVert\boldsymbol{\mu}_j\rVert_2}, + \qquad + D_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) + =1-S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j). +\] +Thus, the cosine rule may be written equivalently as +\[ + \widehat{m}^{(\mathrm{cos})} + =\arg\min_{j\in\mathcal{M}} + D_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j) + =\arg\max_{j\in\mathcal{M}} + S_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j). +\] +No square is applied in the standard cosine distance used by the evaluation +code; each Euclidean norm already satisfies +$\lVert\mathbf{d}\rVert_2=(\sum_k d_k^2)^{1/2}$, but the cosine value itself is +not squared. The squared norm appearing below belongs instead to the RBF kernel. +For the SVM rules, the explicit class-$j$ decision scores and predictions are +\[ + f_j^{(a)}(\mathbf{d})= + \begin{cases} + \mathbf{w}_j^{\mathsf T}\mathbf{z}(\mathbf{d})+b_j, + & a=\mathrm{lin},\\[4pt] + \displaystyle + \sum_{i=1}^{n_{\mathrm{SV}}}\alpha_{ij} + \exp\!\left( + -\gamma\lVert\mathbf{z}(\mathbf{d})-\mathbf{z}(\mathbf{x}_i)\rVert_2^2 + \right)+b_j, + & a=\mathrm{rbf}. + \end{cases} + \qquad + \widehat{m}^{(a)} + =\arg\max_{j\in\mathcal{M}}f_j^{(a)}(\mathbf{d}_{m,s,r}), + \quad a\in\{\mathrm{lin},\mathrm{rbf}\}. +\] +Here $\mathbf{z}(\cdot)$ is the feature-wise standardization learned from the +training split, $(\mathbf{w}_j,b_j)$ are the linear-SVM parameters, and +$\{\mathbf{x}_i,\alpha_{ij}\}_{i=1}^{n_{\mathrm{SV}}}$ and $\gamma$ are the +RBF-SVM support vectors, effective class-$j$ coefficients, and kernel +bandwidth parameter, respectively. The prediction is correct only if +$\widehat{m}^{(a)}=m$. Thus, cosine and SVM methods +are evaluated on identical multiclass labels, queries, and candidate sets. + +\subsection{Methods} + +\begin{table}[ht] +\centering +\caption{Planned representation $\times$ classifier comparison.} +\label{tab:method-matrix} +\begin{tabular}{lll} +\toprule +Representation & Decision rule & Purpose \\ +\midrule +Original 128-D DNA & cosine nearest centroid & primary retrieval baseline \\ +Original 128-D DNA & multiclass linear SVM & classifier-only ablation \\ +Original 128-D DNA & multiclass RBF SVM & nonlinear classifier ablation \\ +Multi-sample mean DNA & cosine nearest centroid & matched-budget averaging baseline \\ +Distributional DNA & exact RBF-MMD & proposed representation \\ +Distributional DNA & multiclass SVM & full proposed method, if applicable \\ +\bottomrule +\end{tabular} +\end{table} + +The linear and RBF SVMs consume the full 128-dimensional DNA vector. The legacy +linear SVM using only negative cosine distance is not considered an exact-model +classification improvement because a monotone linear transformation of cosine +distance cannot alter closed-set candidate ranking. It remains only a binary +verification baseline. + +\subsection{Known-setting descriptive evaluation} + +For each held-out condition $(T,p)$ and each method $a$, the experiment reports +\[ + A_a(T,p)=\frac{1}{|\mathcal{Q}_{T,p}|} + \sum_{q\in\mathcal{Q}_{T,p}}\mathbb{I}[\widehat{m}_{a,q}=m_q]. +\] +Top-1 is primary; Top-3, Top-5, and mean reciprocal rank (MRR) are secondary. +The change from the deterministic condition is +\[ + \Delta A_a(T,p)=A_a(T,p)-A_a(0). +\] +All per-setting comparisons will use the same held-out model queries. + +\section{Unknown-temperature bounded deployment} + +The main deployment scenario assumes that the exact decoding parameters are +not provided to the classifier. For a temperature bound +\[ + \tau\in\{0.2,0.3,0.5,0.7\}, +\] +one classifier is trained using pooled repeats 1--2 from every allowed setting +with $T\leq\tau$. Temperature and top-$p$ are omitted from its features. It is +then evaluated on held-out repeats 3--4 from all allowed settings. + +To avoid overweighting temperatures that happen to have more nominal cells, +repeats are first averaged within each $(T,p)$ cell. The primary mixed accuracy +is the equal-cell mean +\[ + A_a^{\mathrm{mix}}(\tau) + =\frac{1}{|\mathcal{S}_\tau|} + \sum_{s\in\mathcal{S}_\tau} A_a(s), + \qquad + \mathcal{S}_\tau=\{(T,p):T\leq\tau\}. +\] +The robustness guarantee is summarized by the worst observed setting +\[ + A_a^{\mathrm{worst}}(\tau) + =\min_{s\in\mathcal{S}_\tau} A_a(s). +\] +These quantities directly answer how accurately the system performs when the +decoding setting is hidden but its temperature is bounded. + +\section{Distributional and matched-budget evaluation} + +The distributional method operates on repeated response samples rather than a +single point estimate. It will be compared against both a single-sample cosine +baseline and a matched-budget mean/centroid baseline. Every method comparison +must use the same number of response samples, prompts, and total generated-token +budget unless the budget difference is explicitly plotted. + +The planned sample-budget ablation is +\[ + K\in\{1,2,4,8\}, +\] +subject to the number of available independent responses. For each $K$, the +report will include accuracy, generated tokens, reference storage, and analysis +time. This distinguishes gains due to the representation from gains obtained +merely by consuming more samples. + +\section{Secondary verification and tree-aware analysis} + +\subsection{Binary verification} + +If binary pairwise verification is reported, its tasks are separated into: + +\begin{enumerate} + \item exact same-model versus unrelated; + \item lineage-only (off-diagonal related) versus unrelated; and + \item the legacy same-or-related versus unrelated task. +\end{enumerate} + +Training may downsample negative pairs or use class weighting. Testing uses all +available pairs at their natural prevalence. The main binary metrics are +precision--recall AUC, precision, recall, specificity, Matthews correlation +coefficient, false positives per query, and ROC-AUC. Balanced-test accuracy may +be shown only as an explicitly labelled diagnostic. + +\subsection{Model-tree distance} + +The Hugging Face relation exporter stores direct base-model edges in addition +to connected components. Using the undirected direct-edge graph, the shortest +tree distance is +\[ + d_{\mathrm{tree}}(i,j)= + \begin{cases} + 0, & i=j,\\ + 1, & i\text{ and }j\text{ share a direct edge},\\ + 2,3,\ldots, & \text{connected by the corresponding number of edges},\\ + \infty, & \text{disconnected}. + \end{cases} +\] + +The final report will show DNA-distance distributions for exact, direct +one-hop, indirect two-hop, indirect three-or-more-hop, same-component with +unknown hop distance, and disconnected pairs. For Top-1 predictions, cumulative +tree-aware accuracy will be reported as exact, within one hop, within two hops, +and within the same connected component. This analysis describes error severity +and does not replace exact-model accuracy as the primary endpoint. + +\section{Statistical analysis plan} + +\subsection{Uncertainty} + +Every accuracy and MRR estimate will include a 95\% confidence interval. +Resampling will preserve dependence among observations: + +\begin{itemize} + \item model IDs are resampled as clusters across methods and settings; + \item generation repeat is treated as a higher-level resampling unit when + sufficient repeats are available; and + \item tree-related analyses use model-tree component clusters where relevant. +\end{itemize} + +Pair-level observations sharing a query model, reference model, or tree +component will not be treated as independent Bernoulli trials. + +\subsection{Paired comparisons} + +Method differences are computed on identical held-out queries. The principal +quantity is +\[ + \delta_{a,b}(T,p)=A_a(T,p)-A_b(T,p), +\] +with a paired cluster-bootstrap confidence interval. The analysis will also +test whether a method changes the temperature degradation slope, for example +with a mixed-effects logistic model of the form +\[ + \operatorname{logit}\Pr(\mathrm{correct}) + =\beta_0+\beta_1 T+\beta_2 p+\beta_3\mathrm{method} + +\beta_4(T\times\mathrm{method}) + +\beta_5(p\times\mathrm{method})+u_{\mathrm{model}}+u_{\mathrm{repeat}}. +\] +The interaction terms, rather than isolated cell differences, address whether +a method mitigates robustness loss. + +\subsection{Multiplicity and exploratory analyses} + +Top-1 accuracy and bounded mixed accuracy are primary endpoints. Top-3, Top-5, +MRR, tree-aware accuracy, encoder ablations, and projection ablations are +secondary. Any unplanned subgroup or hyperparameter analysis will be labelled +exploratory. If many method-by-setting significance tests are presented, a +false-discovery-rate correction will be applied. + +\section{Ablations and validity checks} + +The following checks are planned where artifacts and compute permit: + +\begin{enumerate} + \item projection dimensions $\{64,128,256,512\}$ and at least five projection + seeds; + \item an unprojected or prompt-level distance baseline; + \item prompt bootstrap and split-half reliability; + \item response encoder comparison including a Chinese or multilingual + encoder; + \item response-token and encoder-token length distributions and truncation + rates by setting; + \item fixed $T=0$ gallery, same-setting repeat gallery, and multi-setting + centroid gallery; + \item leave-one-setting-out evaluation for unseen decoding configurations; + \item performance by model size, architecture, and chat/base status; and + \item failure probability by model attributes and decoding setting. +\end{enumerate} + +\clearpage +\section{Small-scale demonstration report using existing artifacts} +\label{sec:small-demo} + +\subsection{Scope and split} + +To check that the analyses in Sections 6--11 are executable before the primary +sweep is complete, small exploratory demonstrations were run on July 23, 2026, +using only artifacts already present under \path{out/rand_chinese}. No new model +responses were generated. Repeat 1 was used for training/reference data and +repeat 2 for testing. Six complete stochastic cells were available: +\[ + T\in\{0.2,0.3\},\qquad p\in\{0.8,0.9,1.0\}. +\] +The $T=0.5$ and $T=0.7$ cells were excluded because they did not have a +sufficiently complete independent-repeat split. These demonstrations use +convenience cohorts determined by artifact availability, not the fixed +300-model primary cohort. They are therefore pipeline checks and exploratory +effect estimates only. + +\subsection{Executive summary} + +\noindent\fbox{\parbox{0.94\linewidth}{ +\textbf{Demo outcome.} The available artifacts support an end-to-end +demonstration of exact classification, bounded unknown-temperature evaluation, +paired model-cluster uncertainty, exact-only binary verification, a limited +distributional comparison, a limited tree-aware analysis, and a gallery +sensitivity check. Across the six complete cells, the pooled linear SVM had the +highest equal-cell Top-1 (0.722), followed by RBF SVM (0.688) and cosine +retrieval (0.621). The hardest observed cell was $(T,p)=(0.3,1.0)$. These are +exploratory results from availability-selected cohorts and must not be read as +the outcome of the 300-model primary experiment. +}} + +\medskip +Table~\ref{tab:demo-coverage} maps each planned protocol component to what +could actually be demonstrated. ``Partial'' means that the code path ran but +the available cohort or repeat structure could not exercise the full planned +analysis. + +\begin{table}[htbp] +\centering +\caption{Coverage of Sections 6--11 by the small-scale demonstration.} +\label{tab:demo-coverage} +\begin{tabular}{p{0.13\linewidth}p{0.35\linewidth}p{0.23\linewidth}p{0.15\linewidth}} +\toprule +Protocol section & Demonstrated analysis & Available scale & Status \\ +\midrule +6: Exact task + & Cosine centroid, linear SVM, and RBF SVM on identical queries + & Six cells; 84 common models & Complete demo \\ +7: Bounded deployment + & Equal-cell mixed and worst-setting accuracy for $\tau=0.2,0.3$ + & 89 and 84 models & Complete demo \\ +8: Distributional + & Single cosine, mean cosine, and exact RBF-MMD + & Eight models; 20 prompts; at most two repeats & Partial \\ +9: Verification + & Exact-only linear probe at natural pair prevalence + & 95 models; 9,025 pairs per direction & Complete demo \\ +9: Tree-aware + & Exact versus disconnected error and distance summaries + & 95 models; no related pairs & Partial \\ +10: Statistics + & Model-cluster CIs and paired method differences + & One held-out repeat per cell & Partial \\ +11: Validity + & Fixed-$T=0$ versus pooled-gallery diagnostic + & 86 versus 84 models & Partial \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Exact classification and bounded deployment demo} + +For $\tau=0.2$, the common cohort contained 89 models and the training pool +contained the three repeat-1 cells at $T=0.2$. For $\tau=0.3$, requiring all six +cells reduced the common cohort to 84 models. The classifier was not given +temperature or top-$p$. Table~\ref{tab:demo-per-setting} reports held-out +repeat-2 Top-1 accuracy for the $\tau=0.3$ pooled training experiment. + +\begin{table}[ht] +\centering +\caption{Exploratory held-out Top-1 accuracy with 84 common models.} +\label{tab:demo-per-setting} +\begin{tabular}{ccrrr} +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM \\ +\midrule +0.2 & 0.8 & 0.643 & 0.821 & 0.702 \\ +0.2 & 0.9 & 0.726 & 0.845 & 0.762 \\ +0.2 & 1.0 & 0.595 & 0.643 & 0.643 \\ +0.3 & 0.8 & 0.774 & 0.869 & 0.857 \\ +0.3 & 0.9 & 0.690 & 0.786 & 0.762 \\ +0.3 & 1.0 & 0.298 & 0.369 & 0.405 \\ +\bottomrule +\end{tabular} +\end{table} + +Figure~\ref{fig:demo-exact-heatmaps} makes the setting-level pattern explicit. +The linear SVM led in four cells and tied in one, while the RBF SVM was highest +at $(0.3,1.0)$. All methods dropped sharply in that cell, so the demo does not +support a general monotone temperature effect independent of top-$p$. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/exact_top1_heatmaps.png} +\caption{Exploratory exact-model Top-1 by method and decoding cell. Darker +cells indicate higher accuracy. The equal-cell means correspond to the +$\tau=0.3$ pooled-training experiment.} +\label{fig:demo-exact-heatmaps} +\end{figure} + +The equal-cell bounded summaries are shown in +Table~\ref{tab:demo-bounded}. Confidence intervals resample model IDs as +clusters across the included cells. + +\begin{table}[ht] +\centering +\caption{Exploratory bounded-deployment Top-1 summaries.} +\label{tab:demo-bounded} +\begin{tabular}{clrrr} +\toprule +$\tau$ & Method & Mixed accuracy (95\% CI) & Worst cell & Models \\ +\midrule +0.2 & Cosine centroid & 0.596 (0.528, 0.667) & 0.562 & 89 \\ + & Linear SVM & 0.682 (0.614, 0.745) & 0.652 & 89 \\ + & RBF SVM & 0.629 (0.562, 0.700) & 0.584 & 89 \\ +\addlinespace +0.3 & Cosine centroid & 0.621 (0.554, 0.688) & 0.298 & 84 \\ + & Linear SVM & 0.722 (0.675, 0.772) & 0.369 & 84 \\ + & RBF SVM & 0.688 (0.633, 0.742) & 0.405 & 84 \\ +\bottomrule +\end{tabular} +\end{table} + +On identical queries, the paired model-cluster bootstrap difference between +linear SVM and cosine was $0.086$ (95\% CI $0.037$ to $0.135$) at +$\tau=0.2$ and $0.101$ (95\% CI $0.058$ to $0.147$) at $\tau=0.3$. The +corresponding RBF-SVM differences were $0.034$ (95\% CI $0.004$ to $0.071$) +and $0.067$ (95\% CI $0.026$ to $0.111$). These intervals quantify only this +availability-selected demo and do not test the primary hypotheses. + +Figure~\ref{fig:demo-bounded-paired} combines three views that should remain +distinct in interpretation: bounded performance, paired within-query method +gains, and the unpaired gallery diagnostic. In particular, the fixed-gallery +bars are not a controlled estimate of gallery effects because the gallery +cohort and training budget differ from the pooled condition. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/bounded_paired_gallery.png} +\caption{Bounded-deployment and sensitivity summary. Panel A shows equal-cell +mixed accuracy with model-cluster 95\% intervals and the worst observed cell. +Panel B shows paired Top-1 differences relative to cosine. Panel C is an +explicitly unpaired fixed-gallery versus pooled-gallery diagnostic.} +\label{fig:demo-bounded-paired} +\end{figure} + +\subsection{Distributional and matched-budget demo} + +The exact RBF-MMD pilot ran successfully on eight lexicographically selected +models, 20 common evaluation prompts, and two response repeats, using +$T=0.2,p=0.8$ as the reference setting. Single-sample cosine, mean-cosine, and +exact RBF-MMD each achieved Top-1 and MRR of 1.000 for the same-setting +comparison and for cross-setting queries at $(0.3,0.8)$ and $(0.3,0.9)$. The +same-setting split has only one repeat on each side, whereas the cross-setting +comparisons pool both repeats per model on each side. This is a successful +end-to-end demonstration, but the tiny cohort saturated all methods and +therefore provides no evidence that the distributional representation is +better. The available two repeats also do not support the planned +$K\in\{1,2,4,8\}$ ablation. + +\begin{table}[htbp] +\centering +\caption{Distributional pilot Top-1 (MRR was also 1.000 in every entry).} +\label{tab:demo-distributional} +\begin{tabular}{lrrr} +\toprule +Query comparison & Single cosine & Mean cosine & Exact RBF-MMD \\ +\midrule +Same $(0.2,0.8)$ & 1.000 & 1.000 & 1.000 \\ +Cross $(0.2,0.8)\rightarrow(0.3,0.8)$ & 1.000 & 1.000 & 1.000 \\ +Cross $(0.2,0.8)\rightarrow(0.3,0.9)$ & 1.000 & 1.000 & 1.000 \\ +\bottomrule +\end{tabular} +\end{table} + +This complete saturation is itself a useful demo diagnostic: a larger and more +difficult cohort is required before representation differences can be +estimated. It would be inappropriate to select a method from these pilot +numbers. + +\subsection{Binary verification and tree-aware demo} + +An exact-only linear verification probe was trained on one $T=0.2,p=0.8$ +repeat and tested on the other, in both directions, over 95 common models. +Testing retained all 8,930 negative pairs per direction, giving a natural +positive prevalence of $1/95$. Results are in +Table~\ref{tab:demo-verification}. + +\begin{table}[ht] +\centering +\caption{Exploratory exact-only binary verification at natural prevalence.} +\label{tab:demo-verification} +\begin{tabular}{lrrrrrr} +\toprule +Test repeat & PR-AUC & ROC-AUC & Precision & Recall & Specificity & MCC \\ +\midrule +1 & 0.334 & 0.832 & 0.049 & 0.726 & 0.851 & 0.163 \\ +2 & 0.373 & 0.869 & 0.045 & 0.747 & 0.831 & 0.155 \\ +\bottomrule +\end{tabular} +\end{table} + +Despite the apparently strong ROC-AUC, the probe produced 1,329 and 1,511 +false positives in the two directions, respectively. The resulting precision +near 5\% demonstrates why the primary verification report emphasizes PR-AUC, +MCC, specificity, and false positives per query rather than balanced accuracy +alone. + +The tree-aware cosine demo used repeat 1 at $(0.2,0.8)$ as the gallery and 95 +common models. Exact Top-1 was 0.526 for the same-setting repeat-2 query and +0.379 for the $(0.3,0.8)$ repeat-2 query. All 104 combined errors were between +models disconnected in the exported relation graph, so within-one-hop, +within-two-hop, and same-component accuracy did not exceed exact accuracy. +Mean cosine distance was 0.445 versus 0.929 for exact and disconnected pairs +in the same-setting comparison, and 0.566 versus 0.929 in the cross-setting +comparison. No direct or indirect related pair occurred within this +availability-selected cohort, so lineage-error severity could not be +demonstrated. + +Figure~\ref{fig:demo-verification-tree} emphasizes two practical limitations. +First, ROC-AUC obscures the false-positive burden when only one of 95 candidate +pairs is positive. Second, the tree-aware demo separates exact and disconnected +pairs but cannot validate the intended one-hop and indirect strata because no +such pair occurred in the common cohort. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/verification_tree_diagnostics.png} +\caption{Verification and tree-aware diagnostics. Panels A--B show binary +metrics and false positives per query at natural prevalence. Panel C compares +mean cosine distance for exact and disconnected pairs and annotates exact +retrieval accuracy.} +\label{fig:demo-verification-tree} +\end{figure} + +\subsection{Validity-check demo and deferred items} + +As a limited gallery check, a fixed $T=0$ repeat-1 gallery was compared with +the multi-setting pooled gallery on the same six query settings. Equal-cell +mixed Top-1 with the fixed gallery was 0.403 for cosine, 0.310 for linear SVM, +and 0.341 for RBF SVM over 86 common models. The corresponding pooled-gallery +values were 0.621, 0.722, and 0.688 over 84 common models. Because the cohorts +and number of training examples differ, this is a diagnostic rather than a +paired method comparison; it merely confirms that gallery construction is +consequential. + +The remaining Section 10--11 analyses were deferred. Two temperatures and one +held-out repeat are inadequate for the planned mixed-effects degradation model. +Alternative projection dimensions/seeds, encoder comparisons, reliable +encoder-token truncation counts, $K=4$ and $K=8$, lineage-only verification, +and related-pair tree strata are not supported by the currently complete +artifacts. Leave-one-setting-out and subgroup analyses are feasible later but +were omitted to keep this demonstration deliberately small. + +\subsection{Demo conclusions and primary-study handoff} + +The demonstration supports the following limited conclusions: + +\begin{itemize} + \item The exact-classification and bounded-deployment pipelines operate on + disjoint generation repeats and produce aligned per-query scores for + all three classifiers. + \item Within this demo, the linear SVM had the highest mixed Top-1 at both + temperature bounds, and its paired advantage over cosine was positive. + \item Robustness was setting-dependent: $(0.3,1.0)$ was substantially harder + than the other five cells for every method. + \item Natural-prevalence verification exposed a large operational + false-positive burden that ROC-AUC alone would conceal. + \item The distributional and tree-aware code paths run, but the present data + are respectively too easy and too sparse in related pairs to compare + the intended methods. + \item Gallery construction materially changes the diagnostic accuracies, so + the primary report must keep gallery definitions and training budgets + fixed in paired comparisons. +\end{itemize} + +Before any primary claim is made, the study still requires the fixed 300-model +cohort, complete repeats 1--4 for all 13 conditions, the preregistered +repeat-1/2 versus repeat-3/4 split, $K=4$ and $K=8$ response samples, sufficient +direct and indirect model-tree pairs, and the planned encoder/projection +metadata. The small-scale numbers should not be copied into the pending primary +Results tables. + +Machine-readable outputs are stored under +\path{out/small_scale_section_6_11}. The exact-classification, verification, +distributional, fixed-gallery, and tree-aware subdirectories contain the +selected cohorts, per-query predictions where applicable, aggregate CSV files, +and JSON summaries. The three report figures are regenerated without plotting +library dependencies using: + +\begin{lstlisting}[language=bash] +python scripts/plot_small_scale_section_6_11_demo.py +\end{lstlisting} + +\clearpage + +\section{Execution and resource plan} + +The generation launcher is +\path{scripts/run_primary_robustness_experiment.sh}. It uses the 300-model +cohort, four repeats, the 13-condition grid, isolated outputs under +\path{out/primary_robustness_300}, and isolated logs. The scheduler dynamically +uses visible GPUs up to ten concurrent workers unless an explicit disjoint GPU +list is supplied. + +The current 100-model $T=0.5$ top-$p$ control experiment may continue as a +supplementary study. If both studies run simultaneously, they must use disjoint +GPU sets to avoid resource contention. Sequential execution is preferred +because the processes otherwise share Hugging Face model caches and cache +cleanup policies. + +Transient failures are retried and recorded. Progress reports include planned, +successful, failed, and missing models plus failure-status counts. Missing +cells are never represented as zero accuracy and curves are not interpolated +through absent cells. + +\section{Reproducibility and provenance} + +The experiment will preserve: + +\begin{itemize} + \item exact cohort file and SHA-256 hash; + \item prompt dataset and seed; + \item model ID and available model metadata; + \item temperature, top-$p$, sampling flag, generation seed, and token limit; + \item response files and independent repeat suffixes; + \item sentence encoder, DNA dimension, projection method, and projection seed; + \item run manifests, status journals, failure summaries, predictions, and + machine-readable metric tables; and + \item the evaluation code and report version used to produce final figures. +\end{itemize} + +The unsuffixed historical gallery will not be used for the primary causal +interpretation because its temperature and top-$p$ were not recorded. It may be +retained as a clearly labelled legacy reference-sensitivity analysis. + +\section{Planned results} + +\subsection{Cohort and completion table} + +\begin{table}[ht] +\centering +\caption{Cohort completion and availability. Populate after generation.} +\begin{tabular}{lrrrr} +\toprule +Condition & Planned & Successful & Failed & Common cohort \\ +\midrule +$T=0$ & 300 & \todoresult & \todoresult & \todoresult \\ +$T=0.2$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ +$T=0.3$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ +$T=0.5$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ +$T=0.7$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{tabular} +\end{table} + +Counts in this table are per repeat unless stated otherwise. The final version +will additionally report failure reasons and retained/excluded cohort +distributions. + +\subsection{Per-setting exact classification} + +\begin{longtable}{ccrrrr} +\caption{Held-out exact-model performance. Every entry will be reported with a +95\% model-cluster confidence interval.}\label{tab:per-setting}\\ +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ +\midrule +\endfirsthead +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ +\midrule +\endhead +0.0 & N/A & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.2 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.2 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.2 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.3 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.3 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.3 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{longtable} + +\subsection{Bounded unknown-temperature deployment} + +\begin{table}[ht] +\centering +\caption{Temperature-agnostic deployment results. The classifier is not given +temperature or top-$p$.} +\begin{tabular}{clrrr} +\toprule +$\tau$ & Method & Mixed accuracy & Worst setting & MRR \\ +\midrule +0.2 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.3 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.5 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.7 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Planned figures} + +The final report will contain: + +\begin{enumerate} + \item exact Top-1 versus temperature, faceted by top-$p$, with one curve per + representation/classifier and 95\% confidence bands; + \item accuracy loss relative to $T=0$ and estimated degradation slopes; + \item mixed and worst-setting accuracy versus temperature bound $\tau$; + \item stacked tree-aware Top-1 error severity versus temperature; and + \item accuracy versus response-sample/token budget for the mean and + distributional representations. +\end{enumerate} + +\section{Planned interpretation} + +The final conclusions will be limited to what the completed protocol supports: + +\begin{itemize} + \item the effect of query-side decoding conditions relative to explicit, + independently generated galleries; + \item classifier mitigation on the same exact-model task; + \item representation gains under matched budgets; and + \item expected and worst observed performance when decoding parameters are + hidden but temperature is bounded. +\end{itemize} + +No lineage-robustness conclusion will be based on aggregate positives dominated +by same-model pairs. No method will be declared superior from a point estimate +alone, and no causal temperature conclusion will use the historical +unknown-setting gallery. + +\appendix + +\section{Execution commands} + +Build or verify the fixed cohort: + +\begin{lstlisting}[language=bash] +python3 scripts/build_experiment_cohort.py --target 300 +\end{lstlisting} + +Launch generation after the existing control run finishes, or assign a +disjoint GPU set: + +\begin{lstlisting}[language=bash] +nohup bash scripts/run_primary_robustness_experiment.sh \ + > logs/primary_robustness_master.log 2>&1 & +\end{lstlisting} + +Run the preregistered exact-model and tree-aware analyses after all four repeats +are complete: + +\begin{lstlisting}[language=bash] +bash scripts/run_primary_analysis.sh +\end{lstlisting} + +The primary analysis outputs are written beneath +\path{out/primary_robustness_300_analysis}. The exact-model evaluator produces +per-setting metrics, per-query predictions, bounded-deployment tables, cohort +metadata, and JSON summaries. + +\section{Implementation map} + +\begin{longtable}{p{0.34\linewidth}p{0.58\linewidth}} +\toprule +File & Purpose \\ +\midrule +\endfirsthead +\toprule +File & Purpose \\ +\midrule +\endhead +\path{scripts/run_primary_robustness_experiment.sh} + & Launch the 300-model, 13-condition, four-repeat extraction sweep. \\ +\path{scripts/run_primary_analysis.sh} + & Construct the fixed train/test repeat split and run primary analyses. \\ +\path{scripts/evaluate_exact_dna_classification.py} + & Compare cosine centroids and multiclass SVMs on exact identity; produce + bounded unknown-temperature results and confidence intervals. \\ +\path{scripts/analyze_tree_aware_errors.py} + & Produce hierarchical retrieval and tree-distance error summaries. \\ +\path{scripts/evaluate_dna_probe_sweep.py} + & Run legacy and separated binary verification tasks using all test + negatives by default. \\ +\path{scripts/run_rbf_mmd_pilot.py} + & Compare single-sample, matched mean-cosine, and exact RBF-MMD retrieval. \\ +\path{scripts/export_hf_model_tree.py} + & Export direct parent/base edges and connected components. \\ +\path{scripts/report_sweep_progress.py} + & Report completion, missing models, and failure-status counts. \\ +\bottomrule +\end{longtable} + +\end{document} diff --git a/docs/rbf_mmd_pilot.md b/docs/rbf_mmd_pilot.md new file mode 100644 index 0000000..f716580 --- /dev/null +++ b/docs/rbf_mmd_pilot.md @@ -0,0 +1,34 @@ +# RBF-MMD Pilot + +## Setup +- Data dir: `out/rand_chinese` +- Reference setting: `0.2:0.8` +- Comparison settings: `0.2:0.8`, `0.3:0.8`, `0.3:0.9` +- Selected models: `8` +- Selected prompts: `20` +- Calibration prompts: `4` +- Evaluation prompts: `16` +- Embedding features: `4096` + +## Notes +- `single_cosine` uses one response sample per prompt from the selected run split. +- `mean_cosine` averages repeated samples per prompt before concatenation. +- `exact_rbf_mmd` computes prompt-wise squared RBF-MMD and averages across prompts. +- `same_*` compares two repeat splits from the same decoding setting. +- `cross_*` compares the reference setting against another setting. +- NA in intermediate figures means the model or prompt was not present in the required split, or the run lacked enough repeats. + +## Results +- `same_t02_p08` / `single_cosine`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `same_t02_p08` / `mean_cosine`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `same_t02_p08` / `exact_rbf_mmd`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `cross_t02_p08_to_t03_p08` / `single_cosine`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `cross_t02_p08_to_t03_p08` / `mean_cosine`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `cross_t02_p08_to_t03_p08` / `exact_rbf_mmd`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `cross_t02_p08_to_t03_p09` / `single_cosine`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `cross_t02_p08_to_t03_p09` / `mean_cosine`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 +- `cross_t02_p08_to_t03_p09` / `exact_rbf_mmd`: top1=1.000, top3=1.000, top5=1.000, mrr=1.000 + +## Current Result + +The first small pilot run on the cached `out/rand_chinese` subset selected 8 models and 20 prompts. On that tiny subset, all three methods reached perfect top1/top3/top5/MRR, so the current signal is mainly in the distance magnitudes rather than ranking changes. The exact RBF-MMD self-distance was lower than single-sample cosine on the same split, which is consistent with the distributional formulation but is not yet a decisive retrieval gain. diff --git a/docs/revised_experiment_protocol.md b/docs/revised_experiment_protocol.md new file mode 100644 index 0000000..45312fb --- /dev/null +++ b/docs/revised_experiment_protocol.md @@ -0,0 +1,96 @@ +# Revised LLM-DNA robustness experiment + +## Primary claim and task + +The primary task is closed-set **exact-model identification**. Every compared +method ranks the same model labels for the same query DNA and gallery. Pairwise +same-or-related verification is retained only as a secondary diagnostic; it is +not compared directly with 300-way Top-1 accuracy. + +The main method matrix is: + +| Representation | Classifier | Role | +|---|---|---| +| Original 128-D DNA | cosine centroid | retrieval baseline | +| Original 128-D DNA | multiclass linear SVM | classifier-only ablation | +| Original 128-D DNA | multiclass RBF SVM | nonlinear classifier ablation | +| Multi-sample response distribution | mean cosine | budget-matched distributional baseline | +| Multi-sample response distribution | exact RBF-MMD | proposed representation | + +## Data protocol + +- Use the predeclared, performance-independent 300-model cohort in + `configs/rand_chinese_stratified_300.jsonl`. Its audit JSON records the + selection rule, eligible pool, seed, metadata, and SHA-256 hash. +- Generate one deterministic `T=0` cell and the complete factorial grid + `T in {0.2, 0.3, 0.5, 0.7}` by `top_p in {0.8, 0.9, 1.0}`. +- Generate four independent response repeats. Repeats 1-2 form training and + galleries; repeats 3-4 are held-out queries. +- The generation seed changes by repeat (`42001` through `42004`), while the + prompt/projection seed remains 42. Both are stored in artifacts. +- A result is blocked when fewer than 250 common models remain. Failed models + should be backfilled rather than silently shrinking the final cohort. +- All methods use the same prompts, model cohort, train/test repeats, and query + budget. Distributional results must include the matched-budget mean-centroid + baseline, not only the single-sample baseline. + +Launch generation with: + +```bash +python3 scripts/build_experiment_cohort.py --target 300 +nohup bash scripts/run_primary_robustness_experiment.sh \ + > logs/primary_robustness_master.log 2>&1 & +``` + +The launcher dynamically uses all visible GPUs up to ten workers. Override +`GPUS` to pin cards or `MAX_CONCURRENT_GPUS` to change the cap. + +After all four repeats are complete, run: + +```bash +bash scripts/run_primary_analysis.sh +``` + +## Required analyses + +1. Per-setting exact Top-1, Top-3, Top-5, MRR, and model-bootstrap 95% CIs. +2. Temperature-agnostic deployment at each bound + `tau in {0.2, 0.3, 0.5, 0.7}`. The classifier sees pooled eligible training + settings but not temperature/top-p. Report equal-cell mixed accuracy and + worst-setting accuracy. +3. Paired method comparisons on identical held-out model queries. The key + comparison is the degradation slope/retained accuracy, not unrelated task + accuracies. +4. Tree-aware error severity as a secondary analysis: exact, direct one-hop, + two-hop, same-component, and disconnected predictions. Export direct HF + edges with `export_hf_model_tree.py`; connected-component closure alone is + not treated as a direct relationship. +5. Legacy pairwise verification uses every test pair at natural prevalence. + Training may downsample negatives. Report PR-AUC, MCC, specificity, and + false positives; separate same-model and lineage-only labels before making + lineage claims. + +## Interpretation guardrails + +- The unsuffixed historical gallery has unknown decoding provenance and is a + legacy sensitivity analysis only. The primary experiment uses explicit, + independently generated repeat galleries. +- A scalar linear SVM on cosine distance cannot change closed-set ranking; the + primary SVM therefore consumes the full 128-D DNA in a multiclass task. +- `T=0` appears once because top-p is inactive without sampling. +- Do not claim a top-p effect, RBF advantage, or lineage robustness unless the + paired confidence interval supports it. +- Report failure rates/reasons by cell and compare excluded versus retained + model size and architecture distributions. + +## Final outputs + +- Figure 1: exact accuracy versus temperature, faceted by top-p, with one curve + per representation/classifier and 95% CIs. +- Figure 2: accuracy loss relative to `T=0` plus the method-by-temperature + interaction/degradation slope. +- Figure 3: mixed and worst-setting accuracy versus the unknown-temperature + upper bound `tau`. +- Figure 4: stacked tree-aware error severity versus temperature. +- Tables: protocol/cohort, complete per-setting results, bounded-deployment + results, and matched-budget method/efficiency ablations. diff --git a/docs/rfftrace_progress.tex b/docs/rfftrace_progress.tex new file mode 100644 index 0000000..c2020dc --- /dev/null +++ b/docs/rfftrace_progress.tex @@ -0,0 +1,103 @@ +\documentclass[11pt,a4paper]{article} +\usepackage{geometry} +\usepackage{amsmath} +\usepackage{booktabs} +\usepackage{graphicx} +\geometry{margin=1in} +\title{RFFTrace Progress Report} +\author{} +\date{\today} + +\begin{document} +\maketitle + +\begin{abstract} +This note summarises the current repository status for distributional +LLM fingerprinting: exact RBF‑MMD pilot experiments are implemented and +tested; Random Fourier Feature (RFF) based fingerprints ("RFFTrace") +have been specified but not yet implemented or evaluated. +\end{abstract} + +\section{Summary of current implementation} +\begin{itemize} + \item Exact RBF‑MMD: implemented in \texttt{scripts/run\_rbf\_mmd\_pilot.py} as + the function \texttt{exact\_mmd2(a,b,sigma)} and exercised by the pilot. + \item Pilot script: the pilot selects small subsets (models/prompts), + computes three retrieval baselines: \texttt{single\_cosine}, \texttt{mean\_cosine}, + and \texttt{exact\_rbf\_mmd}, and writes results to \texttt{out/rbf\_mmd\_pilot}. + \item Tests: \texttt{tests/test\_distributional\_pilot.py} checks core utilities, + including \texttt{exact\_mmd2} behaving correctly for identical samples. +\end{itemize} + +\section{Pilot results (brief)} +On a very small pilot (8 models, 20 prompts) cached under \texttt{out/rand\_chinese}, +all three methods reached perfect retrieval (top1/top3/top5/MRR = 1.0). The repo +includes a short report at \texttt{docs/rbf\_mmd\_pilot.md} summarising these numbers. + +\section{RFFTrace status} +\begin{itemize} + \item Not implemented: there is currently no code that computes Random Fourier + Features (no \texttt{psi} map, no projection dimension \(D\), nor per‑prompt averaging over \(R\) samples). + \item No experiments: retrieval/identification comparisons using RFF approximations + (RFFTrace) are not present in the repo; only exact MMD pilot exists. +\end{itemize} + +\section{RFFTrace algorithm (planned)} +For reproducibility, the planned RFFTrace pipeline is: +\begin{enumerate} + \item For each model \(m\) and prompt \(x_i\), sample responses \(z_{m,i,r}\) for \(r=1\ldots R\). + \item Embed each response into a vector \(u_{m,i,r}=\phi(\text{response})\) using the current sentence encoder. + \item Draw random frequencies and phases for RFF: for $j=1\ldots D$, sample $\omega_j\sim\mathcal{N}(0,\frac{1}{\gamma}I)$ and $b_j\sim\text{Uniform}(0,2\pi)$. + \item Compute the nonlinear feature map + $$\psi(u)=\sqrt{\tfrac{2}{D}}\left[\cos(\omega_j^\top u + b_j)\right]_{j=1}^D.$$\\ + \item Per prompt, average the RFF features: + $$\widehat{\mu}_{m,i}=\frac{1}{R}\sum_{r=1}^R \psi(u_{m,i,r}).$$ + \item Concatenate (and optionally normalize/compress) across prompts: + $$\widehat v_m=\frac{1}{\sqrt t}[\widehat{\mu}_{m,1};\ldots;\widehat{\mu}_{m,t}].$$ +\end{enumerate} + +The squared Euclidean distance between two RFFTrace vectors approximates the +average per‑prompt RBF MMD between the two response distributions. + +\section{Planned experiments} +Primary comparisons to run once RFF is implemented: +\begin{itemize} + \item Retrieval on the existing pilot subset: compare \texttt{single\_cosine}, \texttt{mean\_cosine}, \texttt{exact\_rbf\_mmd}, and \texttt{RFFTrace} for several $(R,D,\gamma)$. + \item Sensitivity sweeps: vary $R\in\{2,4,8,16\}$, $D\in\{256,1024,4096\}$, bandwidth $\gamma$ (median heuristic and multiples). + \item Cross‑temperature identification: test robustness when enrolling at one decoding setting and querying at another. + \item Compute / memory profiling: measure time and peak memory per model for RFF vs exact MMD. + \item Baseline ablations: compare RFF averages against naive averaging of original embeddings (the ``mean'' baseline) to show (or disprove) the benefit. +\end{itemize} + +\section{Implementation plan} +I can implement RFFTrace with the following deliverables: +\begin{enumerate} + \item A new script \texttt{scripts/run\_rfftrace\_pilot.py} that mirrors the existing RBF pilot, adding RFF computation and parameter sweeps. + \item Unit tests covering the RFF feature map consistency and small‑sample behaviour. + \item CSV/JSON outputs under \texttt{out/rfftrace\_pilot} and a short Markdown/LaTeX summary like the existing pilot. +\end{enumerate} + +Estimated effort: small implementation (1–2 hours) plus pilot runs (minutes for small subsets; hours if scaling up). + +\section{How to run the existing exact RBF pilot} +Use the pilot script that is already in the repository. Example: +\begin{verbatim} +python3 scripts/run_rbf_mmd_pilot.py --data-dir out/rand_chinese --output-dir out/rbf_mmd_pilot +\end{verbatim} + +\section{Next steps} +Tell me whether you want me to: +\begin{itemize} + \item Implement RFFTrace and run the small pilot now (adds code + tests + outputs). + \item Add RFF features only and leave the pilot runs for you to execute locally. + \item Draft a figure/table template for presenting RFF sensitivity sweeps in LaTeX. +\end{itemize} + +\section{Files of interest} +\begin{itemize} + \item Existing pilot: \texttt{scripts/run\_rbf\_mmd\_pilot.py} + \item Pilot report: \texttt{docs/rbf\_mmd\_pilot.md} + \item Tests: \texttt{tests/test\_distributional\_pilot.py} +\end{itemize} + +\end{document} diff --git a/docs/svm_modeltree_experiment.tex b/docs/svm_modeltree_experiment.tex new file mode 100644 index 0000000..7863afa --- /dev/null +++ b/docs/svm_modeltree_experiment.tex @@ -0,0 +1,325 @@ +\documentclass[11pt,a4paper]{article} +\usepackage{graphicx} +\usepackage{booktabs} +\usepackage{amsmath} +\usepackage[utf8]{inputenc} +\usepackage{geometry} +\usepackage[hidelinks]{hyperref} +\geometry{margin=1in} + +\title{Model-Tree Pairwise SVM for LLM-DNA Robustness\\ +\large Experimental Protocol and Interim Results} +\author{} +\date{\today} + +\begin{document} +\maketitle + +\begin{abstract} +This report evaluates how stochastic decoding affects model identification from +LLM-DNA signatures. It compares cosine nearest-neighbour retrieval with +pairwise linear and radial-basis-function (RBF) support vector machines (SVMs) +that predict whether two models are related. Positive pairs mix identical +models with models connected in a Hugging Face model tree, so this legacy +verification task is not a like-for-like alternative to exact identification. +Because the generation sweep is incomplete, this snapshot +uses a fixed 77-model cohort shared by every reported decoding setting, avoiding +comparisons between different successful-model subsets. +\end{abstract} + +\section{Task definition} +For a model $m$ and decoding setting $s=(T,p)$, the pipeline generates responses +to a fixed prompt set and converts them into a DNA vector +$\mathbf{d}_{m,s}\in\mathbb{R}^{128}$. We evaluate two related tasks. + +\paragraph{Exact-model retrieval.} +Given a query DNA $\mathbf{d}_{m,s}$, rank the model DNAs in the fixed reference +gallery by cosine distance. A retrieval is correct when the DNA of the same +model is ranked first. This task produces top-1, top-3, top-5, and mean +reciprocal rank (MRR). + +\paragraph{Model-relatedness classification.} +Given a query/reference pair $(m_i,m_j)$, predict +\begin{equation} + y_{ij} = + \begin{cases} + 1, & m_i=m_j\text{ or }m_i\text{ and }m_j\text{ belong to the same exported model-tree component},\\ + 0, & \text{otherwise}. + \end{cases} +\end{equation} +The classifier receives one scalar feature, +$x_{ij}=-d_{\mathrm{cos}}(\mathbf{d}_{m_i,s},\mathbf{d}_{m_j,\mathrm{ref}})$, +so larger values indicate greater similarity. This formulation avoids treating +only the exact same model as positive and therefore represents known model +lineage in the labels. + +\section{Experimental data} + +\subsection{Model set} +The requested sweep contains 100 Hugging Face models listed in +\texttt{configs/rand\_chinese\_0p3b\_7b\_rerun.jsonl}. Evaluation uses the +intersection of models with valid DNA files in both the unsuffixed reference +gallery and the relevant sweep run. To make settings directly comparable, the +snapshot first retains runs with at least 80 available models, then intersects +their model IDs with the reference gallery. This produces one cohort of 77 +models used for every reported retrieval query, SVM training run, and SVM test +run. The exact cohort and pre-filter availability counts are stored in +\texttt{temp\_top\_p\_modeltree\_common\_models.json}; no model is selected +using test performance. + +\subsection{Prompt set and DNA construction} +Every model receives the same 100 synthetic classical-Chinese prompts from +\texttt{data/rand/rand\_dataset\_chinese.json}. The dataset generator uses seed +42, and the saved extraction metadata identifies the probe set as +\texttt{rand\_chinese\_100}. Each response is embedded with +\texttt{all-mpnet-base-v2} (768 dimensions); the 100 response embeddings are +concatenated and reduced by a seeded Gaussian random projection to one +128-dimensional DNA signature. The response length limit is 1024 tokens and +the extraction seed is 42. Thus, prompts and feature construction are held +fixed while only decoding parameters change. + +\subsection{Decoding settings} +The planned grid uses temperatures $T\in\{0.0,0.2,0.3,0.5,0.7\}$ and +top-$p\in\{0.8,0.9,1.0\}$. For $T=0$, sampling is disabled; for $T>0$, +sampling is enabled with the displayed top-$p$. Each suffix +\texttt{\_tXX\_pYY\_r1} identifies its temperature, top-$p$, and repeat. + +The 80-model availability rule retains the following settings: +\[ +(0.0,0.8),\ (0.2,0.8),\ (0.2,0.9),\ (0.2,1.0),\ +(0.3,0.8),\ (0.3,0.9),\ (0.3,1.0),\ (0.5,0.8),\ +(0.5,1.0),\ (0.7,0.9). +\] +The $(0.7,0.9)$ cell uses the earlier complete \texttt{\_temp07\_run} output. +The current $(0.0,0.9)$, $(0.0,1.0)$, $(0.5,0.9)$, $(0.7,0.8)$, and +$(0.7,1.0)$ runs have 46, 3, 17, 2, and 0 available models, respectively, and +are excluded until their reruns provide adequate coverage. At $T=0$, top-$p$ +is inactive because sampling is disabled; the additional deterministic cells +will therefore be a data-completeness check rather than a decoding comparison. +The reference gallery consists of the existing unsuffixed DNA records under +\texttt{out/rand\_chinese}. Its historical response files do not record an +explicit temperature/top-$p$ pair, so it should be described as a fixed +reference gallery rather than assigned an inferred decoding setting. + +\section{Model-tree labels} +The relation exporter reads Hugging Face metadata and joins base-model links +with a streaming union--find procedure. The resulting connected components +are stored in \texttt{out/hf\_model\_tree/model\_relations.jsonl}. The export +summary is: +\begin{itemize} + \item 11,812 requested models and 13,330 parsed nodes; + \item 6,282 extracted base-model edges; + \item 790 connected components of size at least two; + \item 618 skipped metadata requests; and + \item external models excluded from the exported relation groups. +\end{itemize} +Connected-component membership is transitive. This is a practical lineage +label, not a claim that every pair in a component has identical behaviour. + +\section{SVM specification} + +\subsection{Classifiers used in the reported experiment} +The linear scikit-learn pipeline is +\begin{center} +\texttt{StandardScaler()} $\rightarrow$ +\texttt{LinearSVC(C=1.0, class\_weight='balanced', dual='auto',} +\texttt{max\_iter=20000, random\_state=42)}. +\end{center} +The RBF-kernel probe uses +\texttt{SVC(kernel='rbf', C=1.0, gamma='scale', +class\_weight='balanced', random\_state=42)}. Both probes were enabled in the +snapshot reported here. Because the input has only one feature (negative +cosine distance), the linear model learns a threshold in standardized distance +space, while the RBF model can learn a nonlinear decision interval along that +same scalar axis. This kernel probe is distinct from the exact RBF-MMD +distributional experiment. + +\subsection{Class balancing} +All positive pairs are retained. Negatives are deterministically downsampled +without replacement using seed 42 and a negative-to-positive ratio of 1:1. +The same 1:1 balancing rule is used for the held-out test examples. The saved +summaries therefore have a 50\% positive rate in both training and testing; +\texttt{class\_weight='balanced'} remains enabled as a safeguard. Metrics on +this balanced test sample should not be interpreted as performance under the +natural, highly imbalanced prevalence of all possible model pairs. + +\section{Train/test protocol} +The SVM uses leave-one-decoding-run-out evaluation. For each displayed +temperature/top-$p$ cell: +\begin{enumerate} + \item that run is held out and all of its valid query/reference pairs form + the test pool; + \item every other run supplied to the evaluator forms the training pool; + \item training and test pools are balanced independently as described above; + and + \item the SVM is fit once on the training pool and evaluated on the held-out + setting. +\end{enumerate} +Before constructing any pairs, every retained run and the reference gallery are +restricted to the same 77 model IDs. Thus, both the training pool and held-out +test pool use the identical model cohort, although they use different decoding +runs. +Training and testing are therefore performed \emph{across different decoding +settings}, not on a random pair split within the same setting. A training run +may share the same temperature or top-$p$ with the test run, but it cannot be +the identical run specification. + +There are 10 retained decoding runs. Consequently, each fold trains on nine +runs and tests on one, a 90\%/10\% split by run. For one run, the 77 query models +are paired with the same 77 reference models, producing $77^2=5{,}929$ ordered +pairs. Of these, 99 are positive: 77 same-model diagonal pairs and 22 ordered +off-diagonal pairs connected by the exported model-tree labels. The remaining +5,830 pairs are negative. Because the cohort and relation labels are fixed, +these counts are identical in every fold and for both SVM variants. + +\begin{table}[ht] +\centering +\caption{Pair accounting for every leave-one-decoding-run-out fold. ``Raw +negative'' is the number available before sampling; all positives are retained, +and an equal number of negatives is sampled with seed 42. Linear and RBF SVMs +use the same sampled train and test pairs.} +\label{tab:pair-counts} +\begin{tabular}{lrrrrr} +\toprule +Split & Runs & Positive & Raw negative & Sampled negative & Final samples \\ +\midrule +Train & 9 & 891 & 52,470 & 891 & 1,782 \\ +Test & 1 & 99 & 5,830 & 99 & 198 \\ +\bottomrule +\end{tabular} +\end{table} + +Thus, the raw pair pool is highly imbalanced: positives are only +$99/5{,}929\approx1.67\%$ of each run's pairs. The reported classification +metrics are instead computed on the balanced 198-pair test sample, with 99 +positives and 99 negatives. This distinction is important when interpreting +accuracy: the reported values measure discrimination on a controlled balanced +test set, not accuracy at the natural pair prevalence. + +Model identities and model-tree components are not held out: the same model can +appear under different decoding settings in training and testing. The current +question is therefore robustness to a decoding shift for known models, not +generalization to unseen model families. A family-held-out protocol would +require grouping by model-tree component and is a separate experiment. + +\section{Metrics} + +\paragraph{Retrieval metrics.} +Top-$k$ is the fraction of queries for which the exact model appears among the +first $k$ candidates. MRR is +$\frac{1}{N}\sum_{i=1}^{N}1/\mathrm{rank}_i$. Macro precision, recall, and F1 +are also saved for exact-model top-1 predictions. + +\paragraph{Binary SVM metrics.} +We report accuracy, balanced accuracy, positive-class precision, recall and F1, +specificity, Matthews correlation coefficient (MCC), ROC-AUC, and average +precision. Balanced accuracy, MCC, ROC-AUC, and average precision are included +because ordinary accuracy alone can be misleading for related/unrelated pair +classification. The summary files also record TP, TN, FP, FN, the original +pair counts, and the sampled pair counts. + +\paragraph{Missing settings.} +A missing point in the line plots means that the run had fewer than 80 available +models when this snapshot was generated, normally because GPU scarcity left too +many generation tasks failed or unfinished. Missing points are not zero scores, +and lines are broken rather than interpolated across them. These runs are being +rerun and will be added once they satisfy the common-cohort rule. + +\section{Interim results} +Table~\ref{tab:summary} reports the snapshot values stored in +\texttt{temp\_top\_p\_modeltree\_summary.json}. These values will be replaced +after all planned sweep cells have completed. + +\begin{table}[ht] +\centering +\caption{Fair per-setting comparison on the same 77-model cohort. Top-1 and MRR +evaluate exact-model cosine retrieval. Lin. and RBF columns report balanced-test +accuracy and ROC-AUC under leave-one-run-out evaluation. Runs with fewer than +80 available models are excluded from this snapshot.} +\label{tab:summary} +\resizebox{\textwidth}{!}{% +\begin{tabular}{lrrrrrrrrr} +\toprule +Run & $T$ & Top-$p$ & Models & Top-1 & MRR & Lin. acc. & Lin. AUC & RBF acc. & RBF AUC \\ +\midrule +temp00\_p08\_r1 & 0.0 & 0.8 & 77 & 0.922 & 0.935 & 0.808 & 0.906 & 0.843 & 0.837 \\ +temp02\_p08\_r1 & 0.2 & 0.8 & 77 & 0.442 & 0.532 & 0.722 & 0.808 & 0.783 & 0.764 \\ +temp02\_p09\_r1 & 0.2 & 0.9 & 77 & 0.455 & 0.541 & 0.753 & 0.834 & 0.773 & 0.792 \\ +temp02\_p10\_r1 & 0.2 & 1.0 & 77 & 0.442 & 0.540 & 0.732 & 0.813 & 0.758 & 0.787 \\ +temp03\_p08\_r1 & 0.3 & 0.8 & 77 & 0.377 & 0.481 & 0.753 & 0.774 & 0.732 & 0.799 \\ +temp03\_p09\_r1 & 0.3 & 0.9 & 77 & 0.351 & 0.468 & 0.737 & 0.798 & 0.732 & 0.795 \\ +temp03\_p10\_r1 & 0.3 & 1.0 & 77 & 0.260 & 0.353 & 0.687 & 0.713 & 0.682 & 0.752 \\ +temp05\_p08\_r1 & 0.5 & 0.8 & 77 & 0.247 & 0.333 & 0.657 & 0.708 & 0.646 & 0.731 \\ +temp05\_p10\_r1 & 0.5 & 1.0 & 77 & 0.169 & 0.255 & 0.606 & 0.633 & 0.616 & 0.638 \\ +temp07\_p09\_legacy & 0.7 & 0.9 & 77 & 0.078 & 0.170 & 0.561 & 0.533 & 0.545 & 0.581 \\ +\bottomrule +\end{tabular} +} +\end{table} + +On the fixed cohort, exact-model retrieval falls sharply as decoding becomes +more stochastic: top-1 decreases from 0.922 at $T=0$ to 0.442--0.455 at +$T=0.2$, 0.260--0.377 at $T=0.3$, 0.169--0.247 at $T=0.5$, and 0.078 for the +legacy $T=0.7$ run. The pairwise probes degrade more gradually. This does not +establish that model-tree relatedness survives better than exact identity: 77 +of the 99 positive ordered pairs are exact same-model pairs, the binary task is +much easier than 77-way retrieval, and its test prevalence is artificially +50\%. The supported statement is only that aggregate same-or-related distance +separation degrades more slowly on this controlled balanced sample. + +The RBF kernel does not consistently dominate the linear threshold. Across the +fixed-cohort cells, their accuracies differ by at most 0.061: RBF is higher at +$T=0$, $T=0.2$, and $(0.5,1.0)$, while linear is slightly higher for the +$T=0.3$ cells, $(0.5,0.8)$, and the legacy $T=0.7$ cell. This mixed difference is +consistent with both classifiers receiving only one scalar distance feature; +there is limited structure for the kernel to exploit. These observations +remain provisional because each cell has one generation repeat and the grid is +not complete. + +\section{Figures} + +\begin{figure}[ht] + \centering + \includegraphics[width=0.95\linewidth]{out/dna_probe_sweep_modeltree/temp_top_p_modeltree_curves.png} + \caption{Exact-model retrieval on the shared 77-model cohort. Each panel fixes + top-$p$; labeled lines show top-1, top-3, top-5, and MRR as temperature changes. + Missing settings failed the 80-model availability rule, and lines are broken + rather than interpolated across those settings.} + \label{fig:retrieval-grid} +\end{figure} + +\begin{figure}[ht] + \centering + \includegraphics[width=0.95\linewidth]{out/dna_probe_sweep_modeltree/temp_top_p_modeltree_curves_probe_metrics.png} + \caption{Pairwise-SVM accuracy on the shared 77-model cohort. Each panel fixes + top-$p$ and compares the labeled linear and RBF-SVM lines across temperature. + Each point tests the displayed run after training on all other retained runs. + Accuracy equals balanced accuracy here because the test pairs are sampled + with equal positive and negative counts.} + \label{fig:svm-grid} +\end{figure} + +\section{Reproducibility and limitations} +The evaluation implementation is in +\texttt{scripts/evaluate\_dna\_probe\_sweep.py}; model-tree export is in +\texttt{scripts/export\_hf\_model\_tree.py}. Machine-readable summaries and +predictions are stored under \texttt{out/dna\_probe\_sweep\_modeltree}. +Random projection, negative sampling, and SVM fitting all use seed 42. + +The main limitations of the interim experiment are the incomplete decoding +grid, the provisional 80-model inclusion threshold, one repeat per included +cell, test-set downsampling, transitive model-tree labels, and model-family +overlap between training and testing. The historical reference gallery also +lacks recorded decoding parameters, so the temperature curve measures mismatch +to that particular gallery rather than a clean causal temperature effect. The +fixed cohort removes unequal coverage as a comparison confound but reduces the +population from 100 to 77 models. + +This table is therefore retained as a legacy diagnostic, not the primary +experiment. The revised protocol uses a predeclared 300-model cohort, four +independent generation seeds, explicit repeat galleries, full-natural- +prevalence verification, multiclass SVMs on the full DNA vector, and bounded +unknown-temperature evaluation. Its executable specification is +\texttt{docs/revised\_experiment\_protocol.md}. + +\end{document} diff --git a/docs/svm_modeltree_experiment_zh.html b/docs/svm_modeltree_experiment_zh.html new file mode 100644 index 0000000..bd40dd3 --- /dev/null +++ b/docs/svm_modeltree_experiment_zh.html @@ -0,0 +1,121 @@ + + + + + + DNA 探针扫描 — 模型树 SVM 实验（实际结果） + + + +

DNA 探针扫描 — 模型树 SVM 实验（实际结果）

+ +

摘要：本文报告记录了使用 export_hf_model_tree.py 和 + evaluate_dna_probe_sweep.py 的实际运行，所用的模型关系文件位于 + out/hf_model_tree/model_relations.jsonl，实验输出位于 + out/dna_probe_sweep_modeltree。

+ +

概述

+
    +
  1. 使用流式并查集（union-find）从模型列表导出 Hugging Face 模型关系（连通分量）。
    2. +
  2. 在多种生成设置（runs）上，使用最近邻和成对线性 SVM 探针评估 DNA 可识别性。
    3. +
  3. 将汇总指标、预测 CSV 和诊断图写入 out/dna_probe_sweep_modeltree。
    4. +
+ +

模型树导出摘要

+
    +
  • 输入模型数量：11812
    • +
  • 解析后节点数：13330
    • +
  • 提取的 base 边数：6282
    • +
  • 大小 ≥2 的连通分量（组）数：790
    • +
  • 跳过的模型（获取元数据时出错）：618
    • +
+ +

SVM 探针的实际实现

+
    +
  • 对每个运行（生成设置）加载预计算的模型 DNA 特征向量。
    • +
  • 构建查询模型与候选基模型之间的所有成对示例，每对记录两者间的距离（默认为余弦距离）。
    • +
  • 如果查询与基模型为同一模型或在导出的模型关系中属于同一关系组，则标记为正样本，否则为负样本。
    • +
  • 具体划分过程： +
      +
    1. 选择一个运行（test_run_name）作为保留的测试运行。
      2. +
    2. 通过遍历其余所有运行构建训练示例，生成这些运行中的所有（query, base）配对。
      3. +
    3. 测试示例仅来自被保留的测试运行（该运行中查询与基模型的所有配对）。
      4. +
    4. 训练和测试中的正样本依据 out/hf_model_tree/model_relations.jsonl 中的 + is_same_or_related(query, base) 判定。
      5. +
    5. 负样本按 sampled_negatives = round(negative_ratio * #positives) 随机采样（默认 + negative_ratio=1.0），脚本使用 RNG 种子 42 保证可复现性。
      6. +
    +
    • +
  • 特征与分类器：使用单一特征 x = -distance（取负以使较大值倾向正类）， + 经 StandardScaler 变换后喂入带 class_weight='balanced' 的 LinearSVC。
    • +
+ +

训练/测试划分（具体）

+
    +
  • 测试集：仅包含来自单个被保留运行（test_run_name）构建的所有配对示例。
    • +
  • 训练集：由其他所有运行（除被保留运行外的 --run 规范）构建的所有配对示例组成。
    • +
  • 负样本采样：配对构建后，按代码中的 balance_examples() 随机采样负样本至配置的 + negative_ratio（默认 1.0）；测试路径也采用相同的采样（测试负样本同样采样为 1.0 倍正样本）。
    • +
  • 后果：该划分衡量跨生成设置（temperature/top-p）的泛化能力，但不保证严格的组级排除—— + 如果相关模型在不同运行中都被生成，则可能同时出现在训练和测试中。若要做组级隔离评估 + （确保测试组的相关模型不出现在训练中），请使用基于连通分量 id 的 GroupKFold 或显式的组保留策略。
    • +
  • 观测到的测试/训练比率：该比率并非事先固定选择，而是由 leave-one-run-out 设计、每运行配对构建 + 以及平衡负样本（negative_ratio=1.0）共同决定。每个集合在采样后包含 正样本 + 采样负样本 + （正负样本数近似相等），因此正样本率由设计决定约为 50%。在生成的摘要 + （temp_top_p_modeltree_summary.json）中，每个运行的测试占比大约在 7.9%–13.5% + （平均约 12.5%），差异来源于不同被保留运行生成的配对数量不同。负样本采样的 RNG 种子为 42， + 保证了采样的确定性与可复现性。
    • +
+ +

结果（示例运行）

+

脚本生成了 temp_top_p_modeltree_summary.json，包含每个运行的聚合指标。表格展示了部分运行的关键指标。

+ + + + + + + + + + + + + + + + +
每运行汇总（最近邻 + 线性 SVM 指标）
运行温度Top-p模型数Top1MRR线性 AUC
temp00_p08_r10.00.8660.9240.9400.936
temp02_p08_r10.20.8960.4060.4960.818
temp02_p09_r10.20.9970.3610.4490.801
temp02_p10_r10.21.0930.3870.4840.804
temp03_p08_r10.30.8980.3270.4280.773
temp03_p09_r10.30.9980.2960.4000.759
temp03_p10_r10.31.0970.2270.3120.723
temp05_p08_r10.50.8880.2390.3110.706
+ +

图

+

生成的诊断图（来自实验输出目录）：

+
+ curves +
按设置组织的热图网格。每个面板显示 top-1/top-3/top-5/MRR 在 temperature 与 top-p 上的分布，并标出最佳单元。
+
+ +
+ linear accuracy heatmap + top1 heatmap +
（左）线性准确率热图 （右）Top‑1 热图。
+
+ +

说明与下一步

+
    +
  • 该 SVM 探针保持简单（单特征线性分类器），在低温度运行中表现较好；随着温度升高，自我距离增大，性能下降。
    • +
  • 若需要组感知评估，我可以将 evaluate_dna_probe_sweep.py 修改为使用 GroupKFold + 或按连通分量 id 显式保留并重新生成指标。
    • +
  • 演示或展示时可直接使用文中 PNG 以及 temp_top_p_modeltree_summary.json + / temp_top_p_modeltree_summary.csv 作为表和图的来源。
    • +
+ +

（生成于机器上，图片与 JSON 输出路径均为相对工作区路径。）

+ + diff --git a/docs/svm_modeltree_experiment_zh.tex b/docs/svm_modeltree_experiment_zh.tex new file mode 100644 index 0000000..0000be7 --- /dev/null +++ b/docs/svm_modeltree_experiment_zh.tex @@ -0,0 +1,131 @@ +\documentclass[11pt,a4paper]{article} +\usepackage{graphicx} +\usepackage{booktabs} +\usepackage{amsmath} +\usepackage[utf8]{inputenc} +\usepackage{geometry} +\geometry{margin=1in} +\title{DNA 探针扫描 — 模型树 SVM 实验（实际结果）} +\author{} +\date{\today} + +\begin{document} +\maketitle + +\begin{abstract} +本文报告记录了使用 \texttt{export\_hf\_model\_tree.py} 和 +\texttt{evaluate\_dna\_probe\_sweep.py} 的实际运行过程，所用的模型关系文件位于 +\texttt{out/hf\_model\_tree/model\_relations.jsonl}，实验输出位于 +\texttt{out/dna\_probe\_sweep\_modeltree}。 +\end{abstract} + +\section{概述} +本次流水线执行步骤如下： +\begin{enumerate} + \item 使用流式并查集（union--find）从模型列表导出 Hugging Face 模型关系（连通分量）。 + \item 在多种生成设置（runs）上，使用最近邻和成对线性 SVM 探针评估 DNA 可识别性。 + \item 将汇总指标、预测 CSV 和诊断图写入 \texttt{out/dna\_probe\_sweep\_modeltree}。 +\end{enumerate} + +\section{模型树导出摘要} +导出器写入了 \texttt{out/hf\_model\_tree/model\_relations.jsonl} 及摘要文件 +\texttt{out/hf\_model\_tree/model\_relations\_summary.json}。摘要中的关键计数： +\begin{itemize} + \item 输入模型数量：11812 + \item 解析后节点数：13330 + \item 提取的 base 边数：6282 + \item 大小 $\ge2$ 的连通分量（组）数：790 + \item 跳过的模型（获取元数据时出错）：618 +\end{itemize} + +\section{SVM 探针的实际实现} +脚本的实际做法概述： +\begin{itemize} + \item 对每个运行（生成设置）加载预计算的模型 DNA 特征向量。 + \item 构建查询模型与候选基模型之间的所有成对示例，每对记录两者间的距离（默认为余弦距离）。 + \item 如果查询与基模型为同一模型或在导出的模型关系中属于同一关系组，则标记为正样本，否则为负样本。 + \item 具体划分过程： + \begin{enumerate} + \item 选择一个运行（\texttt{test\_run\_name}）作为保留的测试运行。 + \item 通过遍历其余所有运行构建训练示例，生成这些运行中的所有（query, base）配对。 + \item 测试示例仅来自被保留的测试运行（该运行中查询与基模型的所有配对）。 + \item 训练和测试中的正样本依据 \texttt{out/hf\_model\_tree/model\_relations.jsonl} 中的 + \texttt{is\_same\_or\_related(query, base)} 判定。 + \item 负样本按 \texttt{sampled\_negatives = round(negative\_ratio * \#positives)} 随机采样（默认 + \texttt{negative\_ratio=1.0}），脚本使用 RNG 种子 42 保证可复现性。 + \end{enumerate} + \item 特征与分类器：使用单一特征 $x = -\text{distance}$（取负以使较大值倾向正类）， + 经 \texttt{StandardScaler} 变换后喂入带 \texttt{class\_weight='balanced'} 的 \texttt{LinearSVC}。 +\end{itemize} + +\section{训练/测试划分（具体）} +本次运行采用跨运行（leave‑one‑run‑out）划分： +\begin{itemize} + \item 测试集：仅包含来自单个被保留运行（\texttt{test\_run\_name}）构建的所有配对示例。 + \item 训练集：由其他所有运行（除被保留运行外的 \texttt{--run} 规范）构建的所有配对示例组成。 + \item 负样本采样：配对构建后，按代码中的 \texttt{balance\_examples()} 随机采样负样本至配置的 + \texttt{negative\_ratio}（默认 1.0）；测试路径也采用相同的采样（测试负样本同样采样为 1.0 倍正样本）。 + \item 后果：该划分衡量跨生成设置（temperature/top‑p）的泛化能力，但不保证严格的组级排除—— + 如果相关模型在不同运行中都被生成，则可能同时出现在训练和测试中。若要做组级隔离评估 + （确保测试组的相关模型不出现在训练中），请使用基于连通分量 id 的 \texttt{GroupKFold} 或显式的组保留策略。 + \item 观测到的测试/训练比率：该比率并非事先固定选择，而是由 leave‑one‑run‑out 设计、每运行配对构建 + 以及平衡负样本（\texttt{negative\_ratio=1.0}）共同决定。每个集合在采样后包含 正样本 + 采样负样本 + （正负样本数近似相等），因此正样本率由设计决定约为 50\%。在生成的摘要 + （\texttt{temp\_top\_p\_modeltree\_summary.json}）中，每个运行的测试占比大约在 7.9\%–13.5\% + （平均约 12.5\%），差异来源于不同被保留运行生成的配对数量不同。负样本采样的 RNG 种子为 42， + 保证了采样的确定性与可复现性。 +\end{itemize} + +\section{结果（示例运行）} +脚本生成了 \texttt{temp\_top\_p\_modeltree\_summary.json}，包含每个运行的聚合指标。 +表\ref{tab:summary} 展示了本次扫描中部分运行的关键指标。 + +\begin{table}[ht] +\centering +\caption{每运行汇总（最近邻 + 线性 SVM 指标）}\label{tab:summary} +\begin{tabular}{llrrrrr} +\toprule +运行 & 温度 & Top‑p & 模型数 & Top1 & MRR & 线性 AUC \\ +\midrule +temp00\_p08\_r1 & 0.0 & 0.8 & 66 & 0.924 & 0.940 & 0.936 \\ +temp02\_p08\_r1 & 0.2 & 0.8 & 96 & 0.406 & 0.496 & 0.818 \\ +temp02\_p09\_r1 & 0.2 & 0.9 & 97 & 0.361 & 0.449 & 0.801 \\ +temp02\_p10\_r1 & 0.2 & 1.0 & 93 & 0.387 & 0.484 & 0.804 \\ +temp03\_p08\_r1 & 0.3 & 0.8 & 98 & 0.327 & 0.428 & 0.773 \\ +temp03\_p09\_r1 & 0.3 & 0.9 & 98 & 0.296 & 0.400 & 0.759 \\ +temp03\_p10\_r1 & 0.3 & 1.0 & 97 & 0.227 & 0.312 & 0.723 \\ +temp05\_p08\_r1 & 0.5 & 0.8 & 88 & 0.239 & 0.311 & 0.706 \\ +\bottomrule +\end{tabular} +\end{table} + +\section{图} +以下为生成的诊断图（来自实验输出目录），如需展示可替换为更高分辨率导出图像。 + +\begin{figure}[ht] + \centering + \includegraphics[width=0.8\\linewidth]{out/dna_probe_sweep_modeltree/temp_top_p_modeltree_curves.png} + \caption{按设置组织的热图网格。每个面板显示 top-1/top-3/top-5/MRR 在 temperature 与 top-p 上的分布，并标出最佳单元。} +\end{figure} + +\begin{figure}[ht] + \centering + \begin{tabular}{cc} + \includegraphics[width=0.45\\linewidth]{out/dna_probe_sweep_modeltree/temp_top_p_modeltree_linear_accuracy_setting_heatmap.png} & + \includegraphics[width=0.45\\linewidth]{out/dna_probe_sweep_modeltree/temp_top_p_modeltree_top1_setting_heatmap.png} \\ + (a) 线性准确率热图 & (b) Top‑1 热图 \\ + \end{tabular} + \caption{按设置（temperature × top-p）划分的单指标热图。} +\end{figure} + +\section{说明与下一步} +\begin{itemize} + \item 该 SVM 探针故意保持简单（单特征线性分类器），在低温度运行（例如 \texttt{temp00\_p08\_r1}）中表现良好， + 因为最近邻效果已很强；随着温度升高，自我距离增大，性能下降。 + \item 若需要组感知评估（在未见的相关组上测试），我可以将 \texttt{evaluate\_dna\_probe\_sweep.py} + 修改为使用 \texttt{GroupKFold} 或按连通分量 id 做显式保留并重新生成指标。 + \item 演示或展示时可直接使用文中 PNG 以及 \texttt{temp\_top\_p\_modeltree\_summary.json} + / \texttt{temp\_top\_p\_modeltree\_summary.csv} 作为表和图的来源。 +\end{itemize} + +\end{document} From c8abbffbfcdc5af560046cc7d3076c27bcfb8187 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Fri, 24 Jul 2026 18:11:44 +0800 Subject: [PATCH 19/28] Update configs --- configs/rand_chinese_stratified_100.jsonl | 100 +++ .../rand_chinese_stratified_100_a100.jsonl | 23 + .../rand_chinese_stratified_100_audit.json | 629 ++++++++++++++++++ .../rand_chinese_stratified_100_rtx3090.jsonl | 77 +++ ...d_chinese_stratified_100_shards_audit.json | 119 ++++ 5 files changed, 948 insertions(+) create mode 100644 configs/rand_chinese_stratified_100.jsonl create mode 100644 configs/rand_chinese_stratified_100_a100.jsonl create mode 100644 configs/rand_chinese_stratified_100_audit.json create mode 100644 configs/rand_chinese_stratified_100_rtx3090.jsonl create mode 100644 configs/rand_chinese_stratified_100_shards_audit.json diff --git a/configs/rand_chinese_stratified_100.jsonl b/configs/rand_chinese_stratified_100.jsonl new file mode 100644 index 0000000..16d6489 --- /dev/null +++ b/configs/rand_chinese_stratified_100.jsonl @@ -0,0 +1,100 @@ +{"model_id": "FreedomIntelligence/openPangu-Embedded-1B", "provider": "huggingface", "task": "text-generation", "downloads": 2945, "parameter_count_billions": 0.97, "architecture": "PanguEmbedded"} +{"model_id": "swiss-ai/Apertus-8B-2509", "provider": "huggingface", "task": "text-generation", "downloads": 13107, "parameter_count_billions": 6.98, "architecture": "apertus"} +{"model_id": "arcee-ai/AFM-4.5B", "provider": "huggingface", "task": "text-generation", "downloads": 1822, "parameter_count_billions": 3.16, "architecture": "arcee"} +{"model_id": "Salesforce/codegen-350M-multi", "provider": "huggingface", "task": "text-generation", "downloads": 5016, "parameter_count_billions": 0.35, "architecture": "codegen"} +{"model_id": "Salesforce/codegen-2B-mono", "provider": "huggingface", "task": "text-generation", 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"LiquidAI/LFM2.5-1.2B-JP", "provider": "huggingface", "task": "text-generation", "downloads": 2519, "parameter_count_billions": 0.94, "architecture": "lfm2"} +{"model_id": "hyper-accel/ci-random-llama2-7b", "provider": "huggingface", "task": "text-generation", "downloads": 2259, "parameter_count_billions": 0.53, "architecture": "llama"} +{"model_id": "unsloth/tinyllama-chat", "provider": "huggingface", "task": "text-generation", "downloads": 12134, "parameter_count_billions": 1.17, "architecture": "llama"} diff --git a/configs/rand_chinese_stratified_100_shards_audit.json b/configs/rand_chinese_stratified_100_shards_audit.json new file mode 100644 index 0000000..bf1fa17 --- /dev/null +++ b/configs/rand_chinese_stratified_100_shards_audit.json @@ -0,0 +1,119 @@ +{ + "cohort": "configs/rand_chinese_stratified_100.jsonl", + "cohort_sha256": "19b7c7b554af4e98edeb3ab279f52ccae66a9bddbff81c0c15f2fb464e895e6e", + "assignment_rule": "the 23 largest models by recorded parameter_count_billions go to the two A100 GPUs; all remaining models go to the four-GPU RTX 3090 fleet", + "rtx3090": { + "path": "configs/rand_chinese_stratified_100_rtx3090.jsonl", + "count": 77, + "sha256": "e615f574cea8ce376143b906eeb71e06067f3ce3743bed32fbd2f35f1e13b807", + "models": [ + "FreedomIntelligence/openPangu-Embedded-1B", + "arcee-ai/AFM-4.5B", + "Salesforce/codegen-350M-multi", + "Salesforce/codegen-2B-mono", + "deepseek-ai/DeepSeek-Coder-V2-Lite-Base", + "ai-sage/GigaChat3-10B-A1.8B-bf16", + "euclaise/falcon_1b_stage2", + "tiiuae/Falcon-H1-0.5B-Base", + "tiiuae/Falcon-H1-1.5B-Base", + "tiiuae/Falcon-H1-3B-Base", + "TechxGenus/gemma-1.1-2b-it-GPTQ", + "unsloth/gemma-7b-it", + "yam-peleg/Hebrew-Gemma-11B-V2", + "vandijklab/C2S-Scale-Gemma-2-2B", + "nvidia/Nemotron-Content-Safety-Reasoning-4B", + "unsloth/gemma-3-1b-it", + "gghfez/gemma-3-4b-novision", + "robowaifudev/megatron-gpt2-345m", + "rinna/japanese-gpt-1b", + "EleutherAI/pythia-410m-seed1", + "cleanrl/EleutherAI_pythia-1b-deduped__sft__tldr", + "stabilityai/stablelm-tuned-alpha-3b", + "squ11z1/gpt-oss-nano", + "ibm-granite/granite-guardian-3.1-2b", + "ibm-granite/granite-3.1-3b-a800m-base", + "ibm-granite/granite-4.0-h-350m-base", + "ibm-granite/granite-4.0-h-tiny-base", + "ibm-granite/granite-4.0-micro-base", + "ai21labs/AI21-Jamba2-3B", + "jetmoe/jetmoe-8b", + "unsloth/LFM2.5-1.2B-Instruct", + "svc-nai-cci/nanollama-public", + "TheBloke/TinyLlama-1.1B-Chat-v1.0-AWQ", + "tiiuae/Falcon3-7B-Base", + "state-spaces/mamba-370m-hf", + "state-spaces/mamba-790m-hf", + "state-spaces/mamba-1.4b-hf", + "mii-llm/minerva-chat-v0.1-alpha-sft", + "AIChenKai/TinyLlama-1.1B-Chat-v1.0-x2-MoE", + "nvidia/Minitron-4B-Base", + "allenai/OLMo-2-0425-1B-RLVR1", + "yec019/fbopt-350m-8bit", + "facebook/galactica-1.3b", + "bharatgenai/Param2-17B-A2.4B-Thinking", + "Artples/L-MChat-Small", + "ReDiX/Artemide-3.5", + "unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit", + "TIGER-Lab/VLM2Vec-Full", + "Papaperez/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lanky_reptilian_opossum", + "unsloth/Qwen2.5-Coder-1.5B-bnb-4bit", + "unsloth/Qwen2.5-3B-unsloth-bnb-4bit", + "unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit", + "dicta-il/DictaLM-3.0-1.7B-Base", + "unsloth/Qwen3-4B-unsloth-bnb-4bit", + "Aratako/Qwen3-30B-A3B-NSFW-JP", + "vincentzed-hf/Qwen3-Coder-Next-NVFP4", + "google/recurrentgemma-2b-it", + "stabilityai/stablelm-2-zephyr-1_6b", + "stabilityai/stablelm-3b-4e1t", + "openbmb/MiniCPM-MoE-8x2B", + "Zyphra/Zamba2-1.2B-instruct", + "tiiuae/falcon-rw-1b", + "unsloth/gemma-2b-bnb-4bit", + "SeaLLMs/SeaLLM-7B-v2.5", + "unsloth/gemma-2-2b-it", + "unsloth/gemma-3-1b-it-bnb-4bit", + "hyper-accel/ci-random-gpt2-350m", + "EleutherAI/pythia-410m-seed3", + "rinna/japanese-gpt-neox-3.6b-instruction-sft-v2", + "unsloth/gpt-oss-20b-bnb-4bit", + "ibm-granite/granite-guardian-3.0-2b", + "ibm-granite/granite-3.0-3b-a800m-instruct", + "unsloth/granite-4.0-h-tiny", + "ibm-granite/granite-4.0-h-micro-base", + "LiquidAI/LFM2.5-1.2B-JP", + "hyper-accel/ci-random-llama2-7b", + "unsloth/tinyllama-chat" + ] + }, + "a100": { + "path": "configs/rand_chinese_stratified_100_a100.jsonl", + "count": 23, + "sha256": "709d8560ef63f2c3dd626a6e05ac413998850661e6343ff07d8779947fdcb4a4", + "models": [ + "swiss-ai/Apertus-8B-2509", + "tiiuae/Falcon-H1R-7B", + "Unbabel/Tower-Plus-9B", + "EleutherAI/polyglot-ko-5.8b", + "PygmalionAI/pygmalion-6b", + "tencent/Hunyuan-7B-Instruct", + "yaojialzc/Gigi-Llama3-8B-Chinese-zh", + "state-spaces/mamba-2.8b-hf", + "openbmb/MiniCPM4.1-8B-GPTQ", + "Artples/L-MChat-7b", + "hugging-quants/Mixtral-8x7B-Instruct-v0.1-AWQ-INT4", + "unsloth/Nemotron-3-Nano-30B-A3B", + "allenai/OLMo-7B-0724-Instruct-hf", + "allenai/Olmo-3-7B-RL-Zero-Math", + "allenai/Olmo-Hybrid-Instruct-SFT-7B", + "facebook/galactica-6.7b", + "TheBloke/Qwen-7B-Chat-AWQ", + "IQuestLab/Fleming-R1-7B", + "tiiuae/Falcon3-10B-Base", + "swiss-ai/Apertus-8B-Instruct-2509", + "tiiuae/Falcon-H1-7B-Instruct", + "ytu-ce-cosmos/Turkish-Gemma-9b-T1", + "EleutherAI/deep-ignorance-pretraining-stage-unfiltered" + ] + } +} From a893f37b8181ef65e22ad5713f48fd5897fced11 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Fri, 24 Jul 2026 18:17:23 +0800 Subject: [PATCH 20/28] Update primary robustness experiment to use a 100-model cohort and adjust related scripts --- docs/primary_robustness_experiment.tex | 109 ++++++++++------ .../build_primary_robustness_100_shards.py | 116 ++++++++++++++++++ scripts/run_primary_analysis.sh | 8 +- scripts/run_primary_robustness_100_shard.sh | 55 +++++++++ scripts/run_primary_robustness_experiment.sh | 12 +- 5 files changed, 250 insertions(+), 50 deletions(-) create mode 100644 scripts/build_primary_robustness_100_shards.py create mode 100644 scripts/run_primary_robustness_100_shard.sh diff --git a/docs/primary_robustness_experiment.tex b/docs/primary_robustness_experiment.tex index a3ff329..f85bbaa 100644 --- a/docs/primary_robustness_experiment.tex +++ b/docs/primary_robustness_experiment.tex @@ -28,7 +28,7 @@ \title{Primary LLM-DNA Robustness Experiment\\ \large Prospective Experimental Protocol and Report Template} \author{} -\date{July 21, 2026} +\date{July 24, 2026} \begin{document} \maketitle @@ -39,8 +39,8 @@ will measure how temperature and top-$p$ affect exact-model identification, whether a classifier can mitigate the resulting accuracy loss, and whether a multi-sample distributional representation improves robustness under a matched -generation budget. The primary cohort contains 300 models selected without -using evaluation performance. The experiment contains one deterministic +generation budget. The revised primary cohort contains 100 models selected +without using evaluation performance. The experiment contains one deterministic condition and a complete $4\times3$ stochastic decoding grid, with four independent generation repeats per condition. The principal deployment setting assumes that temperature and top-$p$ are hidden at inference time and only an @@ -63,6 +63,12 @@ \section{Status and scope} }} \end{center} +Before generation began, a resource-feasibility review on July 24, 2026 +reduced the planned cohort from 300 to 100 models. The decoding grid, four +independent repeats, train/test separation, prompts, and analysis plan remain +unchanged. Accordingly, exact identification is now a 100-way closed-set task, +and conclusions will be limited to the revised fixed cohort. + This experiment replaces the 77-model interim analysis as the primary robustness study. The earlier analysis remains a legacy diagnostic because it used an incomplete sweep, one generation repeat per reported cell, an @@ -109,25 +115,30 @@ \section{Model cohort} \subsection{Selection rule} -The planned cohort contains 300 Hugging Face models. It was selected from 470 +The planned cohort contains 100 Hugging Face models. It was selected from 470 eligible models with valid unsuffixed reference DNA artifacts and recorded parameter counts between 0.3 and 7.5 billion. Selection used seeded round-robin sampling over architecture $\times$ logarithmic parameter-size strata. It did not use retrieval accuracy, SVM accuracy, response quality, or any other test -outcome. Before launching the primary grid, three models that repeatedly -returned empty responses in the pilot control run were replaced with -similar-size models whose cached baseline runs each contained 100 non-empty -responses. These reliability replacements were documented in advance and did -not use evaluation accuracy. +outcome. Before launching the primary grid, one selected model that repeatedly +returned empty responses in the pilot control run was replaced with a +similar-size model whose cached baseline run contained 100 non-empty responses. +This reliability replacement was documented in advance and did not use +evaluation accuracy. The fixed artifacts are: \begin{itemize} - \item cohort: \path{configs/rand_chinese_stratified_300.jsonl}; - \item audit: \path{configs/rand_chinese_stratified_300_audit.json}; + \item cohort: \path{configs/rand_chinese_stratified_100.jsonl}; + \item cohort audit: \path{configs/rand_chinese_stratified_100_audit.json}; + \item hardware shards: + \path{configs/rand_chinese_stratified_100_rtx3090.jsonl} and + \path{configs/rand_chinese_stratified_100_a100.jsonl}; + \item shard audit: + \path{configs/rand_chinese_stratified_100_shards_audit.json}; \item selection seed: 42; and \item cohort SHA-256: - \texttt{4bc0dcebcd0e4d0e1240687fe981ac177a4306541b4813a943396b659cfe2661}. + \texttt{19b7c7b554af4e98edeb3ab279f52ccae66a9bddbff81c0c15f2fb464e895e6e}. \end{itemize} The audit file records the eligible population, selection method, selected @@ -139,10 +150,10 @@ \subsection{Selection rule} \subsection{Completion requirement} -The intended final analysis uses all 300 models. The automated primary analysis -will refuse to run if fewer than 250 models are common to all required training +The intended final analysis uses all 100 models. The automated primary analysis +will refuse to run if fewer than 80 models are common to all required training and test runs. Missing models should first be backfilled. If the final common -cohort remains below 300, the report will provide success rates and failure +cohort remains below 100, the report will provide success rates and failure reasons by decoding cell and compare retained and excluded models by size and architecture. @@ -184,8 +195,8 @@ \subsection{Factorial grid} This gives 13 decoding conditions in total. Four independent generation repeats are planned for every condition, giving 52 run cells and \[ - 300\ \text{models}\times13\ \text{conditions}\times4\ \text{repeats} - =15{,}600 + 100\ \text{models}\times13\ \text{conditions}\times4\ \text{repeats} + =5{,}200 \] model-level extraction tasks before retries. @@ -482,7 +493,7 @@ \subsection{Scope and split} The $T=0.5$ and $T=0.7$ cells were excluded because they did not have a sufficiently complete independent-repeat split. These demonstrations use convenience cohorts determined by artifact availability, not the fixed -300-model primary cohort. They are therefore pipeline checks and exploratory +100-model primary cohort. They are therefore pipeline checks and exploratory effect estimates only. \subsection{Executive summary} @@ -496,7 +507,7 @@ \subsection{Executive summary} highest equal-cell Top-1 (0.722), followed by RBF SVM (0.688) and cosine retrieval (0.621). The hardest observed cell was $(T,p)=(0.3,1.0)$. These are exploratory results from availability-selected cohorts and must not be read as -the outcome of the 300-model primary experiment. +the outcome of the 100-model primary experiment. }} \medskip @@ -757,7 +768,7 @@ \subsection{Demo conclusions and primary-study handoff} fixed in paired comparisons. \end{itemize} -Before any primary claim is made, the study still requires the fixed 300-model +Before any primary claim is made, the study still requires the fixed 100-model cohort, complete repeats 1--4 for all 13 conditions, the preregistered repeat-1/2 versus repeat-3/4 split, $K=4$ and $K=8$ response samples, sufficient direct and indirect model-tree pairs, and the planned encoder/projection @@ -780,17 +791,21 @@ \subsection{Demo conclusions and primary-study handoff} \section{Execution and resource plan} The generation launcher is -\path{scripts/run_primary_robustness_experiment.sh}. It uses the 300-model +\path{scripts/run_primary_robustness_experiment.sh}. It uses the 100-model cohort, four repeats, the 13-condition grid, isolated outputs under -\path{out/primary_robustness_300}, and isolated logs. The scheduler dynamically -uses visible GPUs up to ten concurrent workers unless an explicit disjoint GPU -list is supplied. +\path{out/primary_robustness_100}, and isolated logs. The hardware-shard wrapper +\path{scripts/run_primary_robustness_100_shard.sh} assigns the 23 largest +models to the two A100 GPUs and the remaining 77 models to the RTX 3090 fleet. +Both processes write model artifacts beneath the same experiment directory, +but use distinct run names, logs, status journals, and global-state files to +avoid concurrent-writer conflicts. The RTX 3090 scheduler uses up to four visible +GPUs; the A100 scheduler uses up to two. The current 100-model $T=0.5$ top-$p$ control experiment may continue as a -supplementary study. If both studies run simultaneously, they must use disjoint -GPU sets to avoid resource contention. Sequential execution is preferred -because the processes otherwise share Hugging Face model caches and cache -cleanup policies. +supplementary study. Its availability-selected cohort and existing artifacts +are not substituted into the revised primary cohort. The two primary shards +should be launched only after that control run finishes. If they share a model +cache, cache cleanup must not evict a model still required by the other shard. Transient failures are retried and recorded. Progress reports include planned, successful, failed, and missing models plus failure-status counts. Missing @@ -828,11 +843,11 @@ \subsection{Cohort and completion table} \toprule Condition & Planned & Successful & Failed & Common cohort \\ \midrule -$T=0$ & 300 & \todoresult & \todoresult & \todoresult \\ -$T=0.2$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ -$T=0.3$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ -$T=0.5$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ -$T=0.7$, all $p$ & 900 & \todoresult & \todoresult & \todoresult \\ +$T=0$ & 100 & \todoresult & \todoresult & \todoresult \\ +$T=0.2$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ +$T=0.3$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ +$T=0.5$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ +$T=0.7$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ \bottomrule \end{tabular} \end{table} @@ -942,15 +957,24 @@ \section{Execution commands} Build or verify the fixed cohort: \begin{lstlisting}[language=bash] -python3 scripts/build_experiment_cohort.py --target 300 +python3 scripts/build_experiment_cohort.py --target 100 \ + --output configs/rand_chinese_stratified_100.jsonl \ + --audit-output configs/rand_chinese_stratified_100_audit.json +python3 scripts/build_primary_robustness_100_shards.py \end{lstlisting} -Launch generation after the existing control run finishes, or assign a -disjoint GPU set: +After the existing control run finishes, launch the two hardware shards from +separate terminals. Each command dynamically uses the visible GPUs up to its +stated worker cap: \begin{lstlisting}[language=bash] -nohup bash scripts/run_primary_robustness_experiment.sh \ - > logs/primary_robustness_master.log 2>&1 & +mkdir -p logs && nohup env MAX_CONCURRENT_GPUS=4 \ + bash scripts/run_primary_robustness_100_shard.sh rtx3090 \ + > logs/primary_robustness_100_rtx3090_master.log 2>&1 & + +mkdir -p logs && nohup env MAX_CONCURRENT_GPUS=2 \ + bash scripts/run_primary_robustness_100_shard.sh a100 \ + > logs/primary_robustness_100_a100_master.log 2>&1 & \end{lstlisting} Run the preregistered exact-model and tree-aware analyses after all four repeats @@ -961,7 +985,7 @@ \section{Execution commands} \end{lstlisting} The primary analysis outputs are written beneath -\path{out/primary_robustness_300_analysis}. The exact-model evaluator produces +\path{out/primary_robustness_100_analysis}. The exact-model evaluator produces per-setting metrics, per-query predictions, bounded-deployment tables, cohort metadata, and JSON summaries. @@ -977,7 +1001,12 @@ \section{Implementation map} \midrule \endhead \path{scripts/run_primary_robustness_experiment.sh} - & Launch the 300-model, 13-condition, four-repeat extraction sweep. \\ + & Launch the 100-model, 13-condition, four-repeat extraction sweep. \\ +\path{scripts/run_primary_robustness_100_shard.sh} + & Run the collision-safe RTX 3090 or A100 hardware shard. \\ +\path{scripts/build_primary_robustness_100_shards.py} + & Assign 77 models to RTX 3090 workers and the 23 largest models to A100 + workers; record shard membership and hashes. \\ \path{scripts/run_primary_analysis.sh} & Construct the fixed train/test repeat split and run primary analyses. \\ \path{scripts/evaluate_exact_dna_classification.py} diff --git a/scripts/build_primary_robustness_100_shards.py b/scripts/build_primary_robustness_100_shards.py new file mode 100644 index 0000000..b797aac --- /dev/null +++ b/scripts/build_primary_robustness_100_shards.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""Split the fixed 100-model primary cohort across RTX 3090 and A100 workers.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + + +def load_jsonl(path: Path) -> list[dict[str, object]]: + rows = [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + model_ids = [str(row.get("model_id", "")).strip() for row in rows] + if len(rows) != 100: + raise ValueError(f"Expected exactly 100 cohort rows, found {len(rows)}.") + if any(not model_id for model_id in model_ids): + raise ValueError("Every cohort row must contain a non-empty model_id.") + if len(set(model_ids)) != len(model_ids): + raise ValueError("The cohort contains duplicate model IDs.") + return rows + + +def write_jsonl(path: Path, rows: list[dict[str, object]]) -> str: + path.parent.mkdir(parents=True, exist_ok=True) + content = "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in rows) + path.write_text(content, encoding="utf-8") + return hashlib.sha256(content.encode("utf-8")).hexdigest() + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Assign the largest primary models to A100s and the remainder to " + "RTX 3090s." + ) + ) + parser.add_argument( + "--cohort", + type=Path, + default=Path("configs/rand_chinese_stratified_100.jsonl"), + ) + parser.add_argument("--a100-models", type=int, default=23) + parser.add_argument( + "--rtx3090-output", + type=Path, + default=Path("configs/rand_chinese_stratified_100_rtx3090.jsonl"), + ) + parser.add_argument( + "--a100-output", + type=Path, + default=Path("configs/rand_chinese_stratified_100_a100.jsonl"), + ) + parser.add_argument( + "--audit-output", + type=Path, + default=Path("configs/rand_chinese_stratified_100_shards_audit.json"), + ) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + rows = load_jsonl(args.cohort) + if not 0 < args.a100_models < len(rows): + raise ValueError("--a100-models must be between 1 and 99.") + + ranked = sorted( + rows, + key=lambda row: ( + -float(row.get("parameter_count_billions", 0.0) or 0.0), + str(row["model_id"]), + ), + ) + a100_ids = {str(row["model_id"]) for row in ranked[: args.a100_models]} + a100_rows = [row for row in rows if str(row["model_id"]) in a100_ids] + rtx3090_rows = [row for row in rows if str(row["model_id"]) not in a100_ids] + + rtx3090_sha = write_jsonl(args.rtx3090_output, rtx3090_rows) + a100_sha = write_jsonl(args.a100_output, a100_rows) + cohort_sha = hashlib.sha256(args.cohort.read_bytes()).hexdigest() + audit = { + "cohort": str(args.cohort), + "cohort_sha256": cohort_sha, + "assignment_rule": ( + f"the {args.a100_models} largest models by recorded " + "parameter_count_billions go to the two A100 GPUs; all remaining " + "models go to the four-GPU RTX 3090 fleet" + ), + "rtx3090": { + "path": str(args.rtx3090_output), + "count": len(rtx3090_rows), + "sha256": rtx3090_sha, + "models": [row["model_id"] for row in rtx3090_rows], + }, + "a100": { + "path": str(args.a100_output), + "count": len(a100_rows), + "sha256": a100_sha, + "models": [row["model_id"] for row in a100_rows], + }, + } + args.audit_output.write_text( + json.dumps(audit, indent=2, ensure_ascii=False) + "\n", + encoding="utf-8", + ) + print(json.dumps(audit, indent=2, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_primary_analysis.sh b/scripts/run_primary_analysis.sh index 7517b38..32c5d81 100644 --- a/scripts/run_primary_analysis.sh +++ b/scripts/run_primary_analysis.sh @@ -1,15 +1,15 @@ #!/usr/bin/env bash # Analyze the completed primary sweep. Repeats 1-2 are training/gallery data; -# repeats 3-4 are held-out queries. The command refuses cohorts below 250. +# repeats 3-4 are held-out queries. The command refuses cohorts below 80. set -euo pipefail ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" cd "$ROOT" -DATA_DIR="${DATA_DIR:-out/primary_robustness_300/rand_chinese}" -ANALYSIS_ROOT="${ANALYSIS_ROOT:-out/primary_robustness_300_analysis}" -MINIMUM_MODELS="${MINIMUM_MODELS:-250}" +DATA_DIR="${DATA_DIR:-out/primary_robustness_100/rand_chinese}" +ANALYSIS_ROOT="${ANALYSIS_ROOT:-out/primary_robustness_100_analysis}" +MINIMUM_MODELS="${MINIMUM_MODELS:-80}" PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" if [[ ! -x "$PYTHON_BIN" ]]; then PYTHON_BIN="$(command -v python3)" diff --git a/scripts/run_primary_robustness_100_shard.sh b/scripts/run_primary_robustness_100_shard.sh new file mode 100644 index 0000000..617d01d --- /dev/null +++ b/scripts/run_primary_robustness_100_shard.sh @@ -0,0 +1,55 @@ +#!/usr/bin/env bash +# Run one hardware shard of the fixed 100-model primary experiment. +# +# Both shards intentionally share OUTPUT_DIR so the completed dataset is +# analysis-ready. Status/state journals and scheduler run names are isolated to +# avoid concurrent writers clobbering each other. + +set -euo pipefail + +if [[ $# -ne 1 ]]; then + echo "Usage: $0 {rtx3090|a100}" >&2 + exit 2 +fi + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +SHARD="$1" +case "$SHARD" in + rtx3090) + HF_JSONL="configs/rand_chinese_stratified_100_rtx3090.jsonl" + EXPECTED_MODELS=77 + export MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-4}" + ;; + a100) + HF_JSONL="configs/rand_chinese_stratified_100_a100.jsonl" + EXPECTED_MODELS=23 + export MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-2}" + ;; + *) + echo "Unknown shard '$SHARD'; expected rtx3090 or a100." >&2 + exit 2 + ;; +esac + +if [[ ! -f "$HF_JSONL" ]]; then + echo "Missing fixed shard file: $HF_JSONL" >&2 + exit 1 +fi +ACTUAL_MODELS="$(wc -l < "$HF_JSONL")" +if [[ "$ACTUAL_MODELS" -ne "$EXPECTED_MODELS" ]]; then + echo "Invalid $SHARD shard: expected $EXPECTED_MODELS rows, found $ACTUAL_MODELS." >&2 + exit 1 +fi + +# These values are deliberately forced so stale exports from the superseded +# 300-model experiment cannot redirect a shard into the old cohort or outputs. +export HF_JSONL +export OUTPUT_DIR="out/primary_robustness_100" +export SWEEP_NAME="primary_robustness_100_${SHARD}" +export LOG_DIR="${LOG_DIR:-logs/primary_robustness_100_${SHARD}_$(date +%Y%m%d-%H%M%S)}" +export GLOBAL_STATUS_JOURNAL="$OUTPUT_DIR/dna_global_status_${SHARD}.jsonl" +export GLOBAL_STATE="$OUTPUT_DIR/dna_global_state_${SHARD}.json" + +exec bash scripts/run_primary_robustness_experiment.sh diff --git a/scripts/run_primary_robustness_experiment.sh b/scripts/run_primary_robustness_experiment.sh index 2126b66..a188c23 100644 --- a/scripts/run_primary_robustness_experiment.sh +++ b/scripts/run_primary_robustness_experiment.sh @@ -1,17 +1,17 @@ #!/usr/bin/env bash -# Primary exact-model robustness dataset: 300 models, a complete factorial -# decoding grid, and three independent generation seeds. T=0 is emitted once. +# Primary exact-model robustness dataset: 100 models, a complete factorial +# decoding grid, and four independent generation seeds. T=0 is emitted once. set -euo pipefail ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" cd "$ROOT" -export HF_JSONL="${HF_JSONL:-configs/rand_chinese_stratified_300.jsonl}" -export OUTPUT_DIR="${OUTPUT_DIR:-out/primary_robustness_300}" +export HF_JSONL="${HF_JSONL:-configs/rand_chinese_stratified_100.jsonl}" +export OUTPUT_DIR="${OUTPUT_DIR:-out/primary_robustness_100}" export DATASET="${DATASET:-rand_chinese}" -export SWEEP_NAME="${SWEEP_NAME:-primary_robustness_300}" -export LOG_DIR="${LOG_DIR:-logs/primary_robustness_300_$(date +%Y%m%d-%H%M%S)}" +export SWEEP_NAME="${SWEEP_NAME:-primary_robustness_100}" +export LOG_DIR="${LOG_DIR:-logs/primary_robustness_100_$(date +%Y%m%d-%H%M%S)}" export TEMPERATURES="0.2 0.3 0.5 0.7" export TOP_PS="0.8 0.9 1.0" From a56f3fbf5a7a30122cb34037caa28ded2903ed50 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Sun, 26 Jul 2026 14:42:47 +0800 Subject: [PATCH 21/28] Update run_temperature_control_p09_100_shard.sh --- scripts/build_experiment_inventory.py | 384 ++++++++++++++++++ scripts/build_sampling_settings_summary.py | 285 +++++++++++++ .../run_temperature_control_p09_100_shard.sh | 74 ++++ 3 files changed, 743 insertions(+) create mode 100644 scripts/build_experiment_inventory.py create mode 100644 scripts/build_sampling_settings_summary.py create mode 100755 scripts/run_temperature_control_p09_100_shard.sh diff --git a/scripts/build_experiment_inventory.py b/scripts/build_experiment_inventory.py new file mode 100644 index 0000000..d205940 --- /dev/null +++ b/scripts/build_experiment_inventory.py @@ -0,0 +1,384 @@ +#!/usr/bin/env python3 +"""Build a reproducible inventory of experiment manifests and final statuses.""" + +from __future__ import annotations + +import csv +import json +from collections import Counter +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] +REPORT_DIR = ROOT / "reports" +MANIFEST_NAME = "manifest.json" +STATUS_NAME = "status.jsonl" + +PARAMETER_FIELDS = [ + "dataset", + "max_samples", + "providers", + "max_model_size_b", + "resume_mode", + "temperature", + "top_p", + "do_sample", + "generation_seed", + "ignore_response_cache", + "output_suffix", + "model_timeout_seconds", + "max_retries", + "oom_retries", + "retry_delay_seconds", + "gpu_memory_headroom_gb", + "gpu_memory_per_billion_gb", +] + + +def relative(path: Path) -> str: + return path.resolve().relative_to(ROOT).as_posix() + + +def load_json(path: Path) -> dict[str, Any]: + with path.open(encoding="utf-8") as handle: + return json.load(handle) + + +def load_statuses(path: Path) -> list[dict[str, Any]]: + records: list[dict[str, Any]] = [] + if not path.exists(): + return records + with path.open(encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + if isinstance(record, dict) and record.get("model_id"): + records.append(record) + return records + + +def task_identity(task: dict[str, Any]) -> str: + provider = task.get("provider") + model_id = task.get("model_id", "") + return f"{provider}:{model_id}" if provider else str(model_id) + + +def model_label(identity: str) -> str: + return identity.split(":", 1)[1] if ":" in identity else identity + + +def final_statuses( + records: list[dict[str, Any]], planned: set[str] +) -> dict[str, dict[str, Any]]: + """Keep the last record per model, matching provider-less legacy statuses.""" + by_model_id = {model_label(identity): identity for identity in planned} + final: dict[str, dict[str, Any]] = {} + for record in records: + identity = task_identity(record) + if identity not in planned: + identity = by_model_id.get(str(record.get("model_id")), identity) + final[identity] = record + return final + + +def status_bucket(status: str) -> str: + if status == "success": + return "success" + if status.startswith("failed"): + return "fail" + if status.startswith("skipped"): + return "skipped" + return "other" + + +def compact_parameters(manifest: dict[str, Any]) -> str: + parts: list[str] = [] + aliases = { + "max_samples": "n", + "providers": "provider", + "max_model_size_b": "max_B", + "resume_mode": "resume", + "temperature": "temp", + "generation_seed": "seed", + "ignore_response_cache": "no_cache", + "model_timeout_seconds": "timeout_s", + "retry_delay_seconds": "retry_delay_s", + "gpu_memory_headroom_gb": "gpu_headroom_GB", + "gpu_memory_per_billion_gb": "gpu_GB_per_B", + } + for field in PARAMETER_FIELDS: + if field in manifest: + value = manifest[field] + if isinstance(value, bool): + value = str(value).lower() + parts.append(f"{aliases.get(field, field)}={value}") + return "; ".join(parts) + + +def diff_summary(added: list[str], removed: list[str]) -> str: + if not added and not removed: + return "BASE (same 100)" + count = len(added) + len(removed) + if count <= 4: + pieces = [f"+{model_label(item)}" for item in added] + pieces += [f"−{model_label(item)}" for item in removed] + return "; ".join(pieces) + return f"+{len(added)} / −{len(removed)} (see model diffs CSV)" + + +def write_csv(path: Path, rows: list[dict[str, Any]], fields: list[str]) -> None: + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fields, extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + +def main() -> None: + manifests: list[tuple[Path, dict[str, Any]]] = [] + for path in sorted(ROOT.rglob(MANIFEST_NAME)): + if ".git" in path.parts: + continue + try: + manifests.append((path, load_json(path))) + except (OSError, json.JSONDecodeError): + continue + + model_sets = [ + frozenset(task_identity(task) for task in data.get("tasks", [])) + for _, data in manifests + ] + frequencies = Counter(model_sets) + baseline = set(frequencies.most_common(1)[0][0]) + baseline_frequency = frequencies.most_common(1)[0][1] + + rows: list[dict[str, Any]] = [] + diff_rows: list[dict[str, Any]] = [] + manifest_dirs: set[Path] = set() + + for manifest_path, manifest in manifests: + run_dir = manifest_path.parent + manifest_dirs.add(run_dir.resolve()) + planned = { + task_identity(task) + for task in manifest.get("tasks", []) + if task.get("model_id") + } + records = load_statuses(run_dir / STATUS_NAME) + final = final_statuses(records, planned) + buckets = Counter( + status_bucket(str(record.get("status", ""))) for record in final.values() + ) + observed_planned = len(set(final) & planned) + observed_total = len(final) + extra_observed = len(set(final) - planned) + unobserved = max(0, len(planned) - observed_planned) + added = sorted(planned - baseline) + removed = sorted(baseline - planned) + + known_fields = set(PARAMETER_FIELDS) | { + "created_at", + "tasks", + "task_count", + } + extra_parameters = { + key: value for key, value in manifest.items() if key not in known_fields + } + run_name = relative(run_dir) + row = { + "created_at": manifest.get("created_at", ""), + "run": run_name, + "models": len(planned), + "observed": observed_total, + "extra_observed": extra_observed, + "success": buckets["success"], + "fail": buckets["fail"], + "skipped": buckets["skipped"], + "other_final_status": buckets["other"], + "unobserved": unobserved, + "model_diff": diff_summary(added, removed), + "parameters": compact_parameters(manifest), + "manifest": relative(manifest_path), + "status_log": ( + relative(run_dir / STATUS_NAME) + if (run_dir / STATUS_NAME).exists() + else "" + ), + "extra_parameters_json": json.dumps( + extra_parameters, ensure_ascii=False, sort_keys=True + ), + } + for field in PARAMETER_FIELDS: + row[field] = manifest.get(field, "") + rows.append(row) + diff_rows.append( + { + "run": run_name, + "is_baseline": not added and not removed, + "model_count": len(planned), + "added_count": len(added), + "removed_count": len(removed), + "added_models": ";".join(added), + "removed_models": ";".join(removed), + } + ) + + # Include legacy status-only runs so "all runs" is not limited to manifests. + for status_path in sorted(ROOT.rglob(STATUS_NAME)): + if ".git" in status_path.parts or status_path.parent.resolve() in manifest_dirs: + continue + records = load_statuses(status_path) + final = final_statuses(records, set()) + models = set(final) + buckets = Counter( + status_bucket(str(record.get("status", ""))) for record in final.values() + ) + summary_path = status_path.parent / "summary.json" + summary = load_json(summary_path) if summary_path.exists() else {} + added = sorted(models - baseline) + removed = sorted(baseline - models) + row = { + "created_at": summary.get("timestamp", ""), + "run": relative(status_path.parent), + "models": len(models), + "observed": len(models), + "extra_observed": 0, + "success": buckets["success"], + "fail": buckets["fail"], + "skipped": buckets["skipped"], + "other_final_status": buckets["other"], + "unobserved": 0, + "model_diff": diff_summary(added, removed), + "parameters": "; ".join( + f"{key}={summary[key]}" + for key in ("dataset", "samples", "gpus") + if key in summary + ), + "manifest": "", + "status_log": relative(status_path), + "extra_parameters_json": json.dumps( + summary, ensure_ascii=False, sort_keys=True + ), + } + for field in PARAMETER_FIELDS: + row[field] = "" + rows.append(row) + diff_rows.append( + { + "run": row["run"], + "is_baseline": False, + "model_count": len(models), + "added_count": len(added), + "removed_count": len(removed), + "added_models": ";".join(added), + "removed_models": ";".join(removed), + } + ) + + rows.sort(key=lambda row: (str(row["created_at"]), str(row["run"]))) + diff_rows.sort(key=lambda row: str(row["run"])) + REPORT_DIR.mkdir(exist_ok=True) + + csv_fields = [ + "created_at", + "run", + *PARAMETER_FIELDS, + "models", + "observed", + "extra_observed", + "success", + "fail", + "skipped", + "other_final_status", + "unobserved", + "model_diff", + "parameters", + "manifest", + "status_log", + "extra_parameters_json", + ] + write_csv(REPORT_DIR / "experiment_inventory.csv", rows, csv_fields) + write_csv( + REPORT_DIR / "experiment_model_diffs.csv", + diff_rows, + [ + "run", + "is_baseline", + "model_count", + "added_count", + "removed_count", + "added_models", + "removed_models", + ], + ) + with (REPORT_DIR / "experiment_baseline_models.txt").open( + "w", encoding="utf-8" + ) as handle: + handle.write("\n".join(sorted(baseline)) + "\n") + + markdown = [ + "# Experiment inventory", + "", + ( + f"Generated from {len(manifests)} manifests plus " + f"{len(rows) - len(manifests)} status-only run(s). " + f"The baseline is the modal exact model cohort: {len(baseline)} models, " + f"used by {baseline_frequency} manifests." + ), + "", + ( + "`success`, `fail`, `skipped`, and `other` count each model's final " + "status record (retry records are collapsed to the last record). " + "`unobserved` means a model was planned in the manifest but has no " + "status record; `extra` means a status model was not in the manifest. " + "A missing status log therefore does not become a failure." + ), + "", + "| Date | Run | Parameters | Planned models | Status models | Success | Fail | Skipped | Other | Unobserved | Extra | Model diff vs baseline |", + "|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---|", + ] + for row in rows: + created = str(row["created_at"])[:19].replace("T", " ") + markdown.append( + "| {date} | `{run}` | {params} | {models} | {observed} | {success} | " + "{fail} | {skipped} | {other} | {unobserved} | {extra} | {diff} |".format( + date=created or "unknown", + run=row["run"], + params=str(row["parameters"]).replace("|", "\\|"), + models=row["models"], + observed=row["observed"], + success=row["success"], + fail=row["fail"], + skipped=row["skipped"], + other=row["other_final_status"], + unobserved=row["unobserved"], + extra=row["extra_observed"], + diff=str(row["model_diff"]).replace("|", "\\|"), + ) + ) + markdown += [ + "", + "Companion files:", + "", + "- `experiment_inventory.csv`: all manifest parameter columns and source paths.", + "- `experiment_model_diffs.csv`: full added/removed model lists for every run.", + "- `experiment_baseline_models.txt`: the exact 100-model baseline cohort.", + "", + ] + (REPORT_DIR / "experiment_inventory.md").write_text( + "\n".join(markdown), encoding="utf-8" + ) + + print( + f"Wrote {len(rows)} runs; baseline={len(baseline)} models " + f"across {baseline_frequency} manifests" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/build_sampling_settings_summary.py b/scripts/build_sampling_settings_summary.py new file mode 100644 index 0000000..cbbc8d1 --- /dev/null +++ b/scripts/build_sampling_settings_summary.py @@ -0,0 +1,285 @@ +#!/usr/bin/env python3 +"""Summarize temperature/top-p experiments by setting rather than run.""" + +from __future__ import annotations + +import csv +import json +import re +from collections import defaultdict +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] +REPORT_DIR = ROOT / "reports" + + +def load_json(path: Path) -> dict[str, Any]: + with path.open(encoding="utf-8") as handle: + return json.load(handle) + + +def cohort_for(path: Path) -> tuple[int, str]: + text = path.as_posix() + if "top_p_control_t05_validated100_20260719" in text: + return 3, "Top-p control — validated 100" + if "top_p_control_t05_20260717" in text: + return 2, "Top-p control — pilot" + return 1, "Temperature/top-p sweep" + + +def repeat_for(manifest: dict[str, Any], path: Path) -> str: + suffix = str(manifest.get("output_suffix", "")) + match = re.search(r"_r(\d+)$", suffix) + if match: + return f"r{match.group(1)}" + if path.parent.name == "rand_chinese_0p3b_7b_temp07": + return "prelim" + return "run" + + +def read_final_statuses(path: Path) -> dict[tuple[str, str], str]: + final: dict[tuple[str, str], str] = {} + if not path.exists(): + return final + with path.open(encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + model_id = record.get("model_id") + if model_id: + final[(str(record.get("provider", "")), str(model_id))] = str( + record.get("status", "") + ) + return final + + +def classify(status: str) -> str: + if status == "success": + return "success" + if status.startswith("failed") or status == "incompatible": + return "fail" + if status.startswith("skipped"): + return "skipped" + return "pending" + + +def outcome_for(path: Path, manifest: dict[str, Any]) -> dict[str, Any]: + planned = { + (str(task.get("provider", "")), str(task["model_id"])) + for task in manifest.get("tasks", []) + if task.get("model_id") + } + final = read_final_statuses(path.parent / "status.jsonl") + # Only count the cohort declared by the manifest. Stale status records from + # models outside the cohort are tracked separately. + current = {identity: status for identity, status in final.items() if identity in planned} + counts = defaultdict(int) + for status in current.values(): + counts[classify(status)] += 1 + unobserved = len(planned - set(current)) + pending = counts["pending"] + unobserved + terminal = counts["success"] + counts["fail"] + counts["skipped"] + complete = terminal == len(planned) and pending == 0 + fully_successful = complete and counts["success"] == len(planned) + return { + "repeat": repeat_for(manifest, path), + "planned": len(planned), + "success": counts["success"], + "fail": counts["fail"], + "skipped": counts["skipped"], + "pending": pending, + "unobserved": unobserved, + "extra_status_models": len(set(final) - planned), + "complete": complete, + "fully_successful": fully_successful, + "seed": manifest.get("generation_seed", ""), + "created_at": manifest.get("created_at", ""), + "source": path.parent.resolve().relative_to(ROOT).as_posix(), + } + + +def format_outcomes(outcomes: list[dict[str, Any]], field: str) -> str: + return "; ".join( + f"{outcome['repeat']} {outcome[field]}/{outcome['planned']}" + for outcome in outcomes + ) + + +def setting_state(outcomes: list[dict[str, Any]]) -> str: + if all(outcome["fully_successful"] for outcome in outcomes): + return "✅ 100% successful" + if all(outcome["complete"] for outcome in outcomes): + return "✅ Complete" + complete = sum(bool(outcome["complete"]) for outcome in outcomes) + return f"⏳ Incomplete ({complete}/{len(outcomes)} repeats complete)" + + +def grid_cell(row: dict[str, Any]) -> str: + outcomes = json.loads(str(row["details_json"])) + values = "/".join(str(outcome["success"]) for outcome in outcomes) + return values if all(outcome["complete"] for outcome in outcomes) else f"{values}*" + + +def append_grid( + markdown: list[str], title: str, rows: list[dict[str, Any]] +) -> None: + temperatures = sorted({float(row["temperature"]) for row in rows}) + top_ps = sorted({float(row["top_p"]) for row in rows}) + lookup = { + (float(row["temperature"]), float(row["top_p"])): grid_cell(row) + for row in rows + } + markdown += [ + f"## {title}", + "", + "| Temperature \\\\ Top-p | " + + " | ".join(f"{top_p:g}" for top_p in top_ps) + + " |", + "|---:|" + "|".join("---:" for _ in top_ps) + "|", + ] + for temperature in temperatures: + cells = [ + lookup.get((temperature, top_p), "—") + for top_p in top_ps + ] + markdown.append( + f"| {temperature:g} | " + " | ".join(cells) + " |" + ) + markdown.append("") + + +def main() -> None: + grouped: dict[ + tuple[int, str, float, float], list[dict[str, Any]] + ] = defaultdict(list) + for path in sorted(ROOT.rglob("manifest.json")): + if ".git" in path.parts: + continue + try: + manifest = load_json(path) + except (OSError, json.JSONDecodeError): + continue + if "temperature" not in manifest or "top_p" not in manifest: + continue + order, cohort = cohort_for(path) + temperature = float(manifest["temperature"]) + top_p = float(manifest["top_p"]) + grouped[(order, cohort, temperature, top_p)].append( + outcome_for(path, manifest) + ) + + rows: list[dict[str, Any]] = [] + for (order, cohort, temperature, top_p), outcomes in sorted(grouped.items()): + outcomes.sort(key=lambda item: (item["repeat"], item["created_at"])) + rows.append( + { + "order": order, + "experiment": cohort, + "temperature": temperature, + "top_p": top_p, + "repeats": len(outcomes), + "successful_models": format_outcomes(outcomes, "success"), + "failed_models": format_outcomes(outcomes, "fail"), + "pending_models": format_outcomes(outcomes, "pending"), + "status": setting_state(outcomes), + "seeds": "; ".join( + f"{item['repeat']} {item['seed'] if item['seed'] != '' else 'not recorded'}" + for item in outcomes + ), + "sources": ";".join(item["source"] for item in outcomes), + "details_json": json.dumps(outcomes, ensure_ascii=False), + } + ) + + REPORT_DIR.mkdir(exist_ok=True) + csv_path = REPORT_DIR / "sampling_settings_summary.csv" + fields = [ + "experiment", + "temperature", + "top_p", + "repeats", + "successful_models", + "failed_models", + "pending_models", + "status", + "seeds", + "sources", + "details_json", + ] + with csv_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fields, extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + markdown = [ + "# Temperature and top-p settings", + "", + ( + f"{len(rows)} distinct settings are shown. Repeated executions of the " + "same setting are combined in one row. Counts use each planned model's " + "latest status; status records outside the manifest cohort are excluded." + ), + "", + ( + "A setting is **Complete** when every planned model in every repeat has " + "a terminal success/failure result. **Incomplete** means at least one " + "model is pending, waiting to retry, or has no status record." + ), + "", + "## Success-count grids", + "", + ( + "Each cell is the number of successful models out of 100. Two values " + "mean `r1/r2`; `*` marks an incomplete setting; `—` means not tested." + ), + "", + ] + for cohort in ( + "Temperature/top-p sweep", + "Top-p control — pilot", + "Top-p control — validated 100", + ): + append_grid( + markdown, + cohort, + [row for row in rows if row["experiment"] == cohort], + ) + markdown += ["## Per-setting details", ""] + last_cohort = "" + for row in rows: + if row["experiment"] != last_cohort: + if last_cohort: + markdown.append("") + last_cohort = str(row["experiment"]) + markdown += [ + f"### {last_cohort}", + "", + "| Temperature | Top-p | Repeats | Successful models | Failed models | Pending models | Current status |", + "|---:|---:|---:|---|---|---|---|", + ] + markdown.append( + f"| {row['temperature']:g} | {row['top_p']:g} | {row['repeats']} | " + f"{row['successful_models']} | {row['failed_models']} | " + f"{row['pending_models']} | {row['status']} |" + ) + markdown += [ + "", + "The companion CSV includes seeds, source directories, unobserved counts, " + "and the complete per-repeat details.", + "", + ] + (REPORT_DIR / "sampling_settings_summary.md").write_text( + "\n".join(markdown), encoding="utf-8" + ) + print(f"Wrote {len(rows)} distinct sampling settings from {sum(r['repeats'] for r in rows)} repeats") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_temperature_control_p09_100_shard.sh b/scripts/run_temperature_control_p09_100_shard.sh new file mode 100755 index 0000000..d454a2b --- /dev/null +++ b/scripts/run_temperature_control_p09_100_shard.sh @@ -0,0 +1,74 @@ +#!/usr/bin/env bash +# Fixed-top-p temperature experiment over the exact primary 100-model cohort. +# Temperatures run sequentially from low to high; each has four generation seeds. + +set -euo pipefail + +if [[ $# -ne 1 ]]; then + echo "Usage: $0 {rtx3090|a100}" >&2 + exit 2 +fi + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +SHARD="$1" +case "$SHARD" in + rtx3090) + HF_JSONL="configs/rand_chinese_stratified_100_rtx3090.jsonl" + EXPECTED_MODELS=77 + export MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-4}" + ;; + a100) + HF_JSONL="configs/rand_chinese_stratified_100_a100.jsonl" + EXPECTED_MODELS=23 + export MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-2}" + ;; + *) + echo "Unknown shard '$SHARD'; expected rtx3090 or a100." >&2 + exit 2 + ;; +esac + +if [[ ! -f "$HF_JSONL" ]]; then + echo "Missing fixed shard file: $HF_JSONL" >&2 + exit 1 +fi +ACTUAL_MODELS="$(wc -l < "$HF_JSONL")" +if [[ "$ACTUAL_MODELS" -ne "$EXPECTED_MODELS" ]]; then + echo "Invalid $SHARD shard: expected $EXPECTED_MODELS rows, found $ACTUAL_MODELS." >&2 + exit 1 +fi + +EXPERIMENT_NAME="temperature_control_p09_100" +export HF_JSONL +export OUTPUT_DIR="out/$EXPERIMENT_NAME" +export DATASET="rand_chinese" +export SWEEP_NAME="${EXPERIMENT_NAME}_${SHARD}" +export LOG_DIR="${LOG_DIR:-logs/${EXPERIMENT_NAME}_${SHARD}_$(date +%Y%m%d-%H%M%S)}" +export GLOBAL_STATUS_JOURNAL="$OUTPUT_DIR/dna_global_status_${SHARD}.jsonl" +export GLOBAL_STATE="$OUTPUT_DIR/dna_global_state_${SHARD}.json" + +# Experimental variables and controls. The sweep runner preserves this order. +export TEMPERATURES="0.2 0.3 0.5 0.7" +export TOP_PS="0.9" +export REPEATS="${REPEATS:-1 2 3 4}" +export GENERATION_SEED_BASE="${GENERATION_SEED_BASE:-42000}" +export INCLUDE_T00="0" + +export GPUS="${GPUS:-}" +export LIMIT="0" +export RESUME_MODE="all" +export TRY_VLLM="${TRY_VLLM:-1}" +export CACHE_EVICT_FINISHED="${CACHE_EVICT_FINISHED:-success}" + +echo "Experiment: $EXPERIMENT_NAME" +echo "Shard: $SHARD ($ACTUAL_MODELS models)" +echo "Fixed top_p: $TOP_PS" +echo "Temperatures (execution order): $TEMPERATURES" +echo "Repeats: $REPEATS" +echo "Generation seeds: 42001 42002 42003 42004" +echo "Output directory: $OUTPUT_DIR" +echo "Log directory: $LOG_DIR" + +exec bash scripts/run_dna_temperature_top_p_sweep.sh From dbf58425e9fded6b169c97b100d55c7b6c93b7a7 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Mon, 10 Aug 2026 20:42:43 +0800 Subject: [PATCH 22/28] Add validated robustness experiment workflow --- .../rand_chinese_validated100_latest.jsonl | 100 ++ docs/primary_robustness_experiment.tex | 1138 +++-------------- ...ess_experiment_full_updated_2026-08-05.tex | 1138 +++++++++++++++++ docs/weekly_phd_update_2026-08-05.tex | 120 ++ reports/primary_robustness_run_grid.csv | 27 + reports/primary_robustness_run_grid.md | 65 + scripts/build_primary_run_grid.py | 338 +++++ scripts/build_validated100_config.py | 47 + scripts/resume_primary_robustness_grid.sh | 182 +++ scripts/resume_validated_top_p_control.sh | 90 ++ scripts/run_primary_robustness_experiment.sh | 4 +- .../run_temperature_control_p09_100_shard.sh | 6 +- src/llm_dna/api.py | 74 +- src/llm_dna/cli.py | 36 + src/llm_dna/models/ModelWrapper.py | 4 +- 15 files changed, 2427 insertions(+), 942 deletions(-) create mode 100644 configs/rand_chinese_validated100_latest.jsonl create mode 100644 docs/primary_robustness_experiment_full_updated_2026-08-05.tex create mode 100644 docs/weekly_phd_update_2026-08-05.tex create mode 100644 reports/primary_robustness_run_grid.csv create mode 100644 reports/primary_robustness_run_grid.md create mode 100644 scripts/build_primary_run_grid.py create mode 100755 scripts/build_validated100_config.py create mode 100644 scripts/resume_primary_robustness_grid.sh create mode 100755 scripts/resume_validated_top_p_control.sh diff --git a/configs/rand_chinese_validated100_latest.jsonl b/configs/rand_chinese_validated100_latest.jsonl new file mode 100644 index 0000000..15d2d31 --- /dev/null +++ b/configs/rand_chinese_validated100_latest.jsonl @@ -0,0 +1,100 @@ +{"model_id": "ibm-granite/granite-4.0-h-350m-base", "provider": "huggingface"} +{"model_id": "EleutherAI/pythia-410m-seed2", "provider": "huggingface"} +{"model_id": "numind/NuExtract-1.5-tiny", "provider": "huggingface"} +{"model_id": "unsloth/Qwen2.5-0.5B", "provider": "huggingface"} +{"model_id": "tiiuae/Falcon-H1-0.5B-Base", "provider": "huggingface"} +{"model_id": "unsloth/gemma-3-1b-it", "provider": "huggingface"} +{"model_id": "LiquidAI/LFM2-1.2B-Extract", "provider": "huggingface"} +{"model_id": "Azurro/APT3-1B-Base", "provider": "huggingface"} +{"model_id": "ibm-granite/granite-3.1-3b-a800m-base", "provider": "huggingface"} +{"model_id": "akhauriyash/DeepSeek-R1-Distill-Qwen-1.5B-SpeculativeReasoner", "provider": "huggingface"} +{"model_id": "SakanaAI/TinySwallow-1.5B", "provider": "huggingface"} +{"model_id": "unsloth/Qwen2.5-1.5B", "provider": "huggingface"} +{"model_id": "Vinnnf/Thinkless-1.5B-RL-DeepScaleR", "provider": "huggingface"} +{"model_id": "stefanruseti/newsvibe-stance-llama-1b", "provider": "huggingface"} +{"model_id": "Jiayi-Pan/Tiny-Vicuna-1B", "provider": "huggingface"} +{"model_id": "unsloth/Qwen2.5-0.5B-Instruct", "provider": "huggingface"} +{"model_id": "TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T", "provider": "huggingface"} +{"model_id": "VishalMysore/cookgptlama", "provider": "huggingface"} +{"model_id": "unsloth/granite-4.0-h-tiny", "provider": "huggingface"} +{"model_id": "tiiuae/falcon-rw-1b", "provider": "huggingface"} +{"model_id": "tiiuae/Falcon-H1-1.5B-Base", "provider": "huggingface"} +{"model_id": "stabilityai/stablelm-2-1_6b-chat", "provider": "huggingface"} +{"model_id": "utter-project/EuroLLM-1.7B", "provider": "huggingface"} +{"model_id": "unsloth/Qwen3-0.6B-Base", "provider": "huggingface"} +{"model_id": "m-a-p/YuE-s2-1B-general", "provider": "huggingface"} +{"model_id": "VillanovaAI/Villanova-2B-2512-Preview", "provider": "huggingface"} +{"model_id": "authormist/authormist-originality", "provider": "huggingface"} +{"model_id": "unsloth/Qwen2.5-3B-Instruct", "provider": "huggingface"} +{"model_id": "unsloth/gemma-2-2b-it", "provider": "huggingface"} +{"model_id": "mii-llm/minerva-chat-v0.1-alpha-sft", "provider": "huggingface"} +{"model_id": "stabilityai/stable-code-3b", "provider": "huggingface"} +{"model_id": "tiiuae/Falcon-H1-3B-Base", "provider": "huggingface"} +{"model_id": "yec019/fbopt-350m-8bit", "provider": "huggingface"} +{"model_id": "ToxicityPrompts/PolyGuard-Qwen-Smol", "provider": "huggingface"} +{"model_id": "princeton-nlp/Sheared-LLaMA-2.7B", "provider": "huggingface"} +{"model_id": "RedHatAI/gpt-oss-20b", "provider": "huggingface"} +{"model_id": "unsloth/gpt-oss-20b-BF16", "provider": "huggingface"} +{"model_id": "huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated", "provider": "huggingface"} +{"model_id": "PrimeIntellect/Qwen3-4B", "provider": "huggingface"} +{"model_id": "riv25-aim410/qwen3-4b-spectrum-nl2sql", "provider": "huggingface"} +{"model_id": "unsloth/Qwen3-4B-Instruct-2507", "provider": "huggingface"} +{"model_id": "ibm-granite/granite-4.0-micro-base", "provider": "huggingface"} +{"model_id": "huihui-ai/Llama-3.2-3B-Instruct-abliterated", "provider": "huggingface"} +{"model_id": "radheneev/NetrAI-L3", "provider": "huggingface"} +{"model_id": "tiiuae/Falcon3-7B-Instruct", "provider": "huggingface"} +{"model_id": "e-palmisano/Phi3-ITA-mini-4K-instruct", "provider": "huggingface"} +{"model_id": "MoxoffSrL/Moxoff-Phi3Mini-KTO", "provider": "huggingface"} +{"model_id": "SeacomSrl/SeaPhi3-mini", "provider": "huggingface"} +{"model_id": "unsloth/Phi-3.5-mini-instruct", "provider": "huggingface"} +{"model_id": "SeaLLMs/SeaLLM-7B-v2.5", "provider": "huggingface"} +{"model_id": "robowaifudev/megatron-gpt2-345m", "provider": "huggingface"} +{"model_id": "yam-peleg/Hebrew-Gemma-11B-V2", "provider": "huggingface"} +{"model_id": "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2", "provider": "huggingface"} +{"model_id": "nvidia/AceInstruct-7B", "provider": "huggingface"} +{"model_id": "unsloth/DeepSeek-R1-Distill-Qwen-7B", "provider": "huggingface"} +{"model_id": "tiiuae/Falcon3-10B-Base", "provider": "huggingface"} +{"model_id": "state-spaces/mamba-2.8b-hf", "provider": "huggingface"} +{"model_id": "state-spaces/mamba-370m-hf", "provider": "huggingface"} +{"model_id": "EleutherAI/pythia-410m-seed1", "provider": "huggingface"} +{"model_id": "allenai/tulu-2-dpo-7b", "provider": "huggingface"} +{"model_id": "dreamgen/WizardLM-2-7B", "provider": "huggingface"} +{"model_id": "GritLM/emb_m7_nodes16_fast", "provider": "huggingface"} +{"model_id": "EleutherAI/pythia-410m-seed3", "provider": "huggingface"} +{"model_id": "Ihor/Text2Graph-R1-Qwen2.5-0.5b", "provider": "huggingface"} +{"model_id": "Nexusflow/Starling-LM-7B-beta", "provider": "huggingface"} +{"model_id": "Open-Orca/Mistral-7B-OpenOrca", "provider": "huggingface"} +{"model_id": "reglab-rrc/mistral-rrc", "provider": "huggingface"} +{"model_id": "RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2", "provider": "huggingface"} +{"model_id": "shadowml/BeagSake-7B", "provider": "huggingface"} +{"model_id": "tartuNLP/Llammas-base-p1-GPT-4o-human-error-mix-paragraph-GEC", "provider": "huggingface"} +{"model_id": "togethercomputer/LLaMA-2-7B-32K", "provider": "huggingface"} +{"model_id": "Universal-NER/UniNER-7B-all", "provider": "huggingface"} +{"model_id": "uukuguy/speechless-zephyr-code-functionary-7b", "provider": "huggingface"} +{"model_id": "Voicelab/trurl-2-7b", "provider": "huggingface"} +{"model_id": "yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B", "provider": "huggingface"} +{"model_id": "ronigold/dictalm2.0-instruct-fine-tuned-alpaca-gpt4-hebrew", "provider": "huggingface"} +{"model_id": "thelamapi/next-1b", "provider": "huggingface"} +{"model_id": "EleutherAI/deep-ignorance-pretraining-stage-unfiltered", "provider": "huggingface"} +{"model_id": "togethercomputer/RedPajama-INCITE-7B-Instruct", "provider": "huggingface"} +{"model_id": "allenai/Llama-3.1-Tulu-3-8B", "provider": "huggingface"} +{"model_id": "aisingapore/Llama-SEA-LION-v3-8B-IT", "provider": "huggingface"} +{"model_id": "anakin87/Llama-3-8b-ita-ties-pro", "provider": "huggingface"} +{"model_id": "DeepMount00/Llama-3.1-8b-ITA", "provider": "huggingface"} +{"model_id": "dfurman/Llama-3-8B-Orpo-v0.1", "provider": "huggingface"} +{"model_id": "gradientai/Llama-3-8B-Instruct-262k", "provider": "huggingface"} +{"model_id": "McGill-NLP/AfriqueLlama-8B", "provider": "huggingface"} +{"model_id": "nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3", "provider": "huggingface"} +{"model_id": "RedHatAI/Llama-3.1-8B-Instruct-NVFP4", "provider": "huggingface"} +{"model_id": "rombodawg/rombos_Replete-Coder-Llama3-8B", "provider": "huggingface"} +{"model_id": "sh2orc/Llama-3.1-Korean-8B-Instruct", "provider": "huggingface"} +{"model_id": "shenzhi-wang/Llama3.1-8B-Chinese-Chat", "provider": "huggingface"} +{"model_id": "tartuNLP/Llama-3.1-EstLLM-8B-Instruct-1125", "provider": "huggingface"} +{"model_id": "tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1", "provider": "huggingface"} +{"model_id": "typhoon-ai/llama-3-typhoon-v1.5-8b-instruct", "provider": "huggingface"} +{"model_id": "UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3", "provider": "huggingface"} +{"model_id": "unsloth/llama-3-8b", "provider": "huggingface"} +{"model_id": "unsloth/Meta-Llama-3.1-8B", "provider": "huggingface"} +{"model_id": "voidful/Llama-3.2-8B-Instruct", "provider": "huggingface"} +{"model_id": "yaojialzc/Gigi-Llama3-8B-Chinese-zh", "provider": "huggingface"} +{"model_id": "swiss-ai/Apertus-8B-Instruct-2509", "provider": "huggingface"} diff --git a/docs/primary_robustness_experiment.tex b/docs/primary_robustness_experiment.tex index f85bbaa..b40563e 100644 --- a/docs/primary_robustness_experiment.tex +++ b/docs/primary_robustness_experiment.tex @@ -1,1029 +1,313 @@ -\documentclass[11pt,a4paper]{article} +\documentclass[10pt,a4paper]{article} -\usepackage[margin=1in]{geometry} +\usepackage[margin=0.72in]{geometry} \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} -\usepackage{amsmath,amssymb} \usepackage{booktabs} \usepackage{graphicx} -\usepackage{longtable} -\usepackage{multirow} \usepackage{xcolor} \usepackage{hyperref} -\usepackage{listings} \usepackage{enumitem} +\usepackage{array} \hypersetup{hidelinks} -\lstset{ - basicstyle=\ttfamily\small, - breaklines=true, - frame=single, - columns=fullflexible, - keepspaces=true -} - -\newcommand{\todoresult}{\textcolor{gray}{\textit{Not yet available}}} -\newcommand{\method}[1]{\texttt{#1}} - -\title{Primary LLM-DNA Robustness Experiment\\ -\large Prospective Experimental Protocol and Report Template} +\setlist{nosep,leftmargin=*} +\setlength{\parindent}{0pt} +\setlength{\parskip}{5pt} +\renewcommand{\arraystretch}{1.08} +\newcommand{\demo}[1]{\textcolor{blue!65!black}{#1\textsuperscript{*}}} +\newcommand{\pending}{\textcolor{gray}{--}} + +\title{\vspace{-1.4cm}LLM-DNA Robustness Experiment\\ +\large Five-Page Progress Brief} \author{} -\date{July 24, 2026} +\date{Updated July 30, 2026} \begin{document} \maketitle +\vspace{-0.8cm} -\begin{abstract} -This document records the prospective design of the primary LLM-DNA decoding -robustness experiment before generation and evaluation are complete. The study -will measure how temperature and top-$p$ affect exact-model identification, -whether a classifier can mitigate the resulting accuracy loss, and whether a -multi-sample distributional representation improves robustness under a matched -generation budget. The revised primary cohort contains 100 models selected -without using evaluation performance. The experiment contains one deterministic -condition and a complete $4\times3$ stochastic decoding grid, with four -independent generation repeats per condition. The principal deployment setting -assumes that temperature and top-$p$ are hidden at inference time and only an -upper bound on temperature is known. No experimental result is claimed in the -primary report; all primary result fields are explicitly marked as pending. -Small-scale exploratory demonstrations using previously generated artifacts are -reported separately and are not substitutes for the preregistered experiment. -\end{abstract} - -\section{Status and scope} - -\begin{center} -\fbox{\parbox{0.92\linewidth}{ -\textbf{Primary experiment status: not yet run.} The cohort file, launch -scripts, evaluation scripts, statistical outputs, and reporting plan have been -prepared. Numerical entries and figures in the primary Results section are -placeholders. Section~\ref{sec:small-demo} contains explicitly labelled -small-scale demonstrations from existing artifacts; they must not be -interpreted as primary findings. -}} -\end{center} - -Before generation began, a resource-feasibility review on July 24, 2026 -reduced the planned cohort from 300 to 100 models. The decoding grid, four -independent repeats, train/test separation, prompts, and analysis plan remain -unchanged. Accordingly, exact identification is now a 100-way closed-set task, -and conclusions will be limited to the revised fixed cohort. - -This experiment replaces the 77-model interim analysis as the primary -robustness study. The earlier analysis remains a legacy diagnostic because it -used an incomplete sweep, one generation repeat per reported cell, an -historical reference gallery with unknown decoding parameters, and a binary -same-or-related task that was not directly comparable to exact-model Top-1 -retrieval. - -\section{Research questions and hypotheses} - -\subsection{Primary research questions} +\section{What changed and what matters} -\begin{enumerate}[label=\textbf{RQ\arabic*.}] - \item How do temperature and top-$p$ affect exact-model classification from - LLM-DNA representations? - \item On the same exact-model task, can a classifier trained on the full DNA - vector reduce the accuracy degradation relative to cosine retrieval? - \item Does the proposed multi-sample distributional representation improve - robustness beyond original DNA under the same response and token - budget? - \item If temperature and top-$p$ are unknown at inference time, but - temperature is known to satisfy $T\leq\tau$, what average and - worst-setting accuracy can be expected? -\end{enumerate} - -\subsection{Secondary research questions} - -\begin{enumerate}[label=\textbf{SRQ\arabic*.}] - \item When exact identification fails, does the predicted model remain close - to the true model in the Hugging Face model tree? - \item Do direct parent--child, indirect, and disconnected model pairs exhibit - different DNA-distance distributions as decoding stochasticity rises? - \item How sensitive are the conclusions to response encoder, truncation, - projection dimension, projection seed, and the number of response - samples? -\end{enumerate} - -The main hypothesis is that temperature will cause a larger reduction in exact -classification accuracy than top-$p$ over the planned range. Classifier and -distributional-method benefits will be judged by paired accuracy differences -and by a shallower degradation curve, rather than by comparing accuracies from -different tasks. +\begin{quote} +\textbf{Main finding.} The current 84-model exploratory evidence does not +support the simple claim that higher temperature always reduces model-detection +performance. Instead, temperature and top-$p$ interact: increasing $T$ from +0.2 to 0.3 improved performance at $p=0.8$, made little detectable difference +at $p=0.9$, and substantially reduced performance at $p=1.0$. +\end{quote} -\section{Model cohort} - -\subsection{Selection rule} - -The planned cohort contains 100 Hugging Face models. It was selected from 470 -eligible models with valid unsuffixed reference DNA artifacts and recorded -parameter counts between 0.3 and 7.5 billion. Selection used seeded round-robin -sampling over architecture $\times$ logarithmic parameter-size strata. It did -not use retrieval accuracy, SVM accuracy, response quality, or any other test -outcome. Before launching the primary grid, one selected model that repeatedly -returned empty responses in the pilot control run was replaced with a -similar-size model whose cached baseline run contained 100 non-empty responses. -This reliability replacement was documented in advance and did not use -evaluation accuracy. - -The fixed artifacts are: +\textbf{Changes from the previous report} \begin{itemize} - \item cohort: \path{configs/rand_chinese_stratified_100.jsonl}; - \item cohort audit: \path{configs/rand_chinese_stratified_100_audit.json}; - \item hardware shards: - \path{configs/rand_chinese_stratified_100_rtx3090.jsonl} and - \path{configs/rand_chinese_stratified_100_a100.jsonl}; - \item shard audit: - \path{configs/rand_chinese_stratified_100_shards_audit.json}; - \item selection seed: 42; and - \item cohort SHA-256: - \texttt{19b7c7b554af4e98edeb3ab279f52ccae66a9bddbff81c0c15f2fb464e895e6e}. + \item The primary cohort is now the fixed validated-100 cohort. + \item The plan was reduced from four to two generation rounds per condition: + repeat 1 is the reference/training split and repeat 2 is held out. + \item The workload is now 13 conditions $\times$ 2 rounds $\times$ 100 + models $=2{,}600$ successful artifacts, rather than 5,200. + \item The report now includes the live grid, exploratory values in the main + result tables, direct answers to the research questions, and a dated + completion forecast. \end{itemize} -The audit file records the eligible population, selection method, selected -model IDs, architecture and size metadata, exclusions, and output hash. The -cohort must not be changed in response to preliminary performance. Any further -operational replacement must be justified by a generation failure, matched as -closely as practical on parameter size, recorded in the audit, and fixed before -the affected run is launched. - -\subsection{Completion requirement} - -The intended final analysis uses all 100 models. The automated primary analysis -will refuse to run if fewer than 80 models are common to all required training -and test runs. Missing models should first be backfilled. If the final common -cohort remains below 100, the report will provide success rates and failure -reasons by decoding cell and compare retained and excluded models by size and -architecture. +\textbf{Current status (July 30, 18:16 Singapore time).} +The live audit contains \textbf{1,352/2,600 successful artifacts (52.0\%)}, +leaving 1,248. A four-GPU backfill on GPUs 0, 1, 2, and 4 is active and has +added 39 successes since its July 30 restart. No primary accuracy claim will +be made until both rounds are complete on a common cohort. -\section{Prompt set and DNA construction} - -Every model receives the same 100 synthetic classical-Chinese prompts from -\path{data/rand/rand_dataset_chinese.json}. The prompt construction and random -projection seed remain fixed at 42 for every model, setting, and repeat. - -For the original representation, each response is encoded using -\method{all-mpnet-base-v2}. The 100 response embeddings are concatenated and -mapped to a 128-dimensional DNA vector by a seeded Gaussian random projection. -The current maximum response length is 1024 generated tokens. The saved -metadata records at least the model ID, dataset, prompt count, temperature, -top-$p$, sampling flag, generation seed, maximum length, projection seed, -encoder, and DNA dimension. - -Because the sentence encoder is primarily English-oriented and truncates long -inputs, the final study will additionally report response-length and encoder -token-length distributions, the fraction exceeding the encoder limit, and a -sensitivity analysis with a Chinese or multilingual response encoder. - -\section{Decoding design} - -\subsection{Factorial grid} - -The deterministic condition is -\[ - T=0, \qquad \text{sampling disabled}, \qquad p=1.0\text{ (recorded only)}. -\] -Top-$p$ is inactive when sampling is disabled; consequently, $T=0$ appears once -and is not repeated as three nominal top-$p$ cells. - -For stochastic decoding, the complete grid is -\[ - T\in\{0.2,0.3,0.5,0.7\}, \qquad - p\in\{0.8,0.9,1.0\}. -\] -This gives 13 decoding conditions in total. Four independent generation -repeats are planned for every condition, giving 52 run cells and -\[ - 100\ \text{models}\times13\ \text{conditions}\times4\ \text{repeats} - =5{,}200 -\] -model-level extraction tasks before retries. - -\subsection{Independent repeats and seeds} - -The response-sampling seeds are: - -\begin{table}[ht] +\begin{table}[h] \centering -\caption{Generation-repeat assignment. The projection seed remains 42 in all rows.} -\label{tab:repeat-assignment} -\begin{tabular}{ccc} +\small +\caption{Current answers to the research questions.} +\begin{tabular}{p{0.08\linewidth}p{0.58\linewidth}p{0.25\linewidth}} \toprule -Repeat & Generation seed & Primary role \\ +RQ & Current answer & Confidence \\ \midrule -1 & 42001 & training and reference gallery \\ -2 & 42002 & training and reference gallery \\ -3 & 42003 & held-out test query \\ -4 & 42004 & held-out test query \\ +RQ1 & Temperature cannot be interpreted independently of top-$p$. & +Limited 84-model evidence \\ +RQ2 & Linear and RBF SVMs improve equal-cell Top-1 over cosine on identical +queries. & Paired exploratory CIs are positive \\ +RQ3 & The proposed distributional advantage is not yet tested fairly on the +84-model cohort. & Inconclusive \\ +RQ4 & Linear SVM has the best mixed accuracy; RBF has the best worst-cell +accuracy at $\tau=0.3$. & Limited 84-model evidence \\ \bottomrule \end{tabular} \end{table} -No response, DNA vector, or repeat may appear in both the training/reference -split and the held-out query split. Generation seed and projection seed are -stored separately so that response variability is not confounded with a change -in the random projection. - -\section{Primary exact-model task} - -Let $\mathcal{M}$ be the fixed common model cohort. Given a query DNA vector -$\mathbf{d}_{m,s,r}$ generated by model $m$, decoding setting $s=(T,p)$, and -held-out repeat $r\in\{3,4\}$ (that is, $r$ is either repeat 3 or repeat 4 in -Table~\ref{tab:repeat-assignment}), each method evaluates every candidate -$j\in\mathcal{M}$. Let $\mathcal{D}^{\mathrm{train}}_j$ denote the set of -training DNA vectors available for candidate model $j$. Its gallery centroid is -\[ - \boldsymbol{\mu}_j - =\frac{1}{|\mathcal{D}^{\mathrm{train}}_j|} - \sum_{\mathbf{x}\in\mathcal{D}^{\mathrm{train}}_j}\mathbf{x}. -\] -The standard cosine similarity and cosine distance both take the query and the -candidate centroid as inputs: -\[ - S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) - =\frac{\mathbf{d}^{\mathsf T}\boldsymbol{\mu}_j} - {\lVert\mathbf{d}\rVert_2\lVert\boldsymbol{\mu}_j\rVert_2}, - \qquad - D_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) - =1-S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j). -\] -Thus, the cosine rule may be written equivalently as -\[ - \widehat{m}^{(\mathrm{cos})} - =\arg\min_{j\in\mathcal{M}} - D_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j) - =\arg\max_{j\in\mathcal{M}} - S_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j). -\] -No square is applied in the standard cosine distance used by the evaluation -code; each Euclidean norm already satisfies -$\lVert\mathbf{d}\rVert_2=(\sum_k d_k^2)^{1/2}$, but the cosine value itself is -not squared. The squared norm appearing below belongs instead to the RBF kernel. -For the SVM rules, the explicit class-$j$ decision scores and predictions are -\[ - f_j^{(a)}(\mathbf{d})= - \begin{cases} - \mathbf{w}_j^{\mathsf T}\mathbf{z}(\mathbf{d})+b_j, - & a=\mathrm{lin},\\[4pt] - \displaystyle - \sum_{i=1}^{n_{\mathrm{SV}}}\alpha_{ij} - \exp\!\left( - -\gamma\lVert\mathbf{z}(\mathbf{d})-\mathbf{z}(\mathbf{x}_i)\rVert_2^2 - \right)+b_j, - & a=\mathrm{rbf}. - \end{cases} - \qquad - \widehat{m}^{(a)} - =\arg\max_{j\in\mathcal{M}}f_j^{(a)}(\mathbf{d}_{m,s,r}), - \quad a\in\{\mathrm{lin},\mathrm{rbf}\}. -\] -Here $\mathbf{z}(\cdot)$ is the feature-wise standardization learned from the -training split, $(\mathbf{w}_j,b_j)$ are the linear-SVM parameters, and -$\{\mathbf{x}_i,\alpha_{ij}\}_{i=1}^{n_{\mathrm{SV}}}$ and $\gamma$ are the -RBF-SVM support vectors, effective class-$j$ coefficients, and kernel -bandwidth parameter, respectively. The prediction is correct only if -$\widehat{m}^{(a)}=m$. Thus, cosine and SVM methods -are evaluated on identical multiclass labels, queries, and candidate sets. - -\subsection{Methods} - -\begin{table}[ht] -\centering -\caption{Planned representation $\times$ classifier comparison.} -\label{tab:method-matrix} -\begin{tabular}{lll} -\toprule -Representation & Decision rule & Purpose \\ -\midrule -Original 128-D DNA & cosine nearest centroid & primary retrieval baseline \\ -Original 128-D DNA & multiclass linear SVM & classifier-only ablation \\ -Original 128-D DNA & multiclass RBF SVM & nonlinear classifier ablation \\ -Multi-sample mean DNA & cosine nearest centroid & matched-budget averaging baseline \\ -Distributional DNA & exact RBF-MMD & proposed representation \\ -Distributional DNA & multiclass SVM & full proposed method, if applicable \\ -\bottomrule -\end{tabular} -\end{table} - -The linear and RBF SVMs consume the full 128-dimensional DNA vector. The legacy -linear SVM using only negative cosine distance is not considered an exact-model -classification improvement because a monotone linear transformation of cosine -distance cannot alter closed-set candidate ranking. It remains only a binary -verification baseline. - -\subsection{Known-setting descriptive evaluation} - -For each held-out condition $(T,p)$ and each method $a$, the experiment reports -\[ - A_a(T,p)=\frac{1}{|\mathcal{Q}_{T,p}|} - \sum_{q\in\mathcal{Q}_{T,p}}\mathbb{I}[\widehat{m}_{a,q}=m_q]. -\] -Top-1 is primary; Top-3, Top-5, and mean reciprocal rank (MRR) are secondary. -The change from the deterministic condition is -\[ - \Delta A_a(T,p)=A_a(T,p)-A_a(0). -\] -All per-setting comparisons will use the same held-out model queries. - -\section{Unknown-temperature bounded deployment} - -The main deployment scenario assumes that the exact decoding parameters are -not provided to the classifier. For a temperature bound -\[ - \tau\in\{0.2,0.3,0.5,0.7\}, -\] -one classifier is trained using pooled repeats 1--2 from every allowed setting -with $T\leq\tau$. Temperature and top-$p$ are omitted from its features. It is -then evaluated on held-out repeats 3--4 from all allowed settings. - -To avoid overweighting temperatures that happen to have more nominal cells, -repeats are first averaged within each $(T,p)$ cell. The primary mixed accuracy -is the equal-cell mean -\[ - A_a^{\mathrm{mix}}(\tau) - =\frac{1}{|\mathcal{S}_\tau|} - \sum_{s\in\mathcal{S}_\tau} A_a(s), - \qquad - \mathcal{S}_\tau=\{(T,p):T\leq\tau\}. -\] -The robustness guarantee is summarized by the worst observed setting -\[ - A_a^{\mathrm{worst}}(\tau) - =\min_{s\in\mathcal{S}_\tau} A_a(s). -\] -These quantities directly answer how accurately the system performs when the -decoding setting is hidden but its temperature is bounded. - -\section{Distributional and matched-budget evaluation} - -The distributional method operates on repeated response samples rather than a -single point estimate. It will be compared against both a single-sample cosine -baseline and a matched-budget mean/centroid baseline. Every method comparison -must use the same number of response samples, prompts, and total generated-token -budget unless the budget difference is explicitly plotted. - -The planned sample-budget ablation is -\[ - K\in\{1,2,4,8\}, -\] -subject to the number of available independent responses. For each $K$, the -report will include accuracy, generated tokens, reference storage, and analysis -time. This distinguishes gains due to the representation from gains obtained -merely by consuming more samples. - -\section{Secondary verification and tree-aware analysis} - -\subsection{Binary verification} - -If binary pairwise verification is reported, its tasks are separated into: - -\begin{enumerate} - \item exact same-model versus unrelated; - \item lineage-only (off-diagonal related) versus unrelated; and - \item the legacy same-or-related versus unrelated task. -\end{enumerate} - -Training may downsample negative pairs or use class weighting. Testing uses all -available pairs at their natural prevalence. The main binary metrics are -precision--recall AUC, precision, recall, specificity, Matthews correlation -coefficient, false positives per query, and ROC-AUC. Balanced-test accuracy may -be shown only as an explicitly labelled diagnostic. - -\subsection{Model-tree distance} - -The Hugging Face relation exporter stores direct base-model edges in addition -to connected components. Using the undirected direct-edge graph, the shortest -tree distance is -\[ - d_{\mathrm{tree}}(i,j)= - \begin{cases} - 0, & i=j,\\ - 1, & i\text{ and }j\text{ share a direct edge},\\ - 2,3,\ldots, & \text{connected by the corresponding number of edges},\\ - \infty, & \text{disconnected}. - \end{cases} -\] - -The final report will show DNA-distance distributions for exact, direct -one-hop, indirect two-hop, indirect three-or-more-hop, same-component with -unknown hop distance, and disconnected pairs. For Top-1 predictions, cumulative -tree-aware accuracy will be reported as exact, within one hop, within two hops, -and within the same connected component. This analysis describes error severity -and does not replace exact-model accuracy as the primary endpoint. - -\section{Statistical analysis plan} - -\subsection{Uncertainty} - -Every accuracy and MRR estimate will include a 95\% confidence interval. -Resampling will preserve dependence among observations: - -\begin{itemize} - \item model IDs are resampled as clusters across methods and settings; - \item generation repeat is treated as a higher-level resampling unit when - sufficient repeats are available; and - \item tree-related analyses use model-tree component clusters where relevant. -\end{itemize} - -Pair-level observations sharing a query model, reference model, or tree -component will not be treated as independent Bernoulli trials. - -\subsection{Paired comparisons} - -Method differences are computed on identical held-out queries. The principal -quantity is -\[ - \delta_{a,b}(T,p)=A_a(T,p)-A_b(T,p), -\] -with a paired cluster-bootstrap confidence interval. The analysis will also -test whether a method changes the temperature degradation slope, for example -with a mixed-effects logistic model of the form -\[ - \operatorname{logit}\Pr(\mathrm{correct}) - =\beta_0+\beta_1 T+\beta_2 p+\beta_3\mathrm{method} - +\beta_4(T\times\mathrm{method}) - +\beta_5(p\times\mathrm{method})+u_{\mathrm{model}}+u_{\mathrm{repeat}}. -\] -The interaction terms, rather than isolated cell differences, address whether -a method mitigates robustness loss. - -\subsection{Multiplicity and exploratory analyses} - -Top-1 accuracy and bounded mixed accuracy are primary endpoints. Top-3, Top-5, -MRR, tree-aware accuracy, encoder ablations, and projection ablations are -secondary. Any unplanned subgroup or hyperparameter analysis will be labelled -exploratory. If many method-by-setting significance tests are presented, a -false-discovery-rate correction will be applied. - -\section{Ablations and validity checks} - -The following checks are planned where artifacts and compute permit: - -\begin{enumerate} - \item projection dimensions $\{64,128,256,512\}$ and at least five projection - seeds; - \item an unprojected or prompt-level distance baseline; - \item prompt bootstrap and split-half reliability; - \item response encoder comparison including a Chinese or multilingual - encoder; - \item response-token and encoder-token length distributions and truncation - rates by setting; - \item fixed $T=0$ gallery, same-setting repeat gallery, and multi-setting - centroid gallery; - \item leave-one-setting-out evaluation for unseen decoding configurations; - \item performance by model size, architecture, and chat/base status; and - \item failure probability by model attributes and decoding setting. -\end{enumerate} - \clearpage -\section{Small-scale demonstration report using existing artifacts} -\label{sec:small-demo} - -\subsection{Scope and split} - -To check that the analyses in Sections 6--11 are executable before the primary -sweep is complete, small exploratory demonstrations were run on July 23, 2026, -using only artifacts already present under \path{out/rand_chinese}. No new model -responses were generated. Repeat 1 was used for training/reference data and -repeat 2 for testing. Six complete stochastic cells were available: -\[ - T\in\{0.2,0.3\},\qquad p\in\{0.8,0.9,1.0\}. -\] -The $T=0.5$ and $T=0.7$ cells were excluded because they did not have a -sufficiently complete independent-repeat split. These demonstrations use -convenience cohorts determined by artifact availability, not the fixed -100-model primary cohort. They are therefore pipeline checks and exploratory -effect estimates only. - -\subsection{Executive summary} +\section{Important exploratory findings} -\noindent\fbox{\parbox{0.94\linewidth}{ -\textbf{Demo outcome.} The available artifacts support an end-to-end -demonstration of exact classification, bounded unknown-temperature evaluation, -paired model-cluster uncertainty, exact-only binary verification, a limited -distributional comparison, a limited tree-aware analysis, and a gallery -sensitivity check. Across the six complete cells, the pooled linear SVM had the -highest equal-cell Top-1 (0.722), followed by RBF SVM (0.688) and cosine -retrieval (0.621). The hardest observed cell was $(T,p)=(0.3,1.0)$. These are -exploratory results from availability-selected cohorts and must not be read as -the outcome of the 100-model primary experiment. -}} +The following results use the same 84 availability-selected models, repeat 1 +for pooled reference/training construction, and repeat 2 for held-out testing. +They are pipeline-scale evidence, not final validated-100 results. -\medskip -Table~\ref{tab:demo-coverage} maps each planned protocol component to what -could actually be demonstrated. ``Partial'' means that the code path ran but -the available cohort or repeat structure could not exercise the full planned -analysis. - -\begin{table}[htbp] +\begin{table}[h] \centering -\caption{Coverage of Sections 6--11 by the small-scale demonstration.} -\label{tab:demo-coverage} -\begin{tabular}{p{0.13\linewidth}p{0.35\linewidth}p{0.23\linewidth}p{0.15\linewidth}} +\small +\caption{Held-out exact-model Top-1. Asterisks denote exploratory results.} +\begin{tabular}{ccrrrr} \toprule -Protocol section & Demonstrated analysis & Available scale & Status \\ +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ \midrule -6: Exact task - & Cosine centroid, linear SVM, and RBF SVM on identical queries - & Six cells; 84 common models & Complete demo \\ -7: Bounded deployment - & Equal-cell mixed and worst-setting accuracy for $\tau=0.2,0.3$ - & 89 and 84 models & Complete demo \\ -8: Distributional - & Single cosine, mean cosine, and exact RBF-MMD - & Eight models; 20 prompts; at most two repeats & Partial \\ -9: Verification - & Exact-only linear probe at natural pair prevalence - & 95 models; 9,025 pairs per direction & Complete demo \\ -9: Tree-aware - & Exact versus disconnected error and distance summaries - & 95 models; no related pairs & Partial \\ -10: Statistics - & Model-cluster CIs and paired method differences - & One held-out repeat per cell & Partial \\ -11: Validity - & Fixed-$T=0$ versus pooled-gallery diagnostic - & 86 versus 84 models & Partial \\ +0.2 & 0.8 & \demo{0.643} & \demo{0.821} & \demo{0.702} & \pending \\ +0.2 & 0.9 & \demo{0.726} & \demo{0.845} & \demo{0.762} & \pending \\ +0.2 & 1.0 & \demo{0.595} & \demo{0.643} & \demo{0.643} & \pending \\ +0.3 & 0.8 & \demo{0.774} & \demo{0.869} & \demo{0.857} & \pending \\ +0.3 & 0.9 & \demo{0.690} & \demo{0.786} & \demo{0.762} & \pending \\ +0.3 & 1.0 & \demo{0.298} & \demo{0.369} & \demo{0.405} & \pending \\ \bottomrule \end{tabular} \end{table} -\subsection{Exact classification and bounded deployment demo} - -For $\tau=0.2$, the common cohort contained 89 models and the training pool -contained the three repeat-1 cells at $T=0.2$. For $\tau=0.3$, requiring all six -cells reduced the common cohort to 84 models. The classifier was not given -temperature or top-$p$. Table~\ref{tab:demo-per-setting} reports held-out -repeat-2 Top-1 accuracy for the $\tau=0.3$ pooled training experiment. +\textbf{Temperature--top-$p$ interaction.} +For cosine retrieval, increasing $T$ from 0.2 to 0.3 changed Top-1 by +$+0.131$ at $p=0.8$ (95\% CI 0.024 to 0.238), $-0.036$ at $p=0.9$ +($-0.167$ to 0.083), and $-0.298$ at $p=1.0$ ($-0.417$ to $-0.179$). +The two SVMs show the same qualitative pattern. The current data therefore +support an interaction at limited scale, but not a general monotone +temperature conclusion. -\begin{table}[ht] +\begin{table}[h] \centering -\caption{Exploratory held-out Top-1 accuracy with 84 common models.} -\label{tab:demo-per-setting} -\begin{tabular}{ccrrr} +\small +\caption{Temperature-agnostic bounded deployment.} +\begin{tabular}{clrr} \toprule -$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM \\ +$\tau$ & Method & Mixed Top-1 & Worst cell \\ \midrule -0.2 & 0.8 & 0.643 & 0.821 & 0.702 \\ -0.2 & 0.9 & 0.726 & 0.845 & 0.762 \\ -0.2 & 1.0 & 0.595 & 0.643 & 0.643 \\ -0.3 & 0.8 & 0.774 & 0.869 & 0.857 \\ -0.3 & 0.9 & 0.690 & 0.786 & 0.762 \\ -0.3 & 1.0 & 0.298 & 0.369 & 0.405 \\ +0.2 & Cosine & 0.596 & 0.562 \\ + & Linear SVM & \textbf{0.682} & \textbf{0.652} \\ + & RBF SVM & 0.629 & 0.584 \\ +\addlinespace +0.3 & Cosine & 0.621 & 0.298 \\ + & Linear SVM & \textbf{0.722} & 0.369 \\ + & RBF SVM & 0.688 & \textbf{0.405} \\ \bottomrule \end{tabular} \end{table} -Figure~\ref{fig:demo-exact-heatmaps} makes the setting-level pattern explicit. -The linear SVM led in four cells and tied in one, while the RBF SVM was highest -at $(0.3,1.0)$. All methods dropped sharply in that cell, so the demo does not -support a general monotone temperature effect independent of top-$p$. +On identical queries at $\tau=0.3$, linear SVM improved over cosine by 0.101 +(paired 95\% CI 0.058 to 0.147), and RBF SVM improved by 0.067 +(0.026 to 0.111). Thus classifier mitigation is the strongest finding +currently supported, although only at exploratory scale. -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/small_scale_section_6_11/exact_top1_heatmaps.png} -\caption{Exploratory exact-model Top-1 by method and decoding cell. Darker -cells indicate higher accuracy. The equal-cell means correspond to the -$\tau=0.3$ pooled-training experiment.} -\label{fig:demo-exact-heatmaps} -\end{figure} +\clearpage +\section{Live generation grid} -The equal-cell bounded summaries are shown in -Table~\ref{tab:demo-bounded}. Confidence intervals resample model IDs as -clusters across the included cells. +Each planned setting requires 200 successes: 100 models in each of two rounds. +A dash denotes an unplanned deterministic duplicate. -\begin{table}[ht] +\begin{table}[h] \centering -\caption{Exploratory bounded-deployment Top-1 summaries.} -\label{tab:demo-bounded} -\begin{tabular}{clrrr} +\small +\caption{Reusable successes as of July 30, 18:16.} +\begin{tabular}{crrr} \toprule -$\tau$ & Method & Mixed accuracy (95\% CI) & Worst cell & Models \\ +Temperature & $p=0.8$ & $p=0.9$ & $p=1.0$ \\ \midrule -0.2 & Cosine centroid & 0.596 (0.528, 0.667) & 0.562 & 89 \\ - & Linear SVM & 0.682 (0.614, 0.745) & 0.652 & 89 \\ - & RBF SVM & 0.629 (0.562, 0.700) & 0.584 & 89 \\ -\addlinespace -0.3 & Cosine centroid & 0.621 (0.554, 0.688) & 0.298 & 84 \\ - & Linear SVM & 0.722 (0.675, 0.772) & 0.369 & 84 \\ - & RBF SVM & 0.688 (0.633, 0.742) & 0.405 & 84 \\ +$0$ & -- & -- & $42/200$ \\ +$0.2$ & $166/200$ & $171/200$ & $162/200$ \\ +$0.3$ & $170/200$ & $168/200$ & $161/200$ \\ +$0.5$ & $79/200$ & $85/200$ & $73/200$ \\ +$0.7$ & $1/200$ & $74/200$ & $0/200$ \\ \bottomrule \end{tabular} \end{table} -On identical queries, the paired model-cluster bootstrap difference between -linear SVM and cosine was $0.086$ (95\% CI $0.037$ to $0.135$) at -$\tau=0.2$ and $0.101$ (95\% CI $0.058$ to $0.147$) at $\tau=0.3$. The -corresponding RBF-SVM differences were $0.034$ (95\% CI $0.004$ to $0.071$) -and $0.067$ (95\% CI $0.026$ to $0.111$). These intervals quantify only this -availability-selected demo and do not test the primary hypotheses. - -Figure~\ref{fig:demo-bounded-paired} combines three views that should remain -distinct in interpretation: bounded performance, paired within-query method -gains, and the unpaired gallery diagnostic. In particular, the fixed-gallery -bars are not a controlled estimate of gallery effects because the gallery -cohort and training budget differ from the pooled condition. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/small_scale_section_6_11/bounded_paired_gallery.png} -\caption{Bounded-deployment and sensitivity summary. Panel A shows equal-cell -mixed accuracy with model-cluster 95\% intervals and the worst observed cell. -Panel B shows paired Top-1 differences relative to cosine. Panel C is an -explicitly unpaired fixed-gallery versus pooled-gallery diagnostic.} -\label{fig:demo-bounded-paired} -\end{figure} - -\subsection{Distributional and matched-budget demo} - -The exact RBF-MMD pilot ran successfully on eight lexicographically selected -models, 20 common evaluation prompts, and two response repeats, using -$T=0.2,p=0.8$ as the reference setting. Single-sample cosine, mean-cosine, and -exact RBF-MMD each achieved Top-1 and MRR of 1.000 for the same-setting -comparison and for cross-setting queries at $(0.3,0.8)$ and $(0.3,0.9)$. The -same-setting split has only one repeat on each side, whereas the cross-setting -comparisons pool both repeats per model on each side. This is a successful -end-to-end demonstration, but the tiny cohort saturated all methods and -therefore provides no evidence that the distributional representation is -better. The available two repeats also do not support the planned -$K\in\{1,2,4,8\}$ ablation. - -\begin{table}[htbp] +\begin{table}[h] \centering -\caption{Distributional pilot Top-1 (MRR was also 1.000 in every entry).} -\label{tab:demo-distributional} +\small +\caption{Progress summarized by temperature.} \begin{tabular}{lrrr} \toprule -Query comparison & Single cosine & Mean cosine & Exact RBF-MMD \\ +Condition & Required & Success & Remaining \\ \midrule -Same $(0.2,0.8)$ & 1.000 & 1.000 & 1.000 \\ -Cross $(0.2,0.8)\rightarrow(0.3,0.8)$ & 1.000 & 1.000 & 1.000 \\ -Cross $(0.2,0.8)\rightarrow(0.3,0.9)$ & 1.000 & 1.000 & 1.000 \\ -\bottomrule -\end{tabular} -\end{table} - -This complete saturation is itself a useful demo diagnostic: a larger and more -difficult cohort is required before representation differences can be -estimated. It would be inappropriate to select a method from these pilot -numbers. - -\subsection{Binary verification and tree-aware demo} - -An exact-only linear verification probe was trained on one $T=0.2,p=0.8$ -repeat and tested on the other, in both directions, over 95 common models. -Testing retained all 8,930 negative pairs per direction, giving a natural -positive prevalence of $1/95$. Results are in -Table~\ref{tab:demo-verification}. - -\begin{table}[ht] -\centering -\caption{Exploratory exact-only binary verification at natural prevalence.} -\label{tab:demo-verification} -\begin{tabular}{lrrrrrr} -\toprule -Test repeat & PR-AUC & ROC-AUC & Precision & Recall & Specificity & MCC \\ +$T=0$ & 200 & 42 & 158 \\ +$T=0.2$, all $p$ & 600 & 499 & 101 \\ +$T=0.3$, all $p$ & 600 & 499 & 101 \\ +$T=0.5$, all $p$ & 600 & 237 & 363 \\ +$T=0.7$, all $p$ & 600 & 75 & 525 \\ \midrule -1 & 0.334 & 0.832 & 0.049 & 0.726 & 0.851 & 0.163 \\ -2 & 0.373 & 0.869 & 0.045 & 0.747 & 0.831 & 0.155 \\ +Total & 2,600 & 1,352 & 1,248 \\ \bottomrule \end{tabular} \end{table} -Despite the apparently strong ROC-AUC, the probe produced 1,329 and 1,511 -false positives in the two directions, respectively. The resulting precision -near 5\% demonstrates why the primary verification report emphasizes PR-AUC, -MCC, specificity, and false positives per query rather than balanced accuracy -alone. - -The tree-aware cosine demo used repeat 1 at $(0.2,0.8)$ as the gallery and 95 -common models. Exact Top-1 was 0.526 for the same-setting repeat-2 query and -0.379 for the $(0.3,0.8)$ repeat-2 query. All 104 combined errors were between -models disconnected in the exported relation graph, so within-one-hop, -within-two-hop, and same-component accuracy did not exceed exact accuracy. -Mean cosine distance was 0.445 versus 0.929 for exact and disconnected pairs -in the same-setting comparison, and 0.566 versus 0.929 in the cross-setting -comparison. No direct or indirect related pair occurred within this -availability-selected cohort, so lineage-error severity could not be -demonstrated. - -Figure~\ref{fig:demo-verification-tree} emphasizes two practical limitations. -First, ROC-AUC obscures the false-positive burden when only one of 95 candidate -pairs is positive. Second, the tree-aware demo separates exact and disconnected -pairs but cannot validate the intended one-hop and indirect strata because no -such pair occurred in the common cohort. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/small_scale_section_6_11/verification_tree_diagnostics.png} -\caption{Verification and tree-aware diagnostics. Panels A--B show binary -metrics and false positives per query at natural prevalence. Panel C compares -mean cosine distance for exact and disconnected pairs and annotates exact -retrieval accuracy.} -\label{fig:demo-verification-tree} -\end{figure} - -\subsection{Validity-check demo and deferred items} - -As a limited gallery check, a fixed $T=0$ repeat-1 gallery was compared with -the multi-setting pooled gallery on the same six query settings. Equal-cell -mixed Top-1 with the fixed gallery was 0.403 for cosine, 0.310 for linear SVM, -and 0.341 for RBF SVM over 86 common models. The corresponding pooled-gallery -values were 0.621, 0.722, and 0.688 over 84 common models. Because the cohorts -and number of training examples differ, this is a diagnostic rather than a -paired method comparison; it merely confirms that gallery construction is -consequential. - -The remaining Section 10--11 analyses were deferred. Two temperatures and one -held-out repeat are inadequate for the planned mixed-effects degradation model. -Alternative projection dimensions/seeds, encoder comparisons, reliable -encoder-token truncation counts, $K=4$ and $K=8$, lineage-only verification, -and related-pair tree strata are not supported by the currently complete -artifacts. Leave-one-setting-out and subgroup analyses are feasible later but -were omitted to keep this demonstration deliberately small. - -\subsection{Demo conclusions and primary-study handoff} +\textbf{Execution priority.} +Resume the deterministic control first, followed by the large $T=0.7$ gaps, +then $T=0.5$, and finally the smaller $T=0.2/0.3$ backfills. Successful +historical artifacts are preserved; only missing or failed model--cell--round +keys are queued. -The demonstration supports the following limited conclusions: +\textbf{What is and is not complete.} +All 13 settings have been started, but none yet contains 100 successful models +in both rounds. The filled accuracy values on the previous page come from a +separate 84-model demonstration and must not be pooled with the primary +validated-100 results. -\begin{itemize} - \item The exact-classification and bounded-deployment pipelines operate on - disjoint generation repeats and produce aligned per-query scores for - all three classifiers. - \item Within this demo, the linear SVM had the highest mixed Top-1 at both - temperature bounds, and its paired advantage over cosine was positive. - \item Robustness was setting-dependent: $(0.3,1.0)$ was substantially harder - than the other five cells for every method. - \item Natural-prevalence verification exposed a large operational - false-positive burden that ROC-AUC alone would conceal. - \item The distributional and tree-aware code paths run, but the present data - are respectively too easy and too sparse in related pairs to compare - the intended methods. - \item Gallery construction materially changes the diagnostic accuracies, so - the primary report must keep gallery definitions and training budgets - fixed in paired comparisons. -\end{itemize} - -Before any primary claim is made, the study still requires the fixed 100-model -cohort, complete repeats 1--4 for all 13 conditions, the preregistered -repeat-1/2 versus repeat-3/4 split, $K=4$ and $K=8$ response samples, sufficient -direct and indirect model-tree pairs, and the planned encoder/projection -metadata. The small-scale numbers should not be copied into the pending primary -Results tables. - -Machine-readable outputs are stored under -\path{out/small_scale_section_6_11}. The exact-classification, verification, -distributional, fixed-gallery, and tree-aware subdirectories contain the -selected cohorts, per-query predictions where applicable, aggregate CSV files, -and JSON summaries. The three report figures are regenerated without plotting -library dependencies using: - -\begin{lstlisting}[language=bash] -python scripts/plot_small_scale_section_6_11_demo.py -\end{lstlisting} +\textbf{Current execution.} +The four-GPU backfill remains active. At the 18:20 host-side check, four model +workers were still processing the deterministic $r1$ cell. The status journal +preserves the 42 completed deterministic artifacts, so later restarts will not +regenerate them. \clearpage +\section{Revised design and the proposed method} -\section{Execution and resource plan} - -The generation launcher is -\path{scripts/run_primary_robustness_experiment.sh}. It uses the 100-model -cohort, four repeats, the 13-condition grid, isolated outputs under -\path{out/primary_robustness_100}, and isolated logs. The hardware-shard wrapper -\path{scripts/run_primary_robustness_100_shard.sh} assigns the 23 largest -models to the two A100 GPUs and the remaining 77 models to the RTX 3090 fleet. -Both processes write model artifacts beneath the same experiment directory, -but use distinct run names, logs, status journals, and global-state files to -avoid concurrent-writer conflicts. The RTX 3090 scheduler uses up to four visible -GPUs; the A100 scheduler uses up to two. - -The current 100-model $T=0.5$ top-$p$ control experiment may continue as a -supplementary study. Its availability-selected cohort and existing artifacts -are not substituted into the revised primary cohort. The two primary shards -should be launched only after that control run finishes. If they share a model -cache, cache cleanup must not evict a model still required by the other shard. - -Transient failures are retried and recorded. Progress reports include planned, -successful, failed, and missing models plus failure-status counts. Missing -cells are never represented as zero accuracy and curves are not interpolated -through absent cells. - -\section{Reproducibility and provenance} - -The experiment will preserve: +\textbf{Primary design} \begin{itemize} - \item exact cohort file and SHA-256 hash; - \item prompt dataset and seed; - \item model ID and available model metadata; - \item temperature, top-$p$, sampling flag, generation seed, and token limit; - \item response files and independent repeat suffixes; - \item sentence encoder, DNA dimension, projection method, and projection seed; - \item run manifests, status journals, failure summaries, predictions, and - machine-readable metric tables; and - \item the evaluation code and report version used to produce final figures. + \item Cohort: fixed validated 100 Hugging Face models. + \item Prompts: the same 100 classical-Chinese prompts for every model. + \item Conditions: $T=0$ once, plus + $T\in\{0.2,0.3,0.5,0.7\}\times p\in\{0.8,0.9,1.0\}$. + \item Split: repeat 1 is reference/training; repeat 2 is held-out query. + \item Primary metric: exact-model Top-1; Top-3, Top-5, and MRR are secondary. + \item Deployment view: decoding parameters are hidden, with only + $T\leq\tau$ known. \end{itemize} -The unsuffixed historical gallery will not be used for the primary causal -interpretation because its temperature and top-$p$ were not recorded. It may be -retained as a clearly labelled legacy reference-sensitivity analysis. +\textbf{Why the Proposed column is still blank.} +The same 84 models and held-out queries can be evaluated with exact RBF-MMD at +$K=1$. However, one reference and one query response per prompt do not test a +multi-sample distribution: at $K=1$, MMD is effectively a kernel point +distance. In addition, the existing eight-model pilot used character +n-gram hashing rather than the response encoder used for the main DNA table. +Its saturated 1.000 accuracy is therefore neither difficult nor directly +comparable. -\section{Planned results} - -\subsection{Cohort and completion table} - -\begin{table}[ht] +\begin{table}[h] \centering -\caption{Cohort completion and availability. Populate after generation.} -\begin{tabular}{lrrrr} +\small +\caption{Data required for a disjoint, matched-budget distributional test.} +\begin{tabular}{lcc} \toprule -Condition & Planned & Successful & Failed & Common cohort \\ +Budget & Reference samples per prompt & Query samples per prompt \\ \midrule -$T=0$ & 100 & \todoresult & \todoresult & \todoresult \\ -$T=0.2$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ -$T=0.3$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ -$T=0.5$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ -$T=0.7$, all $p$ & 300 & \todoresult & \todoresult & \todoresult \\ +$K=1$ & 1 & 1 \\ +$K=2$ & 2 & 2 \\ +$K=4$ & 4 & 4 \\ +$K=8$ & 8 & 8 \\ \bottomrule \end{tabular} \end{table} -Counts in this table are per repeat unless stated otherwise. The final version -will additionally report failure reasons and retained/excluded cohort -distributions. +\textbf{Defensible next step for the proposed method.} +First run an explicitly labelled ``RBF-MMD, $K=1$'' analysis on the exact +84-model cohort using the same encoder, prompts, repeat split, and queries. +This can check the paired pipeline without new generation. A claim about the +proposed multi-sample advantage requires additional independent responses and +matched $K\geq2$ cosine/mean baselines; it should not be inferred from the +current table. -\subsection{Per-setting exact classification} +\textbf{Main limitations} -\begin{longtable}{ccrrrr} -\caption{Held-out exact-model performance. Every entry will be reported with a -95\% model-cluster confidence interval.}\label{tab:per-setting}\\ -\toprule -$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ -\midrule -\endfirsthead -\toprule -$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ -\midrule -\endhead -0.0 & N/A & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.2 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.2 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.2 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.3 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.3 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.3 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.5 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.5 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.5 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.7 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.7 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.7 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ -\bottomrule -\end{longtable} +\begin{itemize} + \item The exploratory cohort was selected by artifact availability. + \item Only $T=0.2$ and 0.3 currently have comparable held-out results. + \item Two rounds quantify model-level uncertainty, not variability across + independent generation rounds. + \item The shared $T=0.5$ seed-42 runs require a duplicate/independence audit. +\end{itemize} + +\clearpage +\section{Schedule, decisions, and next report} -\subsection{Bounded unknown-temperature deployment} +The latest completed four-GPU batch produced 73 successes in 12.43 hours +(5.87 successes/hour). At that rate, 1,248 remaining artifacts require about +213 four-GPU hours, or 8.9 days of continuous generation, before retry and +audit allowances. -\begin{table}[ht] +\begin{table}[h] \centering -\caption{Temperature-agnostic deployment results. The classifier is not given -temperature or top-$p$.} -\begin{tabular}{clrrr} +\small +\caption{Conditional completion forecast.} +\begin{tabular}{p{0.32\linewidth}p{0.20\linewidth}p{0.38\linewidth}} \toprule -$\tau$ & Method & Mixed accuracy & Worst setting & MRR \\ +Milestone & Expected & Completion criterion \\ \midrule -0.2 & Original cosine & \todoresult & \todoresult & \todoresult \\ - & Linear SVM & \todoresult & \todoresult & \todoresult \\ - & RBF SVM & \todoresult & \todoresult & \todoresult \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\addlinespace -0.3 & Original cosine & \todoresult & \todoresult & \todoresult \\ - & Linear SVM & \todoresult & \todoresult & \todoresult \\ - & RBF SVM & \todoresult & \todoresult & \todoresult \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\addlinespace -0.5 & Original cosine & \todoresult & \todoresult & \todoresult \\ - & Linear SVM & \todoresult & \todoresult & \todoresult \\ - & RBF SVM & \todoresult & \todoresult & \todoresult \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\addlinespace -0.7 & Original cosine & \todoresult & \todoresult & \todoresult \\ - & Linear SVM & \todoresult & \todoresult & \todoresult \\ - & RBF SVM & \todoresult & \todoresult & \todoresult \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ +Missing/failed backfill & In progress & Four workers active on GPUs +0, 1, 2, and 4 since July 30 \\ +Generation and retries & Aug.\ 11--12 & 2,600 admissible successes \\ +Independence/provenance audit & Aug.\ 12 & Split passes seed and duplicate checks \\ +Primary evaluation and figures & Aug.\ 13--14 & Final common-cohort outputs \\ +Revised primary report & Aug.\ 14 & Exploratory values replaced by primary results \\ \bottomrule \end{tabular} \end{table} -\subsection{Planned figures} +\textbf{Schedule assessment.} +August 14 remains feasible only if the active four-GPU backfill continues at +approximately the observed throughput. Every +additional idle day moves the forecast by at least one day. Persistent +$T=0.7$ failures or a distinct-seed $T=0.5$ backfill would also extend it. -The final report will contain: +\textbf{Decisions for the next report} \begin{enumerate} - \item exact Top-1 versus temperature, faceted by top-$p$, with one curve per - representation/classifier and 95\% confidence bands; - \item accuracy loss relative to $T=0$ and estimated degradation slopes; - \item mixed and worst-setting accuracy versus temperature bound $\tau$; - \item stacked tree-aware Top-1 error severity versus temperature; and - \item accuracy versus response-sample/token budget for the mean and - distributional representations. + \item Report temperature effects separately by top-$p$; do not present only + a marginal temperature coefficient. + \item Treat classifier mitigation as exploratory until the validated-100 + common cohort is complete. + \item Add the proposed method only under a clearly stated sample budget and + matched encoder/query split. + \item Keep the main report short; move protocol commands, manifests, and + extended validity checks to an appendix or repository files. \end{enumerate} -\section{Planned interpretation} - -The final conclusions will be limited to what the completed protocol supports: - -\begin{itemize} - \item the effect of query-side decoding conditions relative to explicit, - independently generated galleries; - \item classifier mitigation on the same exact-model task; - \item representation gains under matched budgets; and - \item expected and worst observed performance when decoding parameters are - hidden but temperature is bounded. -\end{itemize} +\textbf{Reproducibility pointers.} +The fixed cohort is +\path{configs/rand_chinese_validated100_latest.jsonl}; the live machine-readable +grid is \path{reports/primary_robustness_run_grid.csv}; exploratory predictions +are under \path{out/small_scale_section_6_11}; and the backfill log is +\path{logs/primary_grid_resume_20260730-1235_master.log}. -No lineage-robustness conclusion will be based on aggregate positives dominated -by same-model pairs. No method will be declared superior from a point estimate -alone, and no causal temperature conclusion will use the historical -unknown-setting gallery. - -\appendix - -\section{Execution commands} - -Build or verify the fixed cohort: - -\begin{lstlisting}[language=bash] -python3 scripts/build_experiment_cohort.py --target 100 \ - --output configs/rand_chinese_stratified_100.jsonl \ - --audit-output configs/rand_chinese_stratified_100_audit.json -python3 scripts/build_primary_robustness_100_shards.py -\end{lstlisting} - -After the existing control run finishes, launch the two hardware shards from -separate terminals. Each command dynamically uses the visible GPUs up to its -stated worker cap: - -\begin{lstlisting}[language=bash] -mkdir -p logs && nohup env MAX_CONCURRENT_GPUS=4 \ - bash scripts/run_primary_robustness_100_shard.sh rtx3090 \ - > logs/primary_robustness_100_rtx3090_master.log 2>&1 & - -mkdir -p logs && nohup env MAX_CONCURRENT_GPUS=2 \ - bash scripts/run_primary_robustness_100_shard.sh a100 \ - > logs/primary_robustness_100_a100_master.log 2>&1 & -\end{lstlisting} - -Run the preregistered exact-model and tree-aware analyses after all four repeats -are complete: - -\begin{lstlisting}[language=bash] -bash scripts/run_primary_analysis.sh -\end{lstlisting} - -The primary analysis outputs are written beneath -\path{out/primary_robustness_100_analysis}. The exact-model evaluator produces -per-setting metrics, per-query predictions, bounded-deployment tables, cohort -metadata, and JSON summaries. - -\section{Implementation map} - -\begin{longtable}{p{0.34\linewidth}p{0.58\linewidth}} -\toprule -File & Purpose \\ -\midrule -\endfirsthead -\toprule -File & Purpose \\ -\midrule -\endhead -\path{scripts/run_primary_robustness_experiment.sh} - & Launch the 100-model, 13-condition, four-repeat extraction sweep. \\ -\path{scripts/run_primary_robustness_100_shard.sh} - & Run the collision-safe RTX 3090 or A100 hardware shard. \\ -\path{scripts/build_primary_robustness_100_shards.py} - & Assign 77 models to RTX 3090 workers and the 23 largest models to A100 - workers; record shard membership and hashes. \\ -\path{scripts/run_primary_analysis.sh} - & Construct the fixed train/test repeat split and run primary analyses. \\ -\path{scripts/evaluate_exact_dna_classification.py} - & Compare cosine centroids and multiclass SVMs on exact identity; produce - bounded unknown-temperature results and confidence intervals. \\ -\path{scripts/analyze_tree_aware_errors.py} - & Produce hierarchical retrieval and tree-distance error summaries. \\ -\path{scripts/evaluate_dna_probe_sweep.py} - & Run legacy and separated binary verification tasks using all test - negatives by default. \\ -\path{scripts/run_rbf_mmd_pilot.py} - & Compare single-sample, matched mean-cosine, and exact RBF-MMD retrieval. \\ -\path{scripts/export_hf_model_tree.py} - & Export direct parent/base edges and connected components. \\ -\path{scripts/report_sweep_progress.py} - & Report completion, missing models, and failure-status counts. \\ -\bottomrule -\end{longtable} +\vfill +\begin{center} +\textit{Primary conclusions remain pending until the validated-100 grid and +held-out split are complete.} +\end{center} \end{document} diff --git a/docs/primary_robustness_experiment_full_updated_2026-08-05.tex b/docs/primary_robustness_experiment_full_updated_2026-08-05.tex new file mode 100644 index 0000000..08bf815 --- /dev/null +++ b/docs/primary_robustness_experiment_full_updated_2026-08-05.tex @@ -0,0 +1,1138 @@ +\documentclass[11pt,a4paper]{article} + +\usepackage[margin=1in]{geometry} +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} +\usepackage{amsmath,amssymb} +\usepackage{booktabs} +\usepackage{graphicx} +\usepackage{longtable} +\usepackage{multirow} +\usepackage{xcolor} +\usepackage{hyperref} +\usepackage{listings} +\usepackage{enumitem} + +\hypersetup{hidelinks} +\lstset{ + basicstyle=\ttfamily\small, + breaklines=true, + frame=single, + columns=fullflexible, + keepspaces=true +} + +\newcommand{\todoresult}{\textcolor{gray}{\textit{Not yet available}}} +\newcommand{\method}[1]{\texttt{#1}} +\newcommand{\demovalue}[1]{\textcolor{blue!65!black}{#1\textsuperscript{*}}} + +\title{Primary LLM-DNA Robustness Experiment\\ +\large Updated Full Experimental Protocol and Progress Report} +\author{} +\date{Updated August 5, 2026} + +\begin{document} +\maketitle + +\section*{Executive summary} +This document records the design and current execution status of the primary +LLM-DNA decoding robustness experiment. The study +will measure how temperature and top-$p$ affect exact-model identification, +whether a classifier can mitigate the resulting accuracy loss, and whether a +multi-sample distributional representation improves robustness under a matched +generation budget. The fixed primary cohort contains 100 validated models. +The experiment contains one deterministic condition and a complete +$4\times3$ stochastic grid, with two generation rounds per condition. The +principal deployment setting +assumes that temperature and top-$p$ are hidden at inference time and only an +upper bound on temperature is known. As of August 5, 2,528 of 2,600 planned +artifacts are successful. The remaining 72 terminal failures affect 41 distinct +models, leaving a strict all-grid common cohort of 59. Small-scale exploratory +results are reported separately and are not substitutes for the final primary +analysis. +\par + +\section{Status and scope} + +\textbf{Primary experiment status (August 5, 2026): generation pass complete, +targeted recovery required.} The grid contains 2,528/2,600 successful artifacts +(97.2\%). Two of 13 settings are fully complete. The 72 remaining slots are +52 \texttt{failed\_missing\_file} and 20 +\texttt{failed\_empty\_response}; no ordinary pending slots remain. The strict +intersection across all 26 runs contains 59 models and is below the +preregistered minimum of 80. Section~\ref{sec:small-demo} contains explicitly +labelled exploratory demonstrations that must not be interpreted as final +primary findings. + +A resource-feasibility review reduced the plan from 300 models and four repeats +to 100 models and two rounds. Repeat 1 is the reference/training split and +repeat 2 is held out. Exact identification is a 100-way closed-set task before +failure filtering. + +\begin{table}[htbp] +\centering +\caption{Live primary grid on August 5. Each cell combines two 100-model rounds.} +\label{tab:live-grid} +\begin{tabular}{crrr} +\toprule +Temperature & $p=0.8$ & $p=0.9$ & $p=1.0$ \\ +\midrule +$0$ & -- & -- & $200/200$ \\ +$0.2$ & $199/200$ & $200/200$ & $198/200$ \\ +$0.3$ & $199/200$ & $189/200$ & $185/200$ \\ +$0.5$ & $198/200$ & $196/200$ & $176/200$ \\ +$0.7$ & $196/200$ & $197/200$ & $195/200$ \\ +\bottomrule +\end{tabular} +\end{table} + +The high slot-completion rate and the smaller complete-case cohort are not +contradictory. Failures occur in different models across cells: 72 failed slots +are distributed over 41 models, so $100-41=59$ models succeed everywhere. +For setting-specific paired evaluation, the usable r1--r2 cohorts remain much +larger: + +\begin{table}[htbp] +\centering +\small +\caption{Paired cohort with both rounds successful in each setting.} +\label{tab:paired-cohorts} +\begin{tabular}{crrcrrr} +\toprule +$T$ & $p$ & Paired $N$ && $T$ & $p$ & Paired $N$ \\ +\midrule +0.0 & 1.0 & 100 && 0.3 & 0.9 & 93 \\ +0.2 & 0.8 & 99 && 0.3 & 1.0 & 89 \\ +0.2 & 0.9 & 100 && 0.5 & 0.8 & 98 \\ +0.2 & 1.0 & 98 && 0.5 & 0.9 & 97 \\ +0.3 & 0.8 & 99 && 0.5 & 1.0 & 77 \\ +0.7 & 0.8 & 96 && 0.7 & 0.9 & 97 \\ +0.7 & 1.0 & 95 && & & \\ +\bottomrule +\end{tabular} +\end{table} + +The common cohorts for bounded analyses are 97 at $\tau=0.2$, 86 at +$\tau=0.3$, 63 at $\tau=0.5$, and 59 at $\tau=0.7$. Targeted recovery should +first repair 21 models that each lack only one slot; if successful, this is the +minimum route to restore an 80-model all-grid cohort. + +This experiment replaces the 77-model interim analysis as the primary +robustness study. The earlier analysis remains a legacy diagnostic because it +used an incomplete sweep, one generation repeat per reported cell, an +historical reference gallery with unknown decoding parameters, and a binary +same-or-related task that was not directly comparable to exact-model Top-1 +retrieval. + +\section{Research questions and hypotheses} + +\subsection{Primary research questions} + +\begin{enumerate}[label=\textbf{RQ\arabic*.}] + \item How do temperature and top-$p$ affect exact-model classification from + LLM-DNA representations? + \item On the same exact-model task, can a classifier trained on the full DNA + vector reduce the accuracy degradation relative to cosine retrieval? + \item Does the proposed multi-sample distributional representation improve + robustness beyond original DNA under the same response and token + budget? + \item If temperature and top-$p$ are unknown at inference time, but + temperature is known to satisfy $T\leq\tau$, what average and + worst-setting accuracy can be expected? +\end{enumerate} + +\subsection{Secondary research questions} + +\begin{enumerate}[label=\textbf{SRQ\arabic*.}] + \item When exact identification fails, does the predicted model remain close + to the true model in the Hugging Face model tree? + \item Do direct parent--child, indirect, and disconnected model pairs exhibit + different DNA-distance distributions as decoding stochasticity rises? + \item How sensitive are the conclusions to response encoder, truncation, + projection dimension, projection seed, and the number of response + samples? +\end{enumerate} + +The main hypothesis is that temperature will cause a larger reduction in exact +classification accuracy than top-$p$ over the planned range. Classifier and +distributional-method benefits will be judged by paired accuracy differences +and by a shallower degradation curve, rather than by comparing accuracies from +different tasks. + +\section{Model cohort} + +\subsection{Fixed validated cohort} + +The primary cohort is the 100-model list exported from the latest validated +control manifest. Historical artifacts count only when their exact model ID, +decoding cell, and round match this cohort and grid. Performance was not used +to add or remove models. + +The fixed artifacts are: + +\begin{itemize} + \item cohort: \path{configs/rand_chinese_validated100_latest.jsonl}; + \item canonical manifest: + \path{out/top_p_control_t05_validated100_20260719/top_p_control_t05_t05_p06_r2/manifest.json}; + \item live audit: \path{reports/primary_robustness_run_grid.csv}; and + \item cohort SHA-256: + \texttt{9043a62b95436b9fc92671b6f5b11b15d427fddd745dba2f84c112d97e850e6d}. +\end{itemize} + +Any operational replacement would define a new cohort version and require the +grid and every common-cohort count to be regenerated. + +\subsection{Completion requirement} + +The intended final analysis uses all 100 models. The automated primary analysis +will refuse to run if fewer than 80 models are common to all required training +and test runs. Missing models should first be backfilled. If the final common +cohort remains below 100, the report will provide success rates and failure +reasons by decoding cell and compare retained and excluded models by size and +architecture. + +\section{Prompt set and DNA construction} + +Every model receives the same 100 synthetic classical-Chinese prompts from +\path{data/rand/rand_dataset_chinese.json}. The prompt construction and random +projection seed remain fixed at 42 for every model, setting, and repeat. + +For the original representation, each response is encoded using +\method{all-mpnet-base-v2}. The 100 response embeddings are concatenated and +mapped to a 128-dimensional DNA vector by a seeded Gaussian random projection. +The current maximum response length is 1024 generated tokens. The saved +metadata records at least the model ID, dataset, prompt count, temperature, +top-$p$, sampling flag, generation seed, maximum length, projection seed, +encoder, and DNA dimension. + +Because the sentence encoder is primarily English-oriented and truncates long +inputs, the final study will additionally report response-length and encoder +token-length distributions, the fraction exceeding the encoder limit, and a +sensitivity analysis with a Chinese or multilingual response encoder. + +\section{Decoding design} + +\subsection{Factorial grid} + +The deterministic condition is +\[ + T=0, \qquad \text{sampling disabled}, \qquad p=1.0\text{ (recorded only)}. +\] +Top-$p$ is inactive when sampling is disabled; consequently, $T=0$ appears once +and is not repeated as three nominal top-$p$ cells. + +For stochastic decoding, the complete grid is +\[ + T\in\{0.2,0.3,0.5,0.7\}, \qquad + p\in\{0.8,0.9,1.0\}. +\] +This gives 13 decoding conditions in total. Two generation rounds are planned +for every condition, giving 26 run cells and +\[ + 100\ \text{models}\times13\ \text{conditions}\times2\ \text{rounds} + =2{,}600 +\] +successful artifacts before retries. + +\subsection{Generation rounds and seeds} + +The response-sampling seeds are: + +\begin{table}[ht] +\centering +\caption{Generation-repeat assignment. The projection seed remains 42 in all rows.} +\label{tab:repeat-assignment} +\begin{tabular}{ccc} +\toprule +Repeat & Generation seed & Primary role \\ +\midrule +1 & 42001 (42 in existing $T=0.5$ control) & training/reference \\ +2 & 42002 (42 in existing $T=0.5$ control) & held-out query \\ +\bottomrule +\end{tabular} +\end{table} + +The shared $T=0.5$ cells require a duplicate/independence audit because both +existing rounds use seed 42. Historical sweep artifacts also did not record a +separate response-generation seed. These limitations affect causal +temperature interpretation but do not invalidate paired method comparisons +when every method reads exactly the same responses. + +No response, DNA vector, or round may appear in both the training/reference +split and the held-out query split. Generation seed and projection seed are +stored separately so that response variability is not confounded with a change +in the random projection. + +\section{Primary exact-model task} + +Let $\mathcal{M}$ be the fixed common model cohort. Given a query DNA vector +$\mathbf{d}_{m,s,r}$ generated by model $m$, decoding setting $s=(T,p)$, and +held-out repeat $r=2$ in Table~\ref{tab:repeat-assignment}, each method +evaluates every candidate +$j\in\mathcal{M}$. Let $\mathcal{D}^{\mathrm{train}}_j$ denote the set of +training DNA vectors available for candidate model $j$. Its gallery centroid is +\[ + \boldsymbol{\mu}_j + =\frac{1}{|\mathcal{D}^{\mathrm{train}}_j|} + \sum_{\mathbf{x}\in\mathcal{D}^{\mathrm{train}}_j}\mathbf{x}. +\] +The standard cosine similarity and cosine distance both take the query and the +candidate centroid as inputs: +\[ + S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) + =\frac{\mathbf{d}^{\mathsf T}\boldsymbol{\mu}_j} + {\lVert\mathbf{d}\rVert_2\lVert\boldsymbol{\mu}_j\rVert_2}, + \qquad + D_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) + =1-S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j). +\] +Thus, the cosine rule may be written equivalently as +\[ + \widehat{m}^{(\mathrm{cos})} + =\arg\min_{j\in\mathcal{M}} + D_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j) + =\arg\max_{j\in\mathcal{M}} + S_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j). +\] +No square is applied in the standard cosine distance used by the evaluation +code; each Euclidean norm already satisfies +$\lVert\mathbf{d}\rVert_2=(\sum_k d_k^2)^{1/2}$, but the cosine value itself is +not squared. The squared norm appearing below belongs instead to the RBF kernel. +For the SVM rules, the explicit class-$j$ decision scores and predictions are +\[ + f_j^{(a)}(\mathbf{d})= + \begin{cases} + \mathbf{w}_j^{\mathsf T}\mathbf{z}(\mathbf{d})+b_j, + & a=\mathrm{lin},\\[4pt] + \displaystyle + \sum_{i=1}^{n_{\mathrm{SV}}}\alpha_{ij} + \exp\!\left( + -\gamma\lVert\mathbf{z}(\mathbf{d})-\mathbf{z}(\mathbf{x}_i)\rVert_2^2 + \right)+b_j, + & a=\mathrm{rbf}. + \end{cases} + \qquad + \widehat{m}^{(a)} + =\arg\max_{j\in\mathcal{M}}f_j^{(a)}(\mathbf{d}_{m,s,r}), + \quad a\in\{\mathrm{lin},\mathrm{rbf}\}. +\] +Here $\mathbf{z}(\cdot)$ is the feature-wise standardization learned from the +training split, $(\mathbf{w}_j,b_j)$ are the linear-SVM parameters, and +$\{\mathbf{x}_i,\alpha_{ij}\}_{i=1}^{n_{\mathrm{SV}}}$ and $\gamma$ are the +RBF-SVM support vectors, effective class-$j$ coefficients, and kernel +bandwidth parameter, respectively. The prediction is correct only if +$\widehat{m}^{(a)}=m$. Thus, cosine and SVM methods +are evaluated on identical multiclass labels, queries, and candidate sets. + +\subsection{Methods} + +\begin{table}[ht] +\centering +\caption{Planned representation $\times$ classifier comparison.} +\label{tab:method-matrix} +\begin{tabular}{lll} +\toprule +Representation & Decision rule & Purpose \\ +\midrule +Original 128-D DNA & cosine nearest centroid & primary retrieval baseline \\ +Original 128-D DNA & multiclass linear SVM & classifier-only ablation \\ +Original 128-D DNA & multiclass RBF SVM & nonlinear classifier ablation \\ +Multi-sample mean DNA & cosine nearest centroid & matched-budget averaging baseline \\ +Distributional DNA & exact RBF-MMD & proposed representation \\ +Distributional DNA & multiclass SVM & full proposed method, if applicable \\ +\bottomrule +\end{tabular} +\end{table} + +The linear and RBF SVMs consume the full 128-dimensional DNA vector. The legacy +linear SVM using only negative cosine distance is not considered an exact-model +classification improvement because a monotone linear transformation of cosine +distance cannot alter closed-set candidate ranking. It remains only a binary +verification baseline. + +\subsection{Controlled comparison of old and new methods} + +Fairness is defined by holding the data and evaluation unit fixed while changing +only the representation or decision rule. Every paired comparison uses the +same model IDs, prompts, response files, decoding cell, reference/query split, +candidate set, and metric. + +\begin{table}[htbp] +\centering +\small +\caption{Controlled method-comparison matrix.} +\label{tab:controlled-method-comparison} +\begin{tabular}{p{0.28\linewidth}p{0.20\linewidth}p{0.42\linewidth}} +\toprule +Comparison & What changes & What remains fixed \\ +\midrule +Cosine vs linear/RBF SVM & Decision rule only & Same 128-D DNA, queries, +labels, split, and candidate models \\ +Mean cosine vs RBF-MMD at $K$ & Representation/distance only & Same $K$ +responses per prompt, encoder, prompts, tokens, reference and query sets \\ +$K=1$ point check & Kernel distance only & Same single response on each side; +explicitly not evidence for a multi-sample advantage \\ +\bottomrule +\end{tabular} +\end{table} + +Linear/RBF-SVM hyperparameters are selected only within repeat 1. The RBF-MMD +bandwidth is calibrated only from reference/calibration prompts and then frozen. +Method differences are computed per identical held-out query and summarized by +a paired model-cluster bootstrap. Historical unknown seeds are shared by all +methods in an exploratory comparison rather than being treated as a method +difference. + +\subsection{Known-setting descriptive evaluation} + +For each held-out condition $(T,p)$ and each method $a$, the experiment reports +\[ + A_a(T,p)=\frac{1}{|\mathcal{Q}_{T,p}|} + \sum_{q\in\mathcal{Q}_{T,p}}\mathbb{I}[\widehat{m}_{a,q}=m_q]. +\] +Top-1 is primary; Top-3, Top-5, and mean reciprocal rank (MRR) are secondary. +The change from the deterministic condition is +\[ + \Delta A_a(T,p)=A_a(T,p)-A_a(0). +\] +All per-setting comparisons will use the same held-out model queries. + +\section{Unknown-temperature bounded deployment} + +The main deployment scenario assumes that the exact decoding parameters are +not provided to the classifier. For a temperature bound +\[ + \tau\in\{0.2,0.3,0.5,0.7\}, +\] +one classifier is trained using repeat 1 from every allowed setting with +$T\leq\tau$. Temperature and top-$p$ are omitted from its features. It is +then evaluated on held-out repeat 2 from all allowed settings. + +To avoid overweighting temperatures that happen to have more nominal cells, +repeats are first averaged within each $(T,p)$ cell. The primary mixed accuracy +is the equal-cell mean +\[ + A_a^{\mathrm{mix}}(\tau) + =\frac{1}{|\mathcal{S}_\tau|} + \sum_{s\in\mathcal{S}_\tau} A_a(s), + \qquad + \mathcal{S}_\tau=\{(T,p):T\leq\tau\}. +\] +The robustness guarantee is summarized by the worst observed setting +\[ + A_a^{\mathrm{worst}}(\tau) + =\min_{s\in\mathcal{S}_\tau} A_a(s). +\] +These quantities directly answer how accurately the system performs when the +decoding setting is hidden but its temperature is bounded. + +\section{Distributional and matched-budget evaluation} + +The distributional method operates on repeated response samples rather than a +single point estimate. It will be compared against both a single-sample cosine +baseline and a matched-budget mean/centroid baseline. Every method comparison +must use the same number of response samples, prompts, encoder, and total +generated-token budget unless the budget difference is explicitly plotted. + +The planned sample-budget ablation is +\[ + K\in\{1,2,4,8\}, +\] +subject to the number of available independent responses. For each $K$, the +report will include accuracy, generated tokens, reference storage, and analysis +time. This distinguishes gains due to the representation from gains obtained +merely by consuming more samples. + +With the present two-round grid, only a disjoint $K=1$ reference/query +comparison is immediately possible on the same 84-model exploratory cohort. +At $K=1$, exact RBF-MMD is a kernel point-distance check and must not be +presented as evidence for a multi-sample distributional advantage. A fair +$K=2$ test requires two independent reference and two independent query +responses per prompt; $K=4$ and $K=8$ require correspondingly larger matched +sample budgets. The earlier eight-model pilot used character n-gram hashing +and saturated at 1.000, so it is retained only as an implementation check. + +\section{Secondary verification and tree-aware analysis} + +\subsection{Binary verification} + +If binary pairwise verification is reported, its tasks are separated into: + +\begin{enumerate} + \item exact same-model versus unrelated; + \item lineage-only (off-diagonal related) versus unrelated; and + \item the legacy same-or-related versus unrelated task. +\end{enumerate} + +Training may downsample negative pairs or use class weighting. Testing uses all +available pairs at their natural prevalence. The main binary metrics are +precision--recall AUC, precision, recall, specificity, Matthews correlation +coefficient, false positives per query, and ROC-AUC. Balanced-test accuracy may +be shown only as an explicitly labelled diagnostic. + +\subsection{Model-tree distance} + +The Hugging Face relation exporter stores direct base-model edges in addition +to connected components. Using the undirected direct-edge graph, the shortest +tree distance is +\[ + d_{\mathrm{tree}}(i,j)= + \begin{cases} + 0, & i=j,\\ + 1, & i\text{ and }j\text{ share a direct edge},\\ + 2,3,\ldots, & \text{connected by the corresponding number of edges},\\ + \infty, & \text{disconnected}. + \end{cases} +\] + +The final report will show DNA-distance distributions for exact, direct +one-hop, indirect two-hop, indirect three-or-more-hop, same-component with +unknown hop distance, and disconnected pairs. For Top-1 predictions, cumulative +tree-aware accuracy will be reported as exact, within one hop, within two hops, +and within the same connected component. This analysis describes error severity +and does not replace exact-model accuracy as the primary endpoint. + +\section{Statistical analysis plan} + +\subsection{Uncertainty} + +Every accuracy and MRR estimate will include a 95\% confidence interval. +Resampling will preserve dependence among observations: + +\begin{itemize} + \item model IDs are resampled as clusters across methods and settings; + \item tree-related analyses use model-tree component clusters where relevant. +\end{itemize} + +Pair-level observations sharing a query model, reference model, or tree +component will not be treated as independent Bernoulli trials. With fixed +training and test roles for rounds 1 and 2, the primary confidence intervals +quantify model-level rather than generation-repeat uncertainty. + +\subsection{Paired comparisons} + +Method differences are computed on identical held-out queries. The principal +quantity is +\[ + \delta_{a,b}(T,p)=A_a(T,p)-A_b(T,p), +\] +with a paired cluster-bootstrap confidence interval. The analysis will also +test whether a method changes the temperature degradation slope, for example +with a mixed-effects logistic model of the form +\[ + \operatorname{logit}\Pr(\mathrm{correct}) + =\beta_0+\beta_1 T+\beta_2 p+\beta_3\mathrm{method} + +\beta_4(T\times\mathrm{method}) + +\beta_5(p\times\mathrm{method})+u_{\mathrm{model}}+u_{\mathrm{repeat}}. +\] +The interaction terms, rather than isolated cell differences, address whether +a method mitigates robustness loss. + +\subsection{Multiplicity and exploratory analyses} + +Top-1 accuracy and bounded mixed accuracy are primary endpoints. Top-3, Top-5, +MRR, tree-aware accuracy, encoder ablations, and projection ablations are +secondary. Any unplanned subgroup or hyperparameter analysis will be labelled +exploratory. If many method-by-setting significance tests are presented, a +false-discovery-rate correction will be applied. + +\section{Ablations and validity checks} + +The following checks are planned where artifacts and compute permit: + +\begin{enumerate} + \item projection dimensions $\{64,128,256,512\}$ and at least five projection + seeds; + \item an unprojected or prompt-level distance baseline; + \item prompt bootstrap and split-half reliability; + \item response encoder comparison including a Chinese or multilingual + encoder; + \item response-token and encoder-token length distributions and truncation + rates by setting; + \item fixed $T=0$ gallery, same-setting repeat gallery, and multi-setting + centroid gallery; + \item leave-one-setting-out evaluation for unseen decoding configurations; + \item performance by model size, architecture, and chat/base status; and + \item failure probability by model attributes and decoding setting. +\end{enumerate} + +\clearpage +\section{Small-scale demonstration report using existing artifacts} +\label{sec:small-demo} + +\subsection{Scope and split} + +To check that the analyses in Sections 6--11 are executable before the primary +sweep is complete, small exploratory demonstrations were run on July 23, 2026, +using only artifacts already present under \path{out/rand_chinese}. No new model +responses were generated. Repeat 1 was used for training/reference data and +repeat 2 for testing. Six complete stochastic cells were available: +\[ + T\in\{0.2,0.3\},\qquad p\in\{0.8,0.9,1.0\}. +\] +The $T=0.5$ and $T=0.7$ cells were excluded because they did not have a +sufficiently complete independent-repeat split. These demonstrations use +convenience cohorts determined by artifact availability, not the fixed +100-model primary cohort. They are therefore pipeline checks and exploratory +effect estimates only. + +\subsection{Executive summary} + +\noindent\fbox{\parbox{0.94\linewidth}{ +\textbf{Demo outcome.} The available artifacts support an end-to-end +demonstration of exact classification, bounded unknown-temperature evaluation, +paired model-cluster uncertainty, exact-only binary verification, a limited +distributional comparison, a limited tree-aware analysis, and a gallery +sensitivity check. Across the six complete cells, the pooled linear SVM had the +highest equal-cell Top-1 (0.722), followed by RBF SVM (0.688) and cosine +retrieval (0.621). The hardest observed cell was $(T,p)=(0.3,1.0)$. These are +exploratory results from availability-selected cohorts and must not be read as +the outcome of the 100-model primary experiment. +}} + +\medskip +Table~\ref{tab:demo-coverage} maps each planned protocol component to what +could actually be demonstrated. ``Partial'' means that the code path ran but +the available cohort or repeat structure could not exercise the full planned +analysis. + +\begin{table}[htbp] +\centering +\caption{Coverage of Sections 6--11 by the small-scale demonstration.} +\label{tab:demo-coverage} +\begin{tabular}{p{0.13\linewidth}p{0.35\linewidth}p{0.23\linewidth}p{0.15\linewidth}} +\toprule +Protocol section & Demonstrated analysis & Available scale & Status \\ +\midrule +6: Exact task + & Cosine centroid, linear SVM, and RBF SVM on identical queries + & Six cells; 84 common models & Complete demo \\ +7: Bounded deployment + & Equal-cell mixed and worst-setting accuracy for $\tau=0.2,0.3$ + & 89 and 84 models & Complete demo \\ +8: Distributional + & Single cosine, mean cosine, and exact RBF-MMD + & Eight models; 20 prompts; at most two repeats & Partial \\ +9: Verification + & Exact-only linear probe at natural pair prevalence + & 95 models; 9,025 pairs per direction & Complete demo \\ +9: Tree-aware + & Exact versus disconnected error and distance summaries + & 95 models; no related pairs & Partial \\ +10: Statistics + & Model-cluster CIs and paired method differences + & One held-out repeat per cell & Partial \\ +11: Validity + & Fixed-$T=0$ versus pooled-gallery diagnostic + & 86 versus 84 models & Partial \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Exact classification and bounded deployment demo} + +For $\tau=0.2$, the common cohort contained 89 models and the training pool +contained the three repeat-1 cells at $T=0.2$. For $\tau=0.3$, requiring all six +cells reduced the common cohort to 84 models. The classifier was not given +temperature or top-$p$. Table~\ref{tab:demo-per-setting} reports held-out +repeat-2 Top-1 accuracy for the $\tau=0.3$ pooled training experiment. + +\begin{table}[ht] +\centering +\caption{Exploratory held-out Top-1 accuracy with 84 common models.} +\label{tab:demo-per-setting} +\begin{tabular}{ccrrr} +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM \\ +\midrule +0.2 & 0.8 & 0.643 & 0.821 & 0.702 \\ +0.2 & 0.9 & 0.726 & 0.845 & 0.762 \\ +0.2 & 1.0 & 0.595 & 0.643 & 0.643 \\ +0.3 & 0.8 & 0.774 & 0.869 & 0.857 \\ +0.3 & 0.9 & 0.690 & 0.786 & 0.762 \\ +0.3 & 1.0 & 0.298 & 0.369 & 0.405 \\ +\bottomrule +\end{tabular} +\end{table} + +Figure~\ref{fig:demo-exact-heatmaps} makes the setting-level pattern explicit. +The linear SVM led in four cells and tied in one, while the RBF SVM was highest +at $(0.3,1.0)$. All methods dropped sharply in that cell, so the demo does not +support a general monotone temperature effect independent of top-$p$. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/exact_top1_heatmaps.png} +\caption{Exploratory exact-model Top-1 by method and decoding cell. Darker +cells indicate higher accuracy. The equal-cell means correspond to the +$\tau=0.3$ pooled-training experiment.} +\label{fig:demo-exact-heatmaps} +\end{figure} + +The equal-cell bounded summaries are shown in +Table~\ref{tab:demo-bounded}. Confidence intervals resample model IDs as +clusters across the included cells. + +\begin{table}[ht] +\centering +\caption{Exploratory bounded-deployment Top-1 summaries.} +\label{tab:demo-bounded} +\begin{tabular}{clrrr} +\toprule +$\tau$ & Method & Mixed accuracy (95\% CI) & Worst cell & Models \\ +\midrule +0.2 & Cosine centroid & 0.596 (0.528, 0.667) & 0.562 & 89 \\ + & Linear SVM & 0.682 (0.614, 0.745) & 0.652 & 89 \\ + & RBF SVM & 0.629 (0.562, 0.700) & 0.584 & 89 \\ +\addlinespace +0.3 & Cosine centroid & 0.621 (0.554, 0.688) & 0.298 & 84 \\ + & Linear SVM & 0.722 (0.675, 0.772) & 0.369 & 84 \\ + & RBF SVM & 0.688 (0.633, 0.742) & 0.405 & 84 \\ +\bottomrule +\end{tabular} +\end{table} + +On identical queries, the paired model-cluster bootstrap difference between +linear SVM and cosine was $0.086$ (95\% CI $0.037$ to $0.135$) at +$\tau=0.2$ and $0.101$ (95\% CI $0.058$ to $0.147$) at $\tau=0.3$. The +corresponding RBF-SVM differences were $0.034$ (95\% CI $0.004$ to $0.071$) +and $0.067$ (95\% CI $0.026$ to $0.111$). These intervals quantify only this +availability-selected demo and do not test the primary hypotheses. + +Figure~\ref{fig:demo-bounded-paired} combines three views that should remain +distinct in interpretation: bounded performance, paired within-query method +gains, and the unpaired gallery diagnostic. In particular, the fixed-gallery +bars are not a controlled estimate of gallery effects because the gallery +cohort and training budget differ from the pooled condition. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/bounded_paired_gallery.png} +\caption{Bounded-deployment and sensitivity summary. Panel A shows equal-cell +mixed accuracy with model-cluster 95\% intervals and the worst observed cell. +Panel B shows paired Top-1 differences relative to cosine. Panel C is an +explicitly unpaired fixed-gallery versus pooled-gallery diagnostic.} +\label{fig:demo-bounded-paired} +\end{figure} + +\subsection{Distributional and matched-budget demo} + +The exact RBF-MMD pilot ran successfully on eight lexicographically selected +models, 20 common evaluation prompts, and two response repeats, using +$T=0.2,p=0.8$ as the reference setting. Single-sample cosine, mean-cosine, and +exact RBF-MMD each achieved Top-1 and MRR of 1.000 for the same-setting +comparison and for cross-setting queries at $(0.3,0.8)$ and $(0.3,0.9)$. The +same-setting split has only one repeat on each side, whereas the cross-setting +comparisons pool both repeats per model on each side. This is a successful +end-to-end demonstration, but the tiny cohort saturated all methods and +therefore provides no evidence that the distributional representation is +better. The available two repeats also do not support the planned +$K\in\{1,2,4,8\}$ ablation. + +\begin{table}[htbp] +\centering +\caption{Distributional pilot Top-1 (MRR was also 1.000 in every entry).} +\label{tab:demo-distributional} +\begin{tabular}{lrrr} +\toprule +Query comparison & Single cosine & Mean cosine & Exact RBF-MMD \\ +\midrule +Same $(0.2,0.8)$ & 1.000 & 1.000 & 1.000 \\ +Cross $(0.2,0.8)\rightarrow(0.3,0.8)$ & 1.000 & 1.000 & 1.000 \\ +Cross $(0.2,0.8)\rightarrow(0.3,0.9)$ & 1.000 & 1.000 & 1.000 \\ +\bottomrule +\end{tabular} +\end{table} + +This complete saturation is itself a useful demo diagnostic: a larger and more +difficult cohort is required before representation differences can be +estimated. It would be inappropriate to select a method from these pilot +numbers. + +\subsection{Binary verification and tree-aware demo} + +An exact-only linear verification probe was trained on one $T=0.2,p=0.8$ +repeat and tested on the other, in both directions, over 95 common models. +Testing retained all 8,930 negative pairs per direction, giving a natural +positive prevalence of $1/95$. Results are in +Table~\ref{tab:demo-verification}. + +\begin{table}[ht] +\centering +\caption{Exploratory exact-only binary verification at natural prevalence.} +\label{tab:demo-verification} +\begin{tabular}{lrrrrrr} +\toprule +Test repeat & PR-AUC & ROC-AUC & Precision & Recall & Specificity & MCC \\ +\midrule +1 & 0.334 & 0.832 & 0.049 & 0.726 & 0.851 & 0.163 \\ +2 & 0.373 & 0.869 & 0.045 & 0.747 & 0.831 & 0.155 \\ +\bottomrule +\end{tabular} +\end{table} + +Despite the apparently strong ROC-AUC, the probe produced 1,329 and 1,511 +false positives in the two directions, respectively. The resulting precision +near 5\% demonstrates why the primary verification report emphasizes PR-AUC, +MCC, specificity, and false positives per query rather than balanced accuracy +alone. + +The tree-aware cosine demo used repeat 1 at $(0.2,0.8)$ as the gallery and 95 +common models. Exact Top-1 was 0.526 for the same-setting repeat-2 query and +0.379 for the $(0.3,0.8)$ repeat-2 query. All 104 combined errors were between +models disconnected in the exported relation graph, so within-one-hop, +within-two-hop, and same-component accuracy did not exceed exact accuracy. +Mean cosine distance was 0.445 versus 0.929 for exact and disconnected pairs +in the same-setting comparison, and 0.566 versus 0.929 in the cross-setting +comparison. No direct or indirect related pair occurred within this +availability-selected cohort, so lineage-error severity could not be +demonstrated. + +Figure~\ref{fig:demo-verification-tree} emphasizes two practical limitations. +First, ROC-AUC obscures the false-positive burden when only one of 95 candidate +pairs is positive. Second, the tree-aware demo separates exact and disconnected +pairs but cannot validate the intended one-hop and indirect strata because no +such pair occurred in the common cohort. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/verification_tree_diagnostics.png} +\caption{Verification and tree-aware diagnostics. Panels A--B show binary +metrics and false positives per query at natural prevalence. Panel C compares +mean cosine distance for exact and disconnected pairs and annotates exact +retrieval accuracy.} +\label{fig:demo-verification-tree} +\end{figure} + +\subsection{Validity-check demo and deferred items} + +As a limited gallery check, a fixed $T=0$ repeat-1 gallery was compared with +the multi-setting pooled gallery on the same six query settings. Equal-cell +mixed Top-1 with the fixed gallery was 0.403 for cosine, 0.310 for linear SVM, +and 0.341 for RBF SVM over 86 common models. The corresponding pooled-gallery +values were 0.621, 0.722, and 0.688 over 84 common models. Because the cohorts +and number of training examples differ, this is a diagnostic rather than a +paired method comparison; it merely confirms that gallery construction is +consequential. + +The remaining Section 10--11 analyses were deferred. Two temperatures and one +held-out repeat are inadequate for the planned mixed-effects degradation model. +Alternative projection dimensions/seeds, encoder comparisons, reliable +encoder-token truncation counts, $K=4$ and $K=8$, lineage-only verification, +and related-pair tree strata are not supported by the currently complete +artifacts. Leave-one-setting-out and subgroup analyses are feasible later but +were omitted to keep this demonstration deliberately small. + +\subsection{Demo conclusions and primary-study handoff} + +The demonstration supports the following limited conclusions: + +\begin{itemize} + \item The exact-classification and bounded-deployment pipelines operate on + disjoint generation repeats and produce aligned per-query scores for + all three classifiers. + \item Within this demo, the linear SVM had the highest mixed Top-1 at both + temperature bounds, and its paired advantage over cosine was positive. + \item Robustness was setting-dependent: $(0.3,1.0)$ was substantially harder + than the other five cells for every method. + \item Natural-prevalence verification exposed a large operational + false-positive burden that ROC-AUC alone would conceal. + \item The distributional and tree-aware code paths run, but the present data + are respectively too easy and too sparse in related pairs to compare + the intended methods. + \item Gallery construction materially changes the diagnostic accuracies, so + the primary report must keep gallery definitions and training budgets + fixed in paired comparisons. +\end{itemize} + +Before a final primary claim is made, the study still requires recovery to at +least the preregistered 80-model strict common cohort, a duplicate/independence +audit of the two existing rounds, and the frozen paired evaluation on repeat 2. +The current two-round design supports the exact-model cosine/SVM comparison. +It does not yet support a multi-sample advantage claim: a fair $K=2$ test needs +two independent reference responses and two independent query responses per +prompt, while $K=4$ and $K=8$ require additional generation. The small-scale +numbers remain exploratory until rerun on the frozen primary cohort. + +Machine-readable outputs are stored under +\path{out/small_scale_section_6_11}. The exact-classification, verification, +distributional, fixed-gallery, and tree-aware subdirectories contain the +selected cohorts, per-query predictions where applicable, aggregate CSV files, +and JSON summaries. The three report figures are regenerated without plotting +library dependencies using: + +\begin{lstlisting}[language=bash] +python scripts/plot_small_scale_section_6_11_demo.py +\end{lstlisting} + +\clearpage + +\section{Execution and resource plan} + +The completed backfill sequence used +\path{scripts/resume_primary_robustness_grid.sh}, the exact validated-100 +cohort, and GPUs 0, 1, 2, and 4. It preserved successful historical artifacts +and queued only missing or failed model--cell--round keys. The sequence ended +on August 5 with 72 terminal failures. Repeating the same generic retry is not +the preferred next action: the 52 missing-file failures require compatibility +or source repair, while the 20 empty-response failures require targeted +generation diagnostics. + +Twenty-four affected models lack only one slot. Restoring any 21 of these +models across their missing cells is the minimum theoretical route from 59 to +the preregistered 80-model strict common cohort. Full 100-model completion +remains the target, but this prioritized recovery makes the analysis gate +explicit. + +The expected completion schedule below is measured from the August 5 status +snapshot and assumes continued access to the existing GPUs. It separates the +minimum analysis gate from optional multi-sample generation so that feasibility +is visible rather than implied. + +\begin{table}[htbp] +\centering +\small +\caption{Expected completion schedule from the August 5 status snapshot.} +\label{tab:completion-schedule} +\begin{tabular}{p{0.38\linewidth}p{0.20\linewidth}p{0.32\linewidth}} +\toprule +Milestone & Expected time & Exit condition \\ +\midrule +Diagnose 72 terminal failures & 1 working day & Failure-specific repair list \\ +Targeted recovery and audit & 2--3 working days & Strict common $N\geq80$ and independent rounds \\ +Frozen cosine/linear/RBF evaluation & 1 working day & Paired tables and 95\% model-cluster CIs \\ +Report integration and review & 1 working day & Primary report ready for supervisor review \\ +Optional fair $K=2$ RBF-MMD generation & 3--5 additional working days & Two independent samples on both sides \\ +\bottomrule +\end{tabular} +\end{table} + +Therefore, the controlled cosine/SVM primary update is expected within five to +six working days if recovery reaches $N\geq80$. The multi-sample proposed-method +claim has a separate, later completion date and is not placed on the critical +path for the classifier comparison. + +Transient failures are retried and recorded. Progress reports include planned, +successful, failed, and missing models plus failure-status counts. Missing +cells are never represented as zero accuracy and curves are not interpolated +through absent cells. + +\section{Reproducibility and provenance} + +The experiment will preserve: + +\begin{itemize} + \item exact cohort file and SHA-256 hash; + \item prompt dataset and seed; + \item model ID and available model metadata; + \item temperature, top-$p$, sampling flag, generation seed, and token limit; + \item response files and independent repeat suffixes; + \item sentence encoder, DNA dimension, projection method, and projection seed; + \item run manifests, status journals, failure summaries, predictions, and + machine-readable metric tables; and + \item the evaluation code and report version used to produce final figures. +\end{itemize} + +The unsuffixed historical gallery will not be used for the primary causal +interpretation because its temperature and top-$p$ were not recorded. It may be +retained as a clearly labelled legacy reference-sensitivity analysis. + +\section{Planned results} + +\subsection{Cohort and completion table} + +\begin{table}[ht] +\centering +\caption{Current generation completion across both planned rounds.} +\begin{tabular}{lrrr} +\toprule +Condition & Required & Successful & Remaining \\ +\midrule +$T=0$ & 200 & 200 & 0 \\ +$T=0.2$, all $p$ & 600 & 597 & 3 \\ +$T=0.3$, all $p$ & 600 & 573 & 27 \\ +$T=0.5$, all $p$ & 600 & 570 & 30 \\ +$T=0.7$, all $p$ & 600 & 588 & 12 \\ +\midrule +Total & 2,600 & 2,528 & 72 \\ +\bottomrule +\end{tabular} +\end{table} + +The strict common cohort across all 26 runs is 59. Setting-specific paired +cohorts are reported in Table~\ref{tab:paired-cohorts}; therefore 97.2\% slot +completion must not be described as a 97-model complete-case cohort. + +\subsection{Per-setting exact classification} + +\begin{longtable}{ccrrrr} +\caption{Available held-out exact-model Top-1. Starred entries are exploratory +84-model results, not final validated-100 estimates.}\label{tab:per-setting}\\ +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ +\midrule +\endfirsthead +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ +\midrule +\endhead +0.0 & N/A & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.2 & 0.8 & \demovalue{0.643} & \demovalue{0.821} & \demovalue{0.702} & \todoresult \\ +0.2 & 0.9 & \demovalue{0.726} & \demovalue{0.845} & \demovalue{0.762} & \todoresult \\ +0.2 & 1.0 & \demovalue{0.595} & \demovalue{0.643} & \demovalue{0.643} & \todoresult \\ +0.3 & 0.8 & \demovalue{0.774} & \demovalue{0.869} & \demovalue{0.857} & \todoresult \\ +0.3 & 0.9 & \demovalue{0.690} & \demovalue{0.786} & \demovalue{0.762} & \todoresult \\ +0.3 & 1.0 & \demovalue{0.298} & \demovalue{0.369} & \demovalue{0.405} & \todoresult \\ +0.5 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{longtable} + +\noindent\textsuperscript{*}Repeat 1 was used for pooled training/reference +construction and repeat 2 for testing on the same 84 availability-selected +models. The Proposed column remains pending: a same-query $K=1$ RBF-MMD check +is feasible, but a multi-sample claim requires additional matched responses. + +\subsection{Bounded unknown-temperature deployment} + +\begin{table}[ht] +\centering +\caption{Temperature-agnostic deployment results. The classifier is not given +temperature or top-$p$.} +\begin{tabular}{clrrr} +\toprule +$\tau$ & Method & Mixed accuracy & Worst setting & MRR \\ +\midrule +0.2 & Original cosine & \demovalue{0.596} & \demovalue{0.562} & \demovalue{0.672} \\ + & Linear SVM & \demovalue{0.682} & \demovalue{0.652} & \demovalue{0.744} \\ + & RBF SVM & \demovalue{0.629} & \demovalue{0.584} & \demovalue{0.710} \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.3 & Original cosine & \demovalue{0.621} & \demovalue{0.298} & \demovalue{0.698} \\ + & Linear SVM & \demovalue{0.722} & \demovalue{0.369} & \demovalue{0.777} \\ + & RBF SVM & \demovalue{0.688} & \demovalue{0.405} & \demovalue{0.754} \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.5 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.7 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Planned figures} + +The final report will contain: + +\begin{enumerate} + \item exact Top-1 versus temperature, faceted by top-$p$, with one curve per + representation/classifier and 95\% confidence bands; + \item accuracy loss relative to $T=0$ and estimated degradation slopes; + \item mixed and worst-setting accuracy versus temperature bound $\tau$; + \item stacked tree-aware Top-1 error severity versus temperature; and + \item accuracy versus response-sample/token budget for the mean and + distributional representations. +\end{enumerate} + +\section{Planned interpretation} + +The final conclusions will be limited to what the completed protocol supports: + +\begin{itemize} + \item the effect of query-side decoding conditions relative to explicit, + independently generated galleries; + \item classifier mitigation on the same exact-model task; + \item representation gains under matched budgets; and + \item expected and worst observed performance when decoding parameters are + hidden but temperature is bounded. +\end{itemize} + +No lineage-robustness conclusion will be based on aggregate positives dominated +by same-model pairs. No method will be declared superior from a point estimate +alone, and no causal temperature conclusion will use the historical +unknown-setting gallery. + +\appendix + +\section{Execution commands} + +Build or verify the fixed cohort and refresh the live grid: + +\begin{lstlisting}[language=bash] +python3 scripts/build_validated100_config.py \ + --manifest out/top_p_control_t05_validated100_20260719/\ +top_p_control_t05_t05_p06_r2/manifest.json \ + --output configs/rand_chinese_validated100_latest.jsonl +python3 scripts/build_primary_run_grid.py +\end{lstlisting} + +The completed missing/failed backfill sequence was launched with: + +\begin{lstlisting}[language=bash] +env GPUS=0,1,2,4 MAX_CONCURRENT_GPUS=4 \ + bash scripts/resume_primary_robustness_grid.sh +\end{lstlisting} + +After targeted recovery and the independence audit, run the exact-model and +tree-aware analyses: + +\begin{lstlisting}[language=bash] +bash scripts/run_primary_analysis.sh +\end{lstlisting} + +The primary analysis outputs are written beneath +\path{out/primary_robustness_100_analysis}. The exact-model evaluator produces +per-setting metrics, per-query predictions, bounded-deployment tables, cohort +metadata, and JSON summaries. + +\section{Implementation map} + +\begin{longtable}{p{0.34\linewidth}p{0.58\linewidth}} +\toprule +File & Purpose \\ +\midrule +\endfirsthead +\toprule +File & Purpose \\ +\midrule +\endhead +\path{scripts/run_primary_robustness_experiment.sh} + & Launch the 100-model, 13-condition, two-round extraction sweep. \\ +\path{scripts/resume_primary_robustness_grid.sh} + & Resume missing and failed validated-100 grid cells without regenerating + successful artifacts. \\ +\path{scripts/build_primary_run_grid.py} + & Produce slot-completion and repeat-level cohort audits. \\ +\path{scripts/run_primary_analysis.sh} + & Construct the fixed train/test repeat split and run primary analyses. \\ +\path{scripts/evaluate_exact_dna_classification.py} + & Compare cosine centroids and multiclass SVMs on exact identity; produce + bounded unknown-temperature results and confidence intervals. \\ +\path{scripts/analyze_tree_aware_errors.py} + & Produce hierarchical retrieval and tree-distance error summaries. \\ +\path{scripts/evaluate_dna_probe_sweep.py} + & Run legacy and separated binary verification tasks using all test + negatives by default. \\ +\path{scripts/run_rbf_mmd_pilot.py} + & Compare single-sample, matched mean-cosine, and exact RBF-MMD retrieval. \\ +\path{scripts/export_hf_model_tree.py} + & Export direct parent/base edges and connected components. \\ +\path{scripts/report_sweep_progress.py} + & Report completion, missing models, and failure-status counts. \\ +\bottomrule +\end{longtable} + +\end{document} diff --git a/docs/weekly_phd_update_2026-08-05.tex b/docs/weekly_phd_update_2026-08-05.tex new file mode 100644 index 0000000..d0f0fef --- /dev/null +++ b/docs/weekly_phd_update_2026-08-05.tex @@ -0,0 +1,120 @@ +\documentclass[10pt,a4paper]{article} +\usepackage[margin=0.68in]{geometry} +\usepackage{booktabs,tabularx,array,xcolor,enumitem} +\usepackage[hidelinks]{hyperref} +\setlength{\parindent}{0pt} +\setlength{\parskip}{4pt} +\setlist{nosep,leftmargin=*} +\renewcommand{\arraystretch}{1.08} +\newcommand{\good}[1]{\textcolor{green!45!black}{#1}} +\newcommand{\warn}[1]{\textcolor{orange!75!black}{#1}} + +\title{Weekly Update} +\author{} +\date{5 August 2026} + +\begin{document} +\maketitle +\vspace{-2.2em} + +\section*{What changed this week} + +\textbf{Grid generation finished its scheduled pass.} We now have +\good{2,528/2,600 successful artifacts (97.2\%)} across 100 validated models, +13 decoding settings, and two rounds. There are no ordinary pending cells and +no experiment worker is currently active. The 72 terminal failures comprise +52 missing-file failures and 20 empty responses; they require targeted repair, +not another generic restart. + +\begin{table}[h] +\centering +\caption{Successful artifacts in the two-round grid (maximum 200 per cell).} +\begin{tabular}{crrr} +\toprule +$T$ & $p=0.8$ & $p=0.9$ & $p=1.0$ \\ +\midrule +0.0 & -- & -- & 200/200 \\ +0.2 & 199/200 & 200/200 & 198/200 \\ +0.3 & 199/200 & 189/200 & 185/200 \\ +0.5 & 198/200 & 196/200 & 176/200 \\ +0.7 & 196/200 & 197/200 & 195/200 \\ +\bottomrule +\end{tabular} +\end{table} + +\textbf{Why the strict cohort is 59 despite 97.2\% completion.} The 72 failed +slots are spread across 41 different models, so only $100-41=59$ models have +all 26 required artifacts. This does \emph{not} mean that only 59 models ran. +Per-setting paired cohorts are much larger (77--100), and bounded common +cohorts are 97, 86, 63, and 59 for $\tau=0.2,0.3,0.5,0.7$, respectively. + +\textbf{Method comparison is now controlled.} For cosine, linear SVM, and RBF +SVM, we will use the same model IDs, prompts, response files, 128-D DNA, +decoding cells, repeat-1 training/reference split, repeat-2 held-out queries, +candidate set, and metrics. Only the decision rule changes. Hyperparameters are +selected using repeat 1 only; differences are paired on identical queries and +reported with 95\% model-cluster bootstrap intervals. + +\textbf{Preliminary finding (exploratory 84-model cohort).} Linear SVM was best +on average over the available cells, but temperature/top-$p$ effects were not +monotone. At $T=0.3$, for example, all methods fell sharply at $p=1.0$ relative +to $p=0.8$ or $0.9$. This supports a limited interaction finding, not a broad +causal conclusion, because the final cohort and seed audit are unfinished. + +\clearpage +\section*{New method and next steps} + +\textbf{Can the proposed distributional method be compared fairly?} Yes, but +the claim must match the available sampling budget. At $K=1$, RBF-MMD reduces +to a kernel point-distance check and cannot demonstrate a multi-sample +advantage. A fair $K=2$ comparison needs two independent reference responses +and two independent query responses for every prompt, with the same encoder, +prompts, tokens, and samples used by the mean-cosine baseline. The kernel +bandwidth must be calibrated on reference data only and then frozen. The +existing two rounds support the cosine/SVM comparison but not this $K=2$ +design without additional generation. + +\begin{table}[h] +\centering +\small +\caption{Controlled comparison and current readiness.} +\begin{tabularx}{\linewidth}{>{\raggedright\arraybackslash}p{0.27\linewidth} + >{\raggedright\arraybackslash}p{0.25\linewidth}X} +\toprule +Comparison & Variable changed & Status \\ +\midrule +Cosine vs linear/RBF SVM & Decision rule only & Ready after cohort recovery and round audit \\ +Mean cosine vs RBF-MMD, $K=1$ & Distance only & Feasible as a point check; not multi-sample evidence \\ +Mean cosine vs RBF-MMD, $K=2$ & Representation/distance only & Requires additional independent responses \\ +\bottomrule +\end{tabularx} +\end{table} + +\textbf{Recovery priority.} Twenty-four affected models each lack only one +slot. Repairing any 21 of them is the minimum theoretical route from 59 to the +preregistered strict common cohort of at least 80. We will first diagnose the +missing-file compatibility/source issue and the empty-response issue, then run +only the affected model--cell--round keys. + +\textbf{Expected completion from the 5 August snapshot:} +\begin{itemize} + \item \textbf{Failure diagnosis --- 1 working day:} produce a + failure-specific repair list. + \item \textbf{Targeted recovery and audit --- 2--3 working days:} reach a + strict common $N\geq80$ and confirm independent rounds. + \item \textbf{Frozen cosine/SVM evaluation --- 1 working day:} produce paired + results and 95\% confidence intervals. + \item \textbf{Report integration --- 1 working day:} prepare the primary + report for supervisor review. + \item \textbf{Optional fair $K=2$ MMD study --- 3--5 additional working + days:} obtain two independent samples on both sides. +\end{itemize} + +\textbf{This week's deliverables.} The short update now foregrounds the new +progress, controlled comparison, limited findings, and completion dates. The +full robustness report has also been refreshed with the latest grid counts, +paired/common cohort sizes, failure accounting, controlled-comparison matrix, +and revised execution schedule. Exploratory results remain explicitly starred +until rerun on the recovered primary cohort. + +\end{document} diff --git a/reports/primary_robustness_run_grid.csv b/reports/primary_robustness_run_grid.csv new file mode 100644 index 0000000..2a0b27f --- /dev/null +++ b/reports/primary_robustness_run_grid.csv @@ -0,0 +1,27 @@ +temperature,top_p,repeat,proposed_backfill_seed,recorded_seeds,success,failed,skipped,pending_status,no_status,missing_success,started,manifest_models,cohort_overlap,sources +0.0,1.0,r1,42001,not explicitly set or recorded,100,0,0,0,0,0,True,87,87,out/rand_chinese_temp_top_p_sweep_t00_p10_r1 +0.0,1.0,r2,42002,42002,100,0,0,0,0,0,True,100,100,out/primary_robustness_validated100_20260730/primary_grid_backfill_20260730_t00_p10_r2 +0.2,0.8,r1,42001,not explicitly set or recorded,100,0,0,0,0,0,True,87,87,out/rand_chinese_temp_top_p_sweep_t02_p08_r1 +0.2,0.8,r2,42002,not explicitly set or recorded,99,1,0,0,0,1,True,87,87,out/rand_chinese_temp_top_p_sweep_t02_p08_r2 +0.2,0.9,r1,42001,not explicitly set or recorded,100,0,0,0,0,0,True,87,87,out/rand_chinese_temp_top_p_sweep_t02_p09_r1 +0.2,0.9,r2,42002,not explicitly set or recorded,100,0,0,0,0,0,True,87,87,out/rand_chinese_temp_top_p_sweep_t02_p09_r2 +0.2,1.0,r1,42001,not explicitly set or recorded,99,1,0,0,0,1,True,87,87,out/rand_chinese_temp_top_p_sweep_t02_p10_r1 +0.2,1.0,r2,42002,not explicitly set or recorded,99,1,0,0,0,1,True,87,87,out/rand_chinese_temp_top_p_sweep_t02_p10_r2 +0.3,0.8,r1,42001,not explicitly set or recorded,100,0,0,0,0,0,True,87,87,out/rand_chinese_temp_top_p_sweep_t03_p08_r1 +0.3,0.8,r2,42002,not explicitly set or recorded,99,1,0,0,0,1,True,87,87,out/rand_chinese_temp_top_p_sweep_t03_p08_r2 +0.3,0.9,r1,42001,not explicitly set or recorded,96,4,0,0,0,4,True,87,87,out/rand_chinese_temp_top_p_sweep_t03_p09_r1 +0.3,0.9,r2,42002,not explicitly set or recorded,93,7,0,0,0,7,True,87,87,out/rand_chinese_temp_top_p_sweep_t03_p09_r2 +0.3,1.0,r1,42001,not explicitly set or recorded,94,6,0,0,0,6,True,87,87,out/rand_chinese_temp_top_p_sweep_t03_p10_r1 +0.3,1.0,r2,42002,not explicitly set or recorded,91,9,0,0,0,9,True,87,87,out/rand_chinese_temp_top_p_sweep_resume_t03p10r2_t03_p10_r2 +0.5,0.8,r1,42,not explicitly set or recorded,100,0,0,0,0,0,True,87,87,out/rand_chinese_temp_top_p_sweep_resume_t05_t07_t05_p08_r1 +0.5,0.8,r2,42,42,98,2,0,0,0,2,True,100,100,out/primary_robustness_validated100_20260730/primary_grid_backfill_20260730_t05_p08_r2 +0.5,0.9,r1,42,not explicitly set or recorded,98,2,0,0,0,2,True,87,87,out/rand_chinese_temp_top_p_sweep_t05_p09_r1 +0.5,0.9,r2,42,not explicitly set or recorded,98,2,0,0,0,2,True,87,87,out/rand_chinese_temp_top_p_sweep_t05_p09_r2 +0.5,1.0,r1,42,not explicitly set or recorded,98,2,0,0,0,2,True,87,87,out/rand_chinese_temp_top_p_sweep_t05_p10_r1 +0.5,1.0,r2,42,42,78,22,0,0,0,22,True,100,100,out/primary_robustness_validated100_20260730/primary_grid_backfill_20260730_t05_p10_r2 +0.7,0.8,r1,42001,not explicitly set or recorded,97,3,0,0,0,3,True,87,87,out/rand_chinese_temp_top_p_sweep_t07_p08_r1 +0.7,0.8,r2,42002,42002,99,1,0,0,0,1,True,100,100,out/primary_robustness_validated100_20260730/primary_grid_backfill_20260730_t07_p08_r2 +0.7,0.9,r1,42001,not explicitly set or recorded,99,1,0,0,0,1,True,87,87,out/rand_chinese_0p3b_7b_temp07 +0.7,0.9,r2,42002,42002,98,2,0,0,0,2,True,100,100,out/primary_robustness_validated100_20260730/primary_grid_backfill_20260730_t07_p09_r2 +0.7,1.0,r1,42001,not explicitly set or recorded,96,4,0,0,0,4,True,87,87,out/rand_chinese_temp_top_p_sweep_t07_p10_r1 +0.7,1.0,r2,42002,42002,99,1,0,0,0,1,True,100,100,out/primary_robustness_validated100_20260730/primary_grid_backfill_20260730_t07_p10_r2 diff --git a/reports/primary_robustness_run_grid.md b/reports/primary_robustness_run_grid.md new file mode 100644 index 0000000..930f88a --- /dev/null +++ b/reports/primary_robustness_run_grid.md @@ -0,0 +1,65 @@ +# Primary robustness experiment — run grid + +Updated: 2026-08-05T18:55:39+08:00 + +Revised plan: + +- Fixed cohort: **the latest validated100 model list** from `top_p_control_t05_t05_p06_r2/manifest.json`. +- Historical artifacts are reused only when their model ID and decoding cell match this cohort/grid. +- Two repeats per condition. The shared `temperature=0.5` row uses **seed 42 for both rounds** so its `top_p=0.8/0.9/1.0` cells can also serve the validated top-p control. Other proposed grid backfills remain **r1 / 42001** and **r2 / 42002**. +- The historical sweep did not explicitly set or record a response-generation seed. Its recorded `random_seed=42` controlled probe selection and DNA reduction, not the generation sampler. +- Stochastic grid: temperature `{0.2, 0.3, 0.5, 0.7}` × top-p `{0.8, 0.9, 1.0}`. +- Deterministic control: `temperature=0`, `top_p=1.0` (top-p inactive). +- Total: **13 settings × 2 repeats × 100 models = 2,600 successful model artifacts required**. + +Important cohort note: the two validated100 variants overlap by 97 models, but the older temperature/top-p sweep used here overlaps the latest validated100 cohort by **87 models**. Therefore those grid cells require 13 cohort backfills plus retries for any failed/pending overlapping models. + +Legend: `✅` both repeats have 100 successful models; `♻️` historical results are reusable but the cell is incomplete; `⬜` no matching run was found; `—` is not a planned condition. + +| Temperature \\ Top-p | 0.8 | 0.9 | 1.0 | +|---:|---:|---:|---:| +| 0 | — | — | ✅ 200/200 | +| 0.2 | ♻️ 199/200；还需 1 | ✅ 200/200 | ♻️ 198/200；还需 2 | +| 0.3 | ♻️ 199/200；还需 1 | ♻️ 189/200；还需 11 | ♻️ 185/200；还需 15 | +| 0.5 | ♻️ 198/200；还需 2 | ♻️ 196/200；还需 4 | ♻️ 176/200；还需 24 | +| 0.7 | ♻️ 196/200；还需 4 | ♻️ 197/200；还需 3 | ♻️ 195/200；还需 5 | + +## Overall progress + +- Successful artifacts: **2528/2600**. +- Settings started: **13/13**. +- Settings fully complete: **2/13**. +- Remaining successful artifacts required: **72**. + +## Repeat-level detail + +| Temperature | Top-p | Round | Historical generation seed | Proposed backfill seed (not run) | Reusable success | Failed | Pending/no status | Still needed | State | +|---:|---:|---:|---|---:|---:|---:|---:|---:|---| +| 0 | 1 | r1 | not explicitly set or recorded | 42001 | 100 | 0 | 0 | 0 | ✅ complete | +| 0 | 1 | r2 | 42002 | 42002 | 100 | 0 | 0 | 0 | ✅ complete | +| 0.2 | 0.8 | r1 | not explicitly set or recorded | 42001 | 100 | 0 | 0 | 0 | ✅ complete | +| 0.2 | 0.8 | r2 | not explicitly set or recorded | 42002 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | +| 0.2 | 0.9 | r1 | not explicitly set or recorded | 42001 | 100 | 0 | 0 | 0 | ✅ complete | +| 0.2 | 0.9 | r2 | not explicitly set or recorded | 42002 | 100 | 0 | 0 | 0 | ✅ complete | +| 0.2 | 1 | r1 | not explicitly set or recorded | 42001 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | +| 0.2 | 1 | r2 | not explicitly set or recorded | 42002 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | +| 0.3 | 0.8 | r1 | not explicitly set or recorded | 42001 | 100 | 0 | 0 | 0 | ✅ complete | +| 0.3 | 0.8 | r2 | not explicitly set or recorded | 42002 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | +| 0.3 | 0.9 | r1 | not explicitly set or recorded | 42001 | 96 | 4 | 0 | 4 | ♻️ reusable/incomplete | +| 0.3 | 0.9 | r2 | not explicitly set or recorded | 42002 | 93 | 7 | 0 | 7 | ♻️ reusable/incomplete | +| 0.3 | 1 | r1 | not explicitly set or recorded | 42001 | 94 | 6 | 0 | 6 | ♻️ reusable/incomplete | +| 0.3 | 1 | r2 | not explicitly set or recorded | 42002 | 91 | 9 | 0 | 9 | ♻️ reusable/incomplete | +| 0.5 | 0.8 | r1 | not explicitly set or recorded | 42 | 100 | 0 | 0 | 0 | ✅ complete | +| 0.5 | 0.8 | r2 | 42 | 42 | 98 | 2 | 0 | 2 | ♻️ reusable/incomplete | +| 0.5 | 0.9 | r1 | not explicitly set or recorded | 42 | 98 | 2 | 0 | 2 | ♻️ reusable/incomplete | +| 0.5 | 0.9 | r2 | not explicitly set or recorded | 42 | 98 | 2 | 0 | 2 | ♻️ reusable/incomplete | +| 0.5 | 1 | r1 | not explicitly set or recorded | 42 | 98 | 2 | 0 | 2 | ♻️ reusable/incomplete | +| 0.5 | 1 | r2 | 42 | 42 | 78 | 22 | 0 | 22 | ♻️ reusable/incomplete | +| 0.7 | 0.8 | r1 | not explicitly set or recorded | 42001 | 97 | 3 | 0 | 3 | ♻️ reusable/incomplete | +| 0.7 | 0.8 | r2 | 42002 | 42002 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | +| 0.7 | 0.9 | r1 | not explicitly set or recorded | 42001 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | +| 0.7 | 0.9 | r2 | 42002 | 42002 | 98 | 2 | 0 | 2 | ♻️ reusable/incomplete | +| 0.7 | 1 | r1 | not explicitly set or recorded | 42001 | 96 | 4 | 0 | 4 | ♻️ reusable/incomplete | +| 0.7 | 1 | r2 | 42002 | 42002 | 99 | 1 | 0 | 1 | ♻️ reusable/incomplete | + +Ignored 43 manifest(s) outside the revised primary grid; see the CSV/source tree for audit. diff --git a/scripts/build_primary_run_grid.py b/scripts/build_primary_run_grid.py new file mode 100644 index 0000000..903a658 --- /dev/null +++ b/scripts/build_primary_run_grid.py @@ -0,0 +1,338 @@ +#!/usr/bin/env python3 +"""Build the live completion grid using the latest validated-100 cohort.""" + +from __future__ import annotations + +import csv +import json +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] +OUTPUT_ROOT = ROOT / "out" +COHORT_MANIFEST = ( + OUTPUT_ROOT + / "top_p_control_t05_validated100_20260719" + / "top_p_control_t05_t05_p06_r2" + / "manifest.json" +) +REPORT_DIR = ROOT / "reports" + +TEMPERATURES = (0.0, 0.2, 0.3, 0.5, 0.7) +TOP_PS = (0.8, 0.9, 1.0) +REPEATS = (1, 2) +GENERATION_SEEDS = {1: 42001, 2: 42002} + + +def load_cohort() -> set[str]: + manifest = json.loads(COHORT_MANIFEST.read_text(encoding="utf-8")) + models = { + str(task["model_id"]) + for task in manifest.get("tasks", []) + if task.get("model_id") + } + if len(models) != 100: + raise ValueError(f"Expected 100 fixed models, found {len(models)}") + return models + + +def planned_condition(temperature: float, top_p: float) -> bool: + # At T=0 sampling is disabled, so top-p is inactive and recorded once. + return temperature > 0 or top_p == 1.0 + + +def load_statuses(path: Path) -> dict[str, str]: + final: dict[str, str] = {} + if not path.exists(): + return final + with path.open(encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + model_id = record.get("model_id") + if model_id: + final[str(model_id)] = str(record.get("status", "")) + return final + + +def classify(status: str) -> str: + if status == "success": + return "success" + if status.startswith("failed") or status == "incompatible": + return "failed" + if status.startswith("skipped"): + return "skipped" + return "pending" + + +def collect_runs( + cohort: set[str], +) -> tuple[dict[tuple[float, float, int], dict[str, Any]], list[str]]: + runs: dict[tuple[float, float, int], dict[str, Any]] = defaultdict( + lambda: { + "manifest_models": set(), + "final": {}, + "sources": [], + "manifest_count": 0, + "generation_seeds": set(), + "cohort_overlaps": [], + } + ) + ignored: list[str] = [] + if not OUTPUT_ROOT.exists(): + return runs, ignored + + for manifest_path in sorted(OUTPUT_ROOT.rglob("manifest.json")): + try: + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + temperature = float(manifest["temperature"]) + top_p = float(manifest["top_p"]) + except (OSError, ValueError, KeyError, TypeError, json.JSONDecodeError): + ignored.append(manifest_path.relative_to(ROOT).as_posix()) + continue + + suffix = str(manifest.get("output_suffix", "")) + repeat = next( + (repeat for repeat in REPEATS if suffix.endswith(f"_r{repeat}")), None + ) + # The original T=0.7, top-p=0.9 execution was labelled "prelim" + # rather than r1. It is still a reusable first execution of this cell. + if ( + repeat is None + and manifest_path.parent.name == "rand_chinese_0p3b_7b_temp07" + and temperature == 0.7 + and top_p == 0.9 + ): + repeat = 1 + if ( + repeat is None + or temperature not in TEMPERATURES + or top_p not in TOP_PS + or not planned_condition(temperature, top_p) + ): + ignored.append(manifest_path.relative_to(ROOT).as_posix()) + continue + + key = (temperature, top_p, repeat) + run = runs[key] + run["manifest_count"] += 1 + run["sources"].append(manifest_path.parent.relative_to(ROOT).as_posix()) + manifest_models = { + str(task["model_id"]) + for task in manifest.get("tasks", []) + if task.get("model_id") + } + overlap = manifest_models & cohort + run["manifest_models"].update(overlap) + run["cohort_overlaps"].append(len(overlap)) + seed = manifest.get("generation_seed") + run["generation_seeds"].add( + str(seed) + if seed is not None + else "not explicitly set or recorded" + ) + for model_id, status in load_statuses( + manifest_path.parent / "status.jsonl" + ).items(): + if model_id in cohort: + run["final"][model_id] = status + return runs, ignored + + +def summarize_run(run: dict[str, Any], cohort: set[str]) -> dict[str, Any]: + counts = defaultdict(int) + for model_id, status in run["final"].items(): + if model_id in cohort: + counts[classify(status)] += 1 + observed = sum(counts.values()) + no_status = len(cohort) - observed + missing_success = len(cohort) - counts["success"] + return { + "success": counts["success"], + "failed": counts["failed"], + "skipped": counts["skipped"], + "pending_status": counts["pending"], + "no_status": no_status, + "missing_success": missing_success, + "started": bool(run["manifest_count"] or observed), + "manifest_models": len(run["manifest_models"]), + "cohort_overlap": max(run["cohort_overlaps"], default=0), + "recorded_seeds": ",".join(sorted(run["generation_seeds"])), + "sources": ";".join(run["sources"]), + } + + +def cell_text(repeat_rows: list[dict[str, Any]]) -> str: + success = sum(int(row["success"]) for row in repeat_rows) + target = 100 * len(REPEATS) + if success == target: + return f"✅ {success}/{target}" + if any(bool(row["started"]) for row in repeat_rows): + return f"♻️ {success}/{target}；还需 {target - success}" + return f"⬜ 0/{target}；待跑 r1/r2" + + +def main() -> None: + cohort = load_cohort() + runs, ignored = collect_runs(cohort) + detail_rows: list[dict[str, Any]] = [] + setting_rows: dict[tuple[float, float], list[dict[str, Any]]] = defaultdict(list) + + for temperature in TEMPERATURES: + for top_p in TOP_PS: + if not planned_condition(temperature, top_p): + continue + for repeat in REPEATS: + summary = summarize_run( + runs[(temperature, top_p, repeat)], cohort + ) + row = { + "temperature": temperature, + "top_p": top_p, + "repeat": f"r{repeat}", + # T=0.5 is shared with the validated top-p control, whose + # explicit-seed runs use 42 in both rounds. + "proposed_backfill_seed": ( + 42 + if temperature == 0.5 + else GENERATION_SEEDS[repeat] + ), + **summary, + } + detail_rows.append(row) + setting_rows[(temperature, top_p)].append(row) + + REPORT_DIR.mkdir(exist_ok=True) + with (REPORT_DIR / "primary_robustness_run_grid.csv").open( + "w", newline="", encoding="utf-8" + ) as handle: + fields = [ + "temperature", + "top_p", + "repeat", + "proposed_backfill_seed", + "recorded_seeds", + "success", + "failed", + "skipped", + "pending_status", + "no_status", + "missing_success", + "started", + "manifest_models", + "cohort_overlap", + "sources", + ] + writer = csv.DictWriter(handle, fieldnames=fields, lineterminator="\n") + writer.writeheader() + writer.writerows(detail_rows) + + total_success = sum(int(row["success"]) for row in detail_rows) + target_tasks = len(detail_rows) * 100 + completed_settings = sum( + sum(int(row["success"]) for row in rows) == 200 + for rows in setting_rows.values() + ) + started_settings = sum( + any(bool(row["started"]) for row in rows) + for rows in setting_rows.values() + ) + + markdown = [ + "# Primary robustness experiment — run grid", + "", + f"Updated: {datetime.now().astimezone().isoformat(timespec='seconds')}", + "", + "Revised plan:", + "", + "- Fixed cohort: **the latest validated100 model list** from " + "`top_p_control_t05_t05_p06_r2/manifest.json`.", + "- Historical artifacts are reused only when their model ID and decoding " + "cell match this cohort/grid.", + "- Two repeats per condition. The shared `temperature=0.5` row uses " + "**seed 42 for both rounds** so its `top_p=0.8/0.9/1.0` cells can also " + "serve the validated top-p control. Other proposed grid backfills remain " + "**r1 / 42001** and **r2 / 42002**.", + "- The historical sweep did not explicitly set or record a response-generation " + "seed. Its recorded `random_seed=42` controlled probe selection and DNA " + "reduction, not the generation sampler.", + "- Stochastic grid: temperature `{0.2, 0.3, 0.5, 0.7}` × top-p `{0.8, 0.9, 1.0}`.", + "- Deterministic control: `temperature=0`, `top_p=1.0` (top-p inactive).", + f"- Total: **13 settings × 2 repeats × 100 models = {target_tasks:,} successful model artifacts required**.", + "", + "Important cohort note: the two validated100 variants overlap by 97 models, " + "but the older temperature/top-p sweep used here overlaps the latest " + "validated100 cohort by **87 models**. Therefore those grid cells require " + "13 cohort backfills plus retries for any failed/pending overlapping models.", + "", + "Legend: `✅` both repeats have 100 successful models; `♻️` historical " + "results are reusable but the cell is incomplete; `⬜` no matching run was " + "found; `—` is not a planned condition.", + "", + "| Temperature \\\\ Top-p | 0.8 | 0.9 | 1.0 |", + "|---:|---:|---:|---:|", + ] + for temperature in TEMPERATURES: + cells = [] + for top_p in TOP_PS: + if not planned_condition(temperature, top_p): + cells.append("—") + else: + cells.append(cell_text(setting_rows[(temperature, top_p)])) + markdown.append( + f"| {temperature:g} | " + " | ".join(cells) + " |" + ) + + markdown += [ + "", + "## Overall progress", + "", + f"- Successful artifacts: **{total_success}/{target_tasks}**.", + f"- Settings started: **{started_settings}/13**.", + f"- Settings fully complete: **{completed_settings}/13**.", + f"- Remaining successful artifacts required: **{target_tasks - total_success}**.", + "", + "## Repeat-level detail", + "", + "| Temperature | Top-p | Round | Historical generation seed | Proposed backfill seed (not run) | Reusable success | Failed | Pending/no status | Still needed | State |", + "|---:|---:|---:|---|---:|---:|---:|---:|---:|---|", + ] + for row in detail_rows: + state = ( + "✅ complete" + if row["success"] == 100 + else ("♻️ reusable/incomplete" if row["started"] else "⬜ not run") + ) + historical_seed = row["recorded_seeds"] or "—" + markdown.append( + f"| {row['temperature']:g} | {row['top_p']:g} | {row['repeat']} | " + f"{historical_seed} | {row['proposed_backfill_seed']} | {row['success']} | " + f"{row['failed']} | " + f"{row['pending_status'] + row['skipped'] + row['no_status']} | " + f"{row['missing_success']} | {state} |" + ) + if ignored: + markdown += [ + "", + f"Ignored {len(ignored)} manifest(s) outside the revised primary grid; see the CSV/source tree for audit.", + ] + markdown.append("") + (REPORT_DIR / "primary_robustness_run_grid.md").write_text( + "\n".join(markdown), encoding="utf-8" + ) + print( + f"Wrote primary grid: {total_success}/{target_tasks} successful artifacts, " + f"{started_settings}/13 settings started" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/build_validated100_config.py b/scripts/build_validated100_config.py new file mode 100755 index 0000000..07149ac --- /dev/null +++ b/scripts/build_validated100_config.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +"""Reconstruct the latest validated-100 JSONL from its canonical manifest.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + manifest = json.loads(args.manifest.read_text(encoding="utf-8")) + rows = [ + { + "model_id": str(task["model_id"]), + "provider": str(task.get("provider", "huggingface")), + } + for task in manifest.get("tasks", []) + if task.get("model_id") + ] + model_ids = [row["model_id"] for row in rows] + if len(rows) != 100 or len(set(model_ids)) != 100: + raise ValueError( + f"Expected 100 unique manifest models, found " + f"{len(rows)} rows / {len(set(model_ids))} unique IDs" + ) + + args.output.parent.mkdir(parents=True, exist_ok=True) + temporary = args.output.with_suffix(args.output.suffix + ".tmp") + temporary.write_text( + "".join( + json.dumps(row, ensure_ascii=False) + "\n" + for row in rows + ), + encoding="utf-8", + ) + temporary.replace(args.output) + print(f"Wrote {len(rows)} models to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/resume_primary_robustness_grid.sh b/scripts/resume_primary_robustness_grid.sh new file mode 100644 index 0000000..a0160de --- /dev/null +++ b/scripts/resume_primary_robustness_grid.sh @@ -0,0 +1,182 @@ +#!/usr/bin/env bash +# Resume the revised validated-100 primary grid without regenerating models +# already marked successful in the canonical per-cell status journals. + +set -u + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" +HF_JSONL="${HF_JSONL:-configs/rand_chinese_validated100_latest.jsonl}" +OUTPUT_DIR="${OUTPUT_DIR:-out/primary_robustness_validated100_20260730}" +LOG_DIR="${LOG_DIR:-logs/primary_robustness_validated100_20260730}" +GPUS="${GPUS:-0,1,2,4}" +MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-4}" +DRY_RUN="${DRY_RUN:-0}" + +mkdir -p "$OUTPUT_DIR" "$LOG_DIR" + +LOCK_PATH="$LOG_DIR/resume_grid.lock" +exec 9>"$LOCK_PATH" +if ! flock -n 9; then + echo "Another primary-grid resume process already holds $LOCK_PATH" >&2 + exit 1 +fi + +if [[ ! -x "$PYTHON_BIN" ]]; then + echo "Missing Python environment: $PYTHON_BIN" >&2 + exit 1 +fi +if [[ ! -f "$HF_JSONL" ]]; then + echo "Missing validated cohort: $HF_JSONL" >&2 + exit 1 +fi +if [[ "$(wc -l < "$HF_JSONL")" -ne 100 ]]; then + echo "Validated cohort must contain exactly 100 rows: $HF_JSONL" >&2 + exit 1 +fi + +cell_code() { + "$PYTHON_BIN" - "$1" <<'PY' +import sys +print(f"{int(round(float(sys.argv[1]) * 10)):02d}") +PY +} + +run_phase() { + local temperature="$1" + local top_p="$2" + local repeat="$3" + local generation_seed="$4" + local run_name="$5" + local resume_status="$6" + local resume_mode="$7" + local t_code + local p_code + t_code="$(cell_code "$temperature")" + p_code="$(cell_code "$top_p")" + local suffix="_t${t_code}_p${p_code}_r${repeat}" + + local command=( + "$PYTHON_BIN" scripts/run_hf_dna_pipeline.py + --providers huggingface + --hf-jsonl "$HF_JSONL" + --dataset rand_chinese + --max-samples 100 + --output-dir "$OUTPUT_DIR" + --output-suffix "$suffix" + --run-name "$run_name" + --global-status-journal "$OUTPUT_DIR/dna_global_status.jsonl" + --global-state "$OUTPUT_DIR/dna_global_state.json" + --temperature "$temperature" + --top-p "$top_p" + --generation-seed "$generation_seed" + --ignore-response-cache + --try-vllm + --gpus "$GPUS" + --max-concurrent-gpus "$MAX_CONCURRENT_GPUS" + --min-gpu-free-gb 4 + --gpu-poll-seconds 15 + --model-timeout-seconds 43200 + --max-retries 3 + --oom-retries 3 + --retry-delay-seconds 300 + --gpu-memory-per-billion-gb 2.2 + --gpu-memory-headroom-gb 2 + --cache-evict-finished success + --resume-mode "$resume_mode" + ) + + if [[ "$temperature" == "0.0" ]]; then + command+=(--no-do-sample) + else + command+=(--do-sample) + fi + if [[ -n "$resume_status" ]]; then + command+=(--resume-status "$resume_status") + fi + + echo "[$(date '+%Y-%m-%d %H:%M:%S')] phase=$resume_mode cell=($temperature,$top_p,r$repeat) seed=$generation_seed" + if [[ "$DRY_RUN" == "1" ]]; then + printf "command:" + printf " %q" "${command[@]}" + printf "\n" + return 0 + fi + if "${command[@]}"; then + echo "[$(date '+%Y-%m-%d %H:%M:%S')] phase complete: $run_name $resume_mode" + else + echo "[$(date '+%Y-%m-%d %H:%M:%S')] phase retained failures: $run_name $resume_mode" >&2 + fi +} + +run_cell() { + local temperature="$1" + local top_p="$2" + local repeat="$3" + local generation_seed="$4" + local existing_run_dir="${5:-}" + local t_code + local p_code + t_code="$(cell_code "$temperature")" + p_code="$(cell_code "$top_p")" + local run_name="primary_grid_backfill_20260730_t${t_code}_p${p_code}_r${repeat}" + local resume_status="" + + if [[ -n "$existing_run_dir" && -f "$existing_run_dir/status.jsonl" ]]; then + resume_status="$existing_run_dir/status.jsonl" + # First add cohort models that have no terminal status, then revisit all + # failed records. Successful models are never enqueued by either phase. + run_phase "$temperature" "$top_p" "$repeat" "$generation_seed" \ + "$run_name" "$resume_status" "continue" + run_phase "$temperature" "$top_p" "$repeat" "$generation_seed" \ + "$run_name" "$resume_status" "retry-failed" + else + # A genuinely absent repeat has no successes to preserve. + run_phase "$temperature" "$top_p" "$repeat" "$generation_seed" \ + "$run_name" "" "all" + fi +} + +echo "Validated cohort: $HF_JSONL" +echo "Pinned GPUs: $GPUS" +echo "Output root for new artifacts: $OUTPUT_DIR" +echo "Priority: deterministic control, T=0.7, T=0.5, then T=0.2/0.3 backfills" + +# Deterministic control. +run_cell 0.0 1.0 1 42001 out/rand_chinese_temp_top_p_sweep_t00_p10_r1 +run_cell 0.0 1.0 2 42002 + +# Largest stochastic gaps. +run_cell 0.7 0.8 1 42001 out/rand_chinese_temp_top_p_sweep_t07_p08_r1 +run_cell 0.7 0.8 2 42002 +run_cell 0.7 0.9 1 42001 out/rand_chinese_0p3b_7b_temp07 +run_cell 0.7 0.9 2 42002 +run_cell 0.7 1.0 1 42001 out/rand_chinese_temp_top_p_sweep_t07_p10_r1 +run_cell 0.7 1.0 2 42002 + +# T=0.5 remains compatible with the validated top-p control (seed 42). The +# separate independence audit decides whether a distinct-seed repeat is needed. +run_cell 0.5 0.8 1 42 out/rand_chinese_temp_top_p_sweep_resume_t05_t07_t05_p08_r1 +run_cell 0.5 0.8 2 42 +run_cell 0.5 0.9 1 42 out/rand_chinese_temp_top_p_sweep_t05_p09_r1 +run_cell 0.5 0.9 2 42 out/rand_chinese_temp_top_p_sweep_t05_p09_r2 +run_cell 0.5 1.0 1 42 out/rand_chinese_temp_top_p_sweep_t05_p10_r1 +run_cell 0.5 1.0 2 42 + +# Smaller historical gaps. +run_cell 0.2 0.8 1 42001 out/rand_chinese_temp_top_p_sweep_t02_p08_r1 +run_cell 0.2 0.8 2 42002 out/rand_chinese_temp_top_p_sweep_t02_p08_r2 +run_cell 0.2 0.9 1 42001 out/rand_chinese_temp_top_p_sweep_t02_p09_r1 +run_cell 0.2 0.9 2 42002 out/rand_chinese_temp_top_p_sweep_t02_p09_r2 +run_cell 0.2 1.0 1 42001 out/rand_chinese_temp_top_p_sweep_t02_p10_r1 +run_cell 0.2 1.0 2 42002 out/rand_chinese_temp_top_p_sweep_t02_p10_r2 +run_cell 0.3 0.8 1 42001 out/rand_chinese_temp_top_p_sweep_t03_p08_r1 +run_cell 0.3 0.8 2 42002 out/rand_chinese_temp_top_p_sweep_t03_p08_r2 +run_cell 0.3 0.9 1 42001 out/rand_chinese_temp_top_p_sweep_t03_p09_r1 +run_cell 0.3 0.9 2 42002 out/rand_chinese_temp_top_p_sweep_t03_p09_r2 +run_cell 0.3 1.0 1 42001 out/rand_chinese_temp_top_p_sweep_t03_p10_r1 +run_cell 0.3 1.0 2 42002 out/rand_chinese_temp_top_p_sweep_resume_t03p10r2_t03_p10_r2 + +echo "[$(date '+%Y-%m-%d %H:%M:%S')] primary grid resume sequence complete" diff --git a/scripts/resume_validated_top_p_control.sh b/scripts/resume_validated_top_p_control.sh new file mode 100755 index 0000000..6f596c8 --- /dev/null +++ b/scripts/resume_validated_top_p_control.sh @@ -0,0 +1,90 @@ +#!/usr/bin/env bash +# Resume p=0.6/r2, then continue the validated-100 top-p control through 1.0. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" +EXPERIMENT_ROOT="${EXPERIMENT_ROOT:-out/top_p_control_t05_validated100_20260719}" +CANONICAL_MANIFEST="$EXPERIMENT_ROOT/top_p_control_t05_t05_p06_r2/manifest.json" +HF_JSONL="${HF_JSONL:-configs/rand_chinese_validated100_latest.jsonl}" +GENERATION_SEED="${GENERATION_SEED:-42}" +GPUS="${GPUS:-0,1,2,4}" +MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-4}" +DRY_RUN="${DRY_RUN:-0}" + +"$PYTHON_BIN" scripts/build_validated100_config.py \ + --manifest "$CANONICAL_MANIFEST" \ + --output "$HF_JSONL" + +run_cell() { + local top_p="$1" + local p_code="$2" + local repeat="$3" + local suffix="_t05_p${p_code}_r${repeat}" + local run_name="top_p_control_t05${suffix}" + local run_dir="$EXPERIMENT_ROOT/$run_name" + local status_path="$run_dir/status.jsonl" + local resume_mode="all" + + local command=( + "$PYTHON_BIN" scripts/run_hf_dna_pipeline.py + --providers huggingface + --hf-jsonl "$HF_JSONL" + --dataset rand_chinese + --max-samples 100 + --output-dir "$EXPERIMENT_ROOT" + --output-suffix "$suffix" + --run-name "$run_name" + --global-status-journal "$EXPERIMENT_ROOT/dna_global_status.jsonl" + --global-state "$EXPERIMENT_ROOT/dna_global_state.json" + --temperature 0.5 + --top-p "$top_p" + --generation-seed "$GENERATION_SEED" + --do-sample + --ignore-response-cache + --try-vllm + --gpus "$GPUS" + --max-concurrent-gpus "$MAX_CONCURRENT_GPUS" + --min-gpu-free-gb 4 + --gpu-poll-seconds 15 + --model-timeout-seconds 43200 + --max-retries 3 + --oom-retries 3 + --retry-delay-seconds 300 + --gpu-memory-per-billion-gb 2.2 + --gpu-memory-headroom-gb 2 + --cache-evict-finished success + ) + + if [[ -f "$status_path" ]]; then + resume_mode="continue" + command+=(--resume-status "$status_path") + fi + command+=(--resume-mode "$resume_mode") + + echo "[$(date '+%Y-%m-%d %H:%M:%S')] START $run_name mode=$resume_mode seed=$GENERATION_SEED" + if [[ "$DRY_RUN" == "1" ]]; then + printf "command:" + printf " %q" "${command[@]}" + printf "\n" + return + fi + "${command[@]}" + echo "[$(date '+%Y-%m-%d %H:%M:%S')] DONE $run_name" +} + +# Preserve the 24 completed p=0.6/r2 models and enqueue its remaining 76. +run_cell "0.6" "06" "2" + +# Continue from low to high. Both rounds intentionally use seed 42 so these +# cells remain compatible with the explicit-seed portion of the control sweep. +for cell in "0.7 07" "0.8 08" "0.9 09" "1.0 10"; do + read -r top_p p_code <<< "$cell" + run_cell "$top_p" "$p_code" "1" + run_cell "$top_p" "$p_code" "2" +done + +echo "[$(date '+%Y-%m-%d %H:%M:%S')] validated top-p control complete" diff --git a/scripts/run_primary_robustness_experiment.sh b/scripts/run_primary_robustness_experiment.sh index a188c23..378304e 100644 --- a/scripts/run_primary_robustness_experiment.sh +++ b/scripts/run_primary_robustness_experiment.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash # Primary exact-model robustness dataset: 100 models, a complete factorial -# decoding grid, and four independent generation seeds. T=0 is emitted once. +# decoding grid, and two independent generation seeds. T=0 is emitted once. set -euo pipefail @@ -15,7 +15,7 @@ export LOG_DIR="${LOG_DIR:-logs/primary_robustness_100_$(date +%Y%m%d-%H%M%S)}" export TEMPERATURES="0.2 0.3 0.5 0.7" export TOP_PS="0.8 0.9 1.0" -export REPEATS="${REPEATS:-1 2 3 4}" +export REPEATS="${REPEATS:-1 2}" export INCLUDE_T00="1" export GENERATION_SEED_BASE="${GENERATION_SEED_BASE:-42000}" diff --git a/scripts/run_temperature_control_p09_100_shard.sh b/scripts/run_temperature_control_p09_100_shard.sh index d454a2b..c28ce20 100755 --- a/scripts/run_temperature_control_p09_100_shard.sh +++ b/scripts/run_temperature_control_p09_100_shard.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash # Fixed-top-p temperature experiment over the exact primary 100-model cohort. -# Temperatures run sequentially from low to high; each has four generation seeds. +# Temperatures run sequentially from low to high; each has two generation seeds. set -euo pipefail @@ -52,7 +52,7 @@ export GLOBAL_STATE="$OUTPUT_DIR/dna_global_state_${SHARD}.json" # Experimental variables and controls. The sweep runner preserves this order. export TEMPERATURES="0.2 0.3 0.5 0.7" export TOP_PS="0.9" -export REPEATS="${REPEATS:-1 2 3 4}" +export REPEATS="${REPEATS:-1 2}" export GENERATION_SEED_BASE="${GENERATION_SEED_BASE:-42000}" export INCLUDE_T00="0" @@ -67,7 +67,7 @@ echo "Shard: $SHARD ($ACTUAL_MODELS models)" echo "Fixed top_p: $TOP_PS" echo "Temperatures (execution order): $TEMPERATURES" echo "Repeats: $REPEATS" -echo "Generation seeds: 42001 42002 42003 42004" +echo "Generation seeds: 42001 42002" echo "Output directory: $OUTPUT_DIR" echo "Log directory: $LOG_DIR" diff --git a/src/llm_dna/api.py b/src/llm_dna/api.py index ff6cac1..e6fd978 100644 --- a/src/llm_dna/api.py +++ b/src/llm_dna/api.py @@ -6,6 +6,7 @@ import logging import os import queue +import random import threading import time from datetime import datetime @@ -37,6 +38,7 @@ class DNAExtractionConfig: max_length: int = 1024 output_dir: Path = Path("./out") output_path: Optional[Path] = None + output_suffix: str = "" save: bool = True load_in_8bit: bool = False load_in_4bit: bool = False @@ -48,7 +50,13 @@ class DNAExtractionConfig: gpu_id: Optional[int] = None log_level: str = "INFO" random_seed: int = 42 + generation_seed: Optional[int] = None + temperature: float = 0.0 + do_sample: bool = False + top_p: float = 1.0 use_chat_template: bool = False + try_vllm: bool = False + use_response_cache: bool = True @dataclass(slots=True) @@ -155,13 +163,19 @@ def _validate_quantization(config: DNAExtractionConfig) -> None: ) from exc +def _safe_model_output_name(model_name: str, output_suffix: str = "") -> str: + return f"{model_name.replace('/', '_').replace(':', '_')}{output_suffix}" + + def _signature_output_paths(config: DNAExtractionConfig) -> tuple[Path, Path]: if config.output_path is not None: output_path = Path(config.output_path) summary_path = output_path.with_name(f"{output_path.stem}_summary.json") return output_path, summary_path - safe_model_name = config.model_name.replace("/", "_").replace(":", "_") + safe_model_name = _safe_model_output_name( + config.model_name, config.output_suffix + ) dataset_identifier = config.dataset.replace(",", "_") structured_dir = Path(config.output_dir) / dataset_identifier / safe_model_name output_path = structured_dir / f"{safe_model_name}_dna.json" @@ -284,7 +298,7 @@ def _is_api_parallel_mode(config: DNAExtractionConfig, model_names: list[str]) - def _response_cache_path(config: DNAExtractionConfig, model_name: str) -> Path: - safe_model_name = model_name.replace("/", "_").replace(":", "_") + safe_model_name = _safe_model_output_name(model_name, config.output_suffix) dataset_identifier = config.dataset.replace(",", "_") return Path(config.output_dir) / dataset_identifier / safe_model_name / "responses.json" @@ -395,8 +409,27 @@ def _generate_responses_for_model( load_in_4bit=load_in_4bit, token=resolved_token, is_chat_model=model_meta.get("chat_model", {}).get("is_chat_model", False), + try_vllm=config.try_vllm, ) + generation_seed = ( + config.random_seed + if config.generation_seed is None + else config.generation_seed + ) + # Reset immediately before response generation so model loading cannot + # consume part of the sampling stream. + random.seed(generation_seed) + np.random.seed(generation_seed) + try: + import torch + + torch.manual_seed(generation_seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(generation_seed) + except ImportError: + pass + # Set up incremental saving callback incremental_items: list[dict] = [] def _save_response_incrementally(idx: int, prompt: str, response: str) -> None: @@ -411,20 +444,31 @@ def _save_response_incrementally(idx: int, prompt: str, response: str) -> None: "complete": len(incremental_items) >= len(probe_texts), "items": incremental_items, "generated_at": datetime.now().isoformat(), + "generation_config": { + "temperature": config.temperature, + "top_p": config.top_p, + "do_sample": config.do_sample, + "generation_seed": generation_seed, + "max_length": config.max_length, + }, } with open(incremental_save_path, "w", encoding="utf-8") as handle: json.dump(payload, handle, indent=2, ensure_ascii=False) try: if hasattr(model, "generate_batch") and callable(getattr(model, "generate_batch")): + generation_kwargs = {} + if model.__class__.__name__ == "VLLMWrapper": + generation_kwargs["seed"] = generation_seed responses = model.generate_batch( probe_texts, max_length=config.max_length, - temperature=0.0, - do_sample=False, - top_p=1.0, + temperature=config.temperature, + do_sample=config.do_sample, + top_p=config.top_p, use_chat_template=config.use_chat_template, on_response_callback=_save_response_incrementally if incremental_save_path else None, + **generation_kwargs, ) else: responses = [] @@ -432,9 +476,9 @@ def _save_response_incrementally(idx: int, prompt: str, response: str) -> None: response = model.generate( prompt, max_length=config.max_length, - temperature=0.0, - do_sample=False, - top_p=1.0, + temperature=config.temperature, + do_sample=config.do_sample, + top_p=config.top_p, use_chat_template=config.use_chat_template, ) responses.append(response) @@ -515,6 +559,14 @@ def _extract_signature_from_text_responses( "encoder_device": resolved_encoder_device, "max_length": config.max_length, "dataset": config.dataset, + "temperature": config.temperature, + "do_sample": config.do_sample, + "top_p": config.top_p, + "generation_seed": ( + config.random_seed + if config.generation_seed is None + else config.generation_seed + ), }, aggregation_method=config.embedding_merge, ) @@ -551,7 +603,11 @@ def calc_dna(config: DNAExtractionConfig) -> DNAExtractionResult: raise ValueError(f"Unsupported extractor_type for calc_dna: {config.extractor_type}") response_path = _response_cache_path(config, config.model_name) - cached_responses = _load_cached_responses(response_path, expected_count=len(probe_texts)) + cached_responses = ( + _load_cached_responses(response_path, expected_count=len(probe_texts)) + if config.use_response_cache + else None + ) if cached_responses is not None: logging.info( diff --git a/src/llm_dna/cli.py b/src/llm_dna/cli.py index 56a4d64..269cf9c 100644 --- a/src/llm_dna/cli.py +++ b/src/llm_dna/cli.py @@ -124,9 +124,14 @@ def parse_arguments(argv: Optional[Iterable[str]] = None) -> argparse.Namespace: choices=["sum", "max", "mean", "concat"], ) parser.add_argument("--max-length", type=int, default=1024) + parser.add_argument("--temperature", type=float, default=0.0) + parser.add_argument("--top-p", type=float, default=1.0) + parser.add_argument("--do-sample", action="store_true", default=False) + parser.add_argument("--no-do-sample", dest="do_sample", action="store_false") # Output parser.add_argument("--output-dir", type=Path, default=Path("./out")) + parser.add_argument("--output-suffix", type=str, default="") parser.add_argument( "--output-path", type=Path, @@ -171,12 +176,29 @@ def parse_arguments(argv: Optional[Iterable[str]] = None) -> argparse.Namespace: default="INFO", ) parser.add_argument("--random-seed", type=int, default=42) + parser.add_argument( + "--generation-seed", + type=int, + default=None, + help="Response-sampling seed, independent of probe/reduction random seed.", + ) parser.add_argument( "--use-chat-template", action="store_true", default=False, help="Apply chat template for HuggingFace models (default: disabled).", ) + parser.add_argument( + "--try-vllm", + action="store_true", + default=False, + help="Try vLLM first for decoder-only HuggingFace models.", + ) + parser.add_argument( + "--ignore-response-cache", + action="store_true", + help="Regenerate responses instead of reusing responses.json.", + ) return parser.parse_args(list(argv) if argv is not None else None) @@ -219,6 +241,7 @@ def main(argv: Optional[Iterable[str]] = None) -> int: max_length=args.max_length, output_dir=args.output_dir, output_path=None, + output_suffix=args.output_suffix, save=not args.no_save, load_in_8bit=args.load_in_8bit, load_in_4bit=args.load_in_4bit, @@ -230,7 +253,13 @@ def main(argv: Optional[Iterable[str]] = None) -> int: gpu_id=None, log_level=args.log_level, random_seed=args.random_seed, + generation_seed=args.generation_seed, + temperature=args.temperature, + do_sample=args.do_sample, + top_p=args.top_p, use_chat_template=args.use_chat_template, + try_vllm=args.try_vllm, + use_response_cache=not args.ignore_response_cache, ) try: results = calc_dna_parallel( @@ -274,6 +303,7 @@ def main(argv: Optional[Iterable[str]] = None) -> int: max_length=args.max_length, output_dir=args.output_dir, output_path=args.output_path if single_model_run else None, + output_suffix=args.output_suffix, save=not args.no_save, load_in_8bit=args.load_in_8bit, load_in_4bit=args.load_in_4bit, @@ -285,7 +315,13 @@ def main(argv: Optional[Iterable[str]] = None) -> int: gpu_id=gpu_id, log_level=args.log_level, random_seed=args.random_seed, + generation_seed=args.generation_seed, + temperature=args.temperature, + do_sample=args.do_sample, + top_p=args.top_p, use_chat_template=args.use_chat_template, + try_vllm=args.try_vllm, + use_response_cache=not args.ignore_response_cache, ) result = calc_dna(config) diff --git a/src/llm_dna/models/ModelWrapper.py b/src/llm_dna/models/ModelWrapper.py index a01e9b3..3b6cb31 100644 --- a/src/llm_dna/models/ModelWrapper.py +++ b/src/llm_dna/models/ModelWrapper.py @@ -1074,7 +1074,8 @@ def generate( max_tokens=max_length, temperature=temperature, top_p=top_p, - n=1 + n=1, + seed=kwargs.pop("seed", None), ) outputs = self._engine.generate([prompt], params) if outputs and outputs[0].outputs: @@ -1109,6 +1110,7 @@ def generate_batch( temperature=temperature, top_p=top_p, n=1, + seed=kwargs.pop("seed", None), ) outs = self._engine.generate(formatted, params) result: List[str] = [] From 65ac80b18786f9157b11a754e0ce0f131c528a99 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Mon, 10 Aug 2026 21:02:15 +0800 Subject: [PATCH 23/28] Add targeted primary experiment recovery workflow --- ...ary_robustness_failed_slots_20260805.jsonl | 72 +++++++++ docs/primary_robustness_recovery_3090.md | 38 +++++ scripts/build_primary_recovery_configs.py | 148 ++++++++++++++++++ scripts/build_primary_run_grid.py | 26 ++- ...ecover_primary_robustness_failures_3090.sh | 143 +++++++++++++++++ 5 files changed, 420 insertions(+), 7 deletions(-) create mode 100644 configs/primary_robustness_failed_slots_20260805.jsonl create mode 100644 docs/primary_robustness_recovery_3090.md create mode 100755 scripts/build_primary_recovery_configs.py create mode 100755 scripts/recover_primary_robustness_failures_3090.sh diff --git a/configs/primary_robustness_failed_slots_20260805.jsonl b/configs/primary_robustness_failed_slots_20260805.jsonl new file mode 100644 index 0000000..c63bc22 --- /dev/null +++ b/configs/primary_robustness_failed_slots_20260805.jsonl @@ -0,0 +1,72 @@ +{"model_id":"TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T","provider":"huggingface","temperature":0.2,"top_p":0.8,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"MoxoffSrL/Moxoff-Phi3Mini-KTO","provider":"huggingface","temperature":0.2,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"SeacomSrl/SeaPhi3-mini","provider":"huggingface","temperature":0.2,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_empty_response"} +{"model_id":"SeacomSrl/SeaPhi3-mini","provider":"huggingface","temperature":0.3,"top_p":0.8,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"EleutherAI/pythia-410m-seed3","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"SeacomSrl/SeaPhi3-mini","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"ToxicityPrompts/PolyGuard-Qwen-Smol","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"state-spaces/mamba-370m-hf","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"EleutherAI/pythia-410m-seed3","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"Ihor/Text2Graph-R1-Qwen2.5-0.5b","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"SeacomSrl/SeaPhi3-mini","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"ToxicityPrompts/PolyGuard-Qwen-Smol","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"robowaifudev/megatron-gpt2-345m","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"state-spaces/mamba-370m-hf","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"tiiuae/Falcon-H1-3B-Base","provider":"huggingface","temperature":0.3,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_empty_response"} +{"model_id":"EleutherAI/pythia-410m-seed1","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"Ihor/Text2Graph-R1-Qwen2.5-0.5b","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"ToxicityPrompts/PolyGuard-Qwen-Smol","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"robowaifudev/megatron-gpt2-345m","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"unsloth/Qwen3-0.6B-Base","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"yec019/fbopt-350m-8bit","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_missing_file"} +{"model_id":"EleutherAI/pythia-410m-seed1","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"EleutherAI/pythia-410m-seed3","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"GritLM/emb_m7_nodes16_fast","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"SeacomSrl/SeaPhi3-mini","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"ToxicityPrompts/PolyGuard-Qwen-Smol","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"robowaifudev/megatron-gpt2-345m","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"state-spaces/mamba-370m-hf","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"tiiuae/falcon-rw-1b","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"yec019/fbopt-350m-8bit","provider":"huggingface","temperature":0.3,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_missing_file"} +{"model_id":"tiiuae/Falcon-H1-0.5B-Base","provider":"huggingface","temperature":0.5,"top_p":0.8,"repeat":2,"generation_seed":42,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Meta-Llama-3.1-8B","provider":"huggingface","temperature":0.5,"top_p":0.8,"repeat":2,"generation_seed":42,"prior_status":"failed_empty_response"} +{"model_id":"tiiuae/Falcon-H1-0.5B-Base","provider":"huggingface","temperature":0.5,"top_p":0.9,"repeat":1,"generation_seed":42,"prior_status":"failed_empty_response"} +{"model_id":"yaojialzc/Gigi-Llama3-8B-Chinese-zh","provider":"huggingface","temperature":0.5,"top_p":0.9,"repeat":1,"generation_seed":42,"prior_status":"failed_empty_response"} +{"model_id":"ibm-granite/granite-3.1-3b-a800m-base","provider":"huggingface","temperature":0.5,"top_p":0.9,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"tiiuae/Falcon-H1-0.5B-Base","provider":"huggingface","temperature":0.5,"top_p":0.9,"repeat":2,"generation_seed":42,"prior_status":"failed_empty_response"} +{"model_id":"tartuNLP/Llammas-base-p1-GPT-4o-human-error-mix-paragraph-GEC","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":1,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"utter-project/EuroLLM-1.7B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":1,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"Azurro/APT3-1B-Base","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"DeepMount00/Llama-3.1-8b-ITA","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"SakanaAI/TinySwallow-1.5B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"ToxicityPrompts/PolyGuard-Qwen-Smol","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"Voicelab/trurl-2-7b","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"aisingapore/Llama-SEA-LION-v3-8B-IT","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"anakin87/Llama-3-8b-ita-ties-pro","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"authormist/authormist-originality","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"m-a-p/YuE-s2-1B-general","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"mii-llm/minerva-chat-v0.1-alpha-sft","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"princeton-nlp/Sheared-LLaMA-2.7B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"rombodawg/rombos_Replete-Coder-Llama3-8B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"stabilityai/stable-code-3b","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"tiiuae/Falcon-H1-1.5B-Base","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"unsloth/Meta-Llama-3.1-8B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"unsloth/Qwen2.5-1.5B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"unsloth/Qwen3-4B-Instruct-2507","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"utter-project/EuroLLM-1.7B","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_missing_file"} +{"model_id":"yaojialzc/Gigi-Llama3-8B-Chinese-zh","provider":"huggingface","temperature":0.5,"top_p":1.0,"repeat":2,"generation_seed":42,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen2.5-0.5B","provider":"huggingface","temperature":0.7,"top_p":0.8,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen2.5-1.5B","provider":"huggingface","temperature":0.7,"top_p":0.8,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen3-0.6B-Base","provider":"huggingface","temperature":0.7,"top_p":0.8,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/gemma-3-1b-it","provider":"huggingface","temperature":0.7,"top_p":0.8,"repeat":2,"generation_seed":42002,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen3-0.6B-Base","provider":"huggingface","temperature":0.7,"top_p":0.9,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"togethercomputer/LLaMA-2-7B-32K","provider":"huggingface","temperature":0.7,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/gemma-3-1b-it","provider":"huggingface","temperature":0.7,"top_p":0.9,"repeat":2,"generation_seed":42002,"prior_status":"failed_empty_response"} +{"model_id":"numind/NuExtract-1.5-tiny","provider":"huggingface","temperature":0.7,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen2.5-0.5B","provider":"huggingface","temperature":0.7,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen2.5-1.5B","provider":"huggingface","temperature":0.7,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/Qwen3-0.6B-Base","provider":"huggingface","temperature":0.7,"top_p":1.0,"repeat":1,"generation_seed":42001,"prior_status":"failed_empty_response"} +{"model_id":"unsloth/gemma-3-1b-it","provider":"huggingface","temperature":0.7,"top_p":1.0,"repeat":2,"generation_seed":42002,"prior_status":"failed_empty_response"} diff --git a/docs/primary_robustness_recovery_3090.md b/docs/primary_robustness_recovery_3090.md new file mode 100644 index 0000000..8a590a7 --- /dev/null +++ b/docs/primary_robustness_recovery_3090.md @@ -0,0 +1,38 @@ +# Primary robustness recovery on the 3090 host + +The committed recovery plan contains the 72 unsuccessful model/cell/round +slots from the grid snapshot completed on 5 August 2026. It preserves each +cell's original temperature, top-p, sampling mode, output suffix, and generation +seed. Successful slots are not included. + +After pulling the `dna/experiment-script` branch on the 3090 host, activate the +same Python environment used for LLM-DNA and run a command preview: + +```bash +DRY_RUN=1 bash scripts/recover_primary_robustness_failures_3090.sh +``` + +Launch the recovery on GPU 0: + +```bash +mkdir -p logs/primary_robustness_recovery_20260810 +nohup env GPUS=0 MAX_CONCURRENT_GPUS=1 \ + bash scripts/recover_primary_robustness_failures_3090.sh \ + > logs/primary_robustness_recovery_20260810/nohup.log 2>&1 & +``` + +Set `HF_TOKEN` in the environment first if private or gated Hugging Face models +require it. To use multiple GPUs, set both `GPUS` and `MAX_CONCURRENT_GPUS`, for +example `GPUS=0,1 MAX_CONCURRENT_GPUS=2`. + +The launcher is restart-safe. On a later invocation it continues missing work, +does not rerun recorded successes, and retries recorded failures. Outputs are +kept separate from the original grid under: + +```text +out/primary_robustness_recovery_20260810/ +``` + +Copy that complete directory back into the main repository's `out/` directory +after the run. The grid audit treats a successful recovery record as satisfying +the corresponding historical failure. diff --git a/scripts/build_primary_recovery_configs.py b/scripts/build_primary_recovery_configs.py new file mode 100755 index 0000000..f15fe6d --- /dev/null +++ b/scripts/build_primary_recovery_configs.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +"""Validate a failed-slot plan and build one Hugging Face list per grid cell.""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + + +ALLOWED_FAILURES = {"failed_empty_response", "failed_missing_file"} + + +def cell_id(row: dict[str, Any]) -> str: + temperature = int(round(float(row["temperature"]) * 10)) + top_p = int(round(float(row["top_p"]) * 10)) + return f"t{temperature:02d}_p{top_p:02d}_r{int(row['repeat'])}" + + +def load_plan(path: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + with path.open(encoding="utf-8") as handle: + for line_number, line in enumerate(handle, start=1): + line = line.strip() + if not line: + continue + try: + row = json.loads(line) + except json.JSONDecodeError as exc: + raise ValueError(f"Invalid JSON on {path}:{line_number}") from exc + required = { + "model_id", + "provider", + "temperature", + "top_p", + "repeat", + "generation_seed", + "prior_status", + } + missing = required - set(row) + if missing: + raise ValueError( + f"Missing fields on {path}:{line_number}: {sorted(missing)}" + ) + if row["provider"] != "huggingface": + raise ValueError(f"Unsupported provider on line {line_number}") + if row["prior_status"] not in ALLOWED_FAILURES: + raise ValueError(f"Unexpected prior status on line {line_number}") + rows.append(row) + return rows + + +def validate(rows: list[dict[str, Any]], expected_slots: int) -> None: + if len(rows) != expected_slots: + raise ValueError(f"Expected {expected_slots} failed slots, found {len(rows)}") + + keys = [ + ( + str(row["model_id"]), + float(row["temperature"]), + float(row["top_p"]), + int(row["repeat"]), + ) + for row in rows + ] + if len(set(keys)) != len(keys): + duplicates = [key for key, count in Counter(keys).items() if count > 1] + raise ValueError(f"Duplicate failed-slot keys: {duplicates}") + + for row in rows: + temperature = float(row["temperature"]) + repeat = int(row["repeat"]) + expected_seed = 42 if temperature == 0.5 else 42000 + repeat + if int(row["generation_seed"]) != expected_seed: + raise ValueError( + f"Wrong generation seed for {row['model_id']} {cell_id(row)}: " + f"expected {expected_seed}, found {row['generation_seed']}" + ) + + +def atomic_write(path: Path, content: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text(content, encoding="utf-8") + temporary.replace(path) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--plan", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--expected-slots", type=int, default=72) + args = parser.parse_args() + + rows = load_plan(args.plan) + validate(rows, args.expected_slots) + + grouped: dict[tuple[float, float, int, int], list[dict[str, Any]]] = defaultdict(list) + for row in rows: + key = ( + float(row["temperature"]), + float(row["top_p"]), + int(row["repeat"]), + int(row["generation_seed"]), + ) + grouped[key].append(row) + + index_lines = ["cell_id\ttemperature\ttop_p\trepeat\tgeneration_seed\ttask_count\tconfig"] + for key in sorted(grouped): + temperature, top_p, repeat, generation_seed = key + cell_rows = sorted(grouped[key], key=lambda row: str(row["model_id"])) + identifier = cell_id(cell_rows[0]) + config_path = args.output_dir / f"{identifier}.jsonl" + content = "".join( + json.dumps( + {"model_id": row["model_id"], "provider": "huggingface"}, + ensure_ascii=False, + ) + + "\n" + for row in cell_rows + ) + atomic_write(config_path, content) + index_lines.append( + "\t".join( + [ + identifier, + str(temperature), + str(top_p), + str(repeat), + str(generation_seed), + str(len(cell_rows)), + str(config_path), + ] + ) + ) + + atomic_write(args.output_dir / "cells.tsv", "\n".join(index_lines) + "\n") + failures = Counter(str(row["prior_status"]) for row in rows) + print( + f"Prepared {len(rows)} failed slots across {len(grouped)} cells: " + + ", ".join(f"{status}={count}" for status, count in sorted(failures.items())) + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/build_primary_run_grid.py b/scripts/build_primary_run_grid.py index 903a658..3311f54 100644 --- a/scripts/build_primary_run_grid.py +++ b/scripts/build_primary_run_grid.py @@ -19,6 +19,7 @@ / "top_p_control_t05_t05_p06_r2" / "manifest.json" ) +COHORT_CONFIG = ROOT / "configs" / "rand_chinese_validated100_latest.jsonl" REPORT_DIR = ROOT / "reports" TEMPERATURES = (0.0, 0.2, 0.3, 0.5, 0.7) @@ -28,12 +29,19 @@ def load_cohort() -> set[str]: - manifest = json.loads(COHORT_MANIFEST.read_text(encoding="utf-8")) - models = { - str(task["model_id"]) - for task in manifest.get("tasks", []) - if task.get("model_id") - } + if COHORT_MANIFEST.exists(): + manifest = json.loads(COHORT_MANIFEST.read_text(encoding="utf-8")) + models = { + str(task["model_id"]) + for task in manifest.get("tasks", []) + if task.get("model_id") + } + else: + models = { + str(json.loads(line)["model_id"]) + for line in COHORT_CONFIG.read_text(encoding="utf-8").splitlines() + if line.strip() + } if len(models) != 100: raise ValueError(f"Expected 100 fixed models, found {len(models)}") return models @@ -143,7 +151,11 @@ def collect_runs( manifest_path.parent / "status.jsonl" ).items(): if model_id in cohort: - run["final"][model_id] = status + # A valid success from a targeted recovery satisfies the slot + # even if another matching historical run ended in failure. + previous = run["final"].get(model_id) + if previous != "success" or status == "success": + run["final"][model_id] = status return runs, ignored diff --git a/scripts/recover_primary_robustness_failures_3090.sh b/scripts/recover_primary_robustness_failures_3090.sh new file mode 100755 index 0000000..c9ee7a5 --- /dev/null +++ b/scripts/recover_primary_robustness_failures_3090.sh @@ -0,0 +1,143 @@ +#!/usr/bin/env bash +# Rerun only the 72 failed slots from the validated-100 two-round primary grid. + +set -uo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" +if [[ ! -x "$PYTHON_BIN" ]]; then + PYTHON_BIN="$(command -v python3)" +fi + +PLAN="${PLAN:-configs/primary_robustness_failed_slots_20260805.jsonl}" +OUTPUT_DIR="${OUTPUT_DIR:-out/primary_robustness_recovery_20260810}" +LOG_DIR="${LOG_DIR:-logs/primary_robustness_recovery_20260810}" +GENERATED_CONFIG_DIR="$LOG_DIR/generated_configs" +GPUS="${GPUS:-0}" +MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-1}" +MIN_GPU_FREE_GB="${MIN_GPU_FREE_GB:-4}" +MODEL_TIMEOUT_SECONDS="${MODEL_TIMEOUT_SECONDS:-43200}" +MAX_RETRIES="${MAX_RETRIES:-3}" +OOM_RETRIES="${OOM_RETRIES:-3}" +RETRY_DELAY_SECONDS="${RETRY_DELAY_SECONDS:-300}" +CACHE_EVICT_FINISHED="${CACHE_EVICT_FINISHED:-success}" +TRY_VLLM="${TRY_VLLM:-1}" +DRY_RUN="${DRY_RUN:-0}" + +mkdir -p "$OUTPUT_DIR" "$LOG_DIR" "$GENERATED_CONFIG_DIR" + +LOCK_PATH="$LOG_DIR/recovery.lock" +exec 9>"$LOCK_PATH" +if ! flock -n 9; then + echo "Another recovery process already holds $LOCK_PATH" >&2 + exit 1 +fi + +if [[ ! -f "$PLAN" ]]; then + echo "Missing recovery plan: $PLAN" >&2 + exit 1 +fi +if [[ ! -f data/rand/rand_dataset_chinese.json ]]; then + echo "Missing prompt dataset: data/rand/rand_dataset_chinese.json" >&2 + exit 1 +fi + +"$PYTHON_BIN" scripts/build_primary_recovery_configs.py \ + --plan "$PLAN" \ + --output-dir "$GENERATED_CONFIG_DIR" \ + --expected-slots 72 + +GLOBAL_STATUS="$OUTPUT_DIR/dna_global_status.jsonl" +GLOBAL_STATE="$OUTPUT_DIR/dna_global_state.json" +MASTER_LOG="$LOG_DIR/recovery_master.log" + +run_phase() { + local cell="$1" + local temperature="$2" + local top_p="$3" + local repeat="$4" + local generation_seed="$5" + local task_count="$6" + local config_path="$7" + local mode="$8" + local status_path="$OUTPUT_DIR/primary_recovery_${cell}/status.jsonl" + local cell_log="$LOG_DIR/primary_recovery_${cell}_${mode}.log" + local command=( + "$PYTHON_BIN" scripts/run_hf_dna_pipeline.py + --providers huggingface + --hf-jsonl "$config_path" + --dataset rand_chinese + --max-samples 100 + --output-dir "$OUTPUT_DIR" + --output-suffix "_${cell}" + --run-name "primary_recovery_${cell}" + --global-status-journal "$GLOBAL_STATUS" + --global-state "$GLOBAL_STATE" + --temperature "$temperature" + --top-p "$top_p" + --generation-seed "$generation_seed" + --ignore-response-cache + --gpus "$GPUS" + --max-concurrent-gpus "$MAX_CONCURRENT_GPUS" + --min-gpu-free-gb "$MIN_GPU_FREE_GB" + --gpu-poll-seconds 15 + --model-timeout-seconds "$MODEL_TIMEOUT_SECONDS" + --max-retries "$MAX_RETRIES" + --oom-retries "$OOM_RETRIES" + --retry-delay-seconds "$RETRY_DELAY_SECONDS" + --gpu-memory-per-billion-gb 2.2 + --gpu-memory-headroom-gb 2 + --cache-evict-finished "$CACHE_EVICT_FINISHED" + --resume-mode "$mode" + ) + + if [[ -f "$status_path" ]]; then + command+=(--resume-status "$status_path") + fi + if [[ "$temperature" == "0.0" ]]; then + command+=(--no-do-sample) + else + command+=(--do-sample) + fi + if [[ "$TRY_VLLM" == "1" ]]; then + command+=(--try-vllm) + fi + + { + echo "[$(date '+%Y-%m-%d %H:%M:%S')] cell=$cell tasks=$task_count mode=$mode" + printf "command:" + printf " %q" "${command[@]}" + printf "\n" + } | tee -a "$MASTER_LOG" + + if [[ "$DRY_RUN" == "1" ]]; then + return 0 + fi + if "${command[@]}" 2>&1 | tee "$cell_log"; then + echo "[$(date '+%Y-%m-%d %H:%M:%S')] completed $cell mode=$mode" | tee -a "$MASTER_LOG" + else + echo "[$(date '+%Y-%m-%d %H:%M:%S')] retained failures in $cell mode=$mode" | tee -a "$MASTER_LOG" >&2 + fi +} + +tail -n +2 "$GENERATED_CONFIG_DIR/cells.tsv" | +while IFS=$'\t' read -r cell temperature top_p repeat generation_seed task_count config_path; do + status_path="$OUTPUT_DIR/primary_recovery_${cell}/status.jsonl" + if [[ -f "$status_path" ]]; then + # Continue an interrupted cell without repeating successes, then retry its + # terminal failures. A completed successful cell queues zero models. + run_phase "$cell" "$temperature" "$top_p" "$repeat" \ + "$generation_seed" "$task_count" "$config_path" continue + run_phase "$cell" "$temperature" "$top_p" "$repeat" \ + "$generation_seed" "$task_count" "$config_path" retry-failed + else + run_phase "$cell" "$temperature" "$top_p" "$repeat" \ + "$generation_seed" "$task_count" "$config_path" all + fi +done + +echo "[$(date '+%Y-%m-%d %H:%M:%S')] recovery pass complete" | tee -a "$MASTER_LOG" +echo "Recovery artifacts: $OUTPUT_DIR" +echo "Copy that directory back under this repository's out/ directory for auditing." From 4285bdbb6782eb5a1b4c70cccfce04c4b9873328 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Thu, 13 Aug 2026 12:38:46 +0800 Subject: [PATCH 24/28] Implement DistDNA RFFTrace experiment --- ...ess_experiment_full_updated_2026-08-05.pdf | Bin 0 -> 371425 bytes ...ess_experiment_full_updated_2026-08-13.pdf | Bin 0 -> 383905 bytes ...ess_experiment_full_updated_2026-08-13.tex | 1271 +++++++++++++++++ scripts/evaluate_rfftrace_svm.py | 213 +++ scripts/run_primary_rfftrace_k1.sh | 52 + scripts/run_rbf_mmd_pilot.py | 29 +- scripts/run_rfftrace_experiment.py | 596 ++++++++ src/llm_dna/__init__.py | 5 + src/llm_dna/distributional.py | 269 ++++ tests/test_distributional.py | 111 ++ tests/test_rbf_mmd_pilot.py | 44 + tests/test_rfftrace_experiment.py | 88 ++ tests/test_rfftrace_svm.py | 61 + 13 files changed, 2722 insertions(+), 17 deletions(-) create mode 100644 docs/primary_robustness_experiment_full_updated_2026-08-05.pdf create mode 100644 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Ut(EA=7}XQuX%p>c)#&h$%U7Whbqowe^$Di*%gO5>=2Vp^MfH0lTsUXl-zL%*?Thf@y=GLT g>WRWrqi9$tCpRfOmv!@zPWs)xkcJK0bczZ2KVA-&zW@LL literal 0 HcmV?d00001 diff --git a/docs/primary_robustness_experiment_full_updated_2026-08-13.tex b/docs/primary_robustness_experiment_full_updated_2026-08-13.tex new file mode 100644 index 0000000..4d9fa6b --- /dev/null +++ b/docs/primary_robustness_experiment_full_updated_2026-08-13.tex @@ -0,0 +1,1271 @@ +\documentclass[11pt,a4paper]{article} + +\usepackage[margin=1in]{geometry} +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} +\usepackage{amsmath,amssymb} +\usepackage{booktabs} +\usepackage{graphicx} +\usepackage{longtable} +\usepackage{multirow} +\usepackage{xcolor} +\usepackage{hyperref} +\usepackage{listings} +\usepackage{enumitem} + +\hypersetup{hidelinks} +\lstset{ + basicstyle=\ttfamily\small, + breaklines=true, + frame=single, + columns=fullflexible, + keepspaces=true +} + +\newcommand{\todoresult}{\textcolor{gray}{\textit{Not yet available}}} +\newcommand{\method}[1]{\texttt{#1}} +\newcommand{\demovalue}[1]{\textcolor{blue!65!black}{#1\textsuperscript{*}}} + +\title{Primary LLM-DNA Robustness Experiment\\ +\large Updated Full Experimental Protocol and Progress Report} +\author{} +\date{Updated August 13, 2026} + +\begin{document} +\maketitle + +\section*{Executive summary} +This document records the design and current execution status of the primary +LLM-DNA decoding robustness experiment. The study +will measure how temperature and top-$p$ affect exact-model identification, +whether a classifier can mitigate the resulting accuracy loss, and whether a +multi-sample distributional representation improves robustness under a matched +generation budget. The fixed primary cohort contains 100 validated models. +The experiment contains one deterministic condition and a complete +$4\times3$ stochastic grid, with two generation rounds per condition. The +principal deployment setting +assumes that temperature and top-$p$ are hidden at inference time and only an +upper bound on temperature is known. The frozen pre-recovery audit of August 5 +contains 2,528 successful artifacts out of 2,600 planned slots. Of those +successful artifacts, 572 record an explicit generation seed and 1,956 come +from historical manifests that do not record a separate generation seed. +Thus, 97.2\% is an artifact-coverage statistic, not a seed-clean completion +rate. The 72 unsuccessful slots affect 41 distinct models, leaving a strict +all-grid common cohort of 59 in that snapshot. Targeted recovery was launched +on August 13. Small-scale exploratory results are reported separately and are +not substitutes for the final primary analysis. +\par + +\section{Status and scope} + +\textbf{Primary experiment status (pre-recovery snapshot, August 5, 2026):} +the grid contains 2,528/2,600 successful artifacts (97.2\%). Two of 13 settings +are fully complete. The 72 remaining slots are +52 \texttt{failed\_missing\_file} and 20 +\texttt{failed\_empty\_response}; no ordinary pending slots remain. The strict +intersection across all 26 runs contains 59 models and is below the +preregistered minimum of 80. Section~\ref{sec:small-demo} contains explicitly +labelled exploratory demonstrations that must not be interpreted as final +primary findings. A failure-specific recovery job is running from the fixed +72-slot plan under \path{out/primary_robustness_recovery_20260810}; all final +counts must be regenerated after that job finishes. + +A resource-feasibility review reduced the plan from 300 models and four repeats +to 100 models and two rounds. Repeat 1 is the reference/training split and +repeat 2 is held out. Exact identification is a 100-way closed-set task before +failure filtering. + +\begin{table}[htbp] +\centering +\caption{Frozen pre-recovery primary-grid snapshot from August 5. Each cell +combines two 100-model rounds.} +\label{tab:live-grid} +\begin{tabular}{crrr} +\toprule +Temperature & $p=0.8$ & $p=0.9$ & $p=1.0$ \\ +\midrule +$0$ & -- & -- & $200/200$ \\ +$0.2$ & $199/200$ & $200/200$ & $198/200$ \\ +$0.3$ & $199/200$ & $189/200$ & $185/200$ \\ +$0.5$ & $198/200$ & $196/200$ & $176/200$ \\ +$0.7$ & $196/200$ & $197/200$ & $195/200$ \\ +\bottomrule +\end{tabular} +\end{table} + +The high slot-completion rate and the smaller complete-case cohort are not +contradictory. Failures occur in different models across cells: 72 failed slots +are distributed over 41 models, so $100-41=59$ models succeed everywhere. +The 2,528 successful slots do include historical runs whose generation seed is +unknown: 572 successes have an explicit generation seed and 1,956 do not. +Unknown-seed runs remain usable for paired representation comparisons when +every method reads the identical responses, but they are not treated as +independent seeded evidence for causal decoding claims. +For setting-specific paired evaluation, the usable r1--r2 cohorts remain much +larger: + +\begin{table}[htbp] +\centering +\small +\caption{Paired cohort with both rounds successful in each setting.} +\label{tab:paired-cohorts} +\begin{tabular}{crrcrrr} +\toprule +$T$ & $p$ & Paired $N$ && $T$ & $p$ & Paired $N$ \\ +\midrule +0.0 & 1.0 & 100 && 0.3 & 0.9 & 93 \\ +0.2 & 0.8 & 99 && 0.3 & 1.0 & 89 \\ +0.2 & 0.9 & 100 && 0.5 & 0.8 & 98 \\ +0.2 & 1.0 & 98 && 0.5 & 0.9 & 97 \\ +0.3 & 0.8 & 99 && 0.5 & 1.0 & 77 \\ +0.7 & 0.8 & 96 && 0.7 & 0.9 & 97 \\ +0.7 & 1.0 & 95 && & & \\ +\bottomrule +\end{tabular} +\end{table} + +The common cohorts for bounded analyses are 97 at $\tau=0.2$, 86 at +$\tau=0.3$, 63 at $\tau=0.5$, and 59 at $\tau=0.7$. Targeted recovery should +first repair 21 models that each lack only one slot; if successful, this is the +minimum route to restore an 80-model all-grid cohort. + +This experiment replaces the 77-model interim analysis as the primary +robustness study. The earlier analysis remains a legacy diagnostic because it +used an incomplete sweep, one generation repeat per reported cell, an +historical reference gallery with unknown decoding parameters, and a binary +same-or-related task that was not directly comparable to exact-model Top-1 +retrieval. + +\section{Research questions and hypotheses} + +\subsection{Primary research questions} + +\begin{enumerate}[label=\textbf{RQ\arabic*.}] + \item How do temperature and top-$p$ affect exact-model classification from + LLM-DNA representations? + \item On the same exact-model task, can a classifier trained on the full DNA + vector reduce the accuracy degradation relative to cosine retrieval? + \item Does the proposed multi-sample distributional representation improve + robustness beyond original DNA under the same response and token + budget? + \item If temperature and top-$p$ are unknown at inference time, but + temperature is known to satisfy $T\leq\tau$, what average and + worst-setting accuracy can be expected? +\end{enumerate} + +\subsection{Secondary research questions} + +\begin{enumerate}[label=\textbf{SRQ\arabic*.}] + \item When exact identification fails, does the predicted model remain close + to the true model in the Hugging Face model tree? + \item Do direct parent--child, indirect, and disconnected model pairs exhibit + different DNA-distance distributions as decoding stochasticity rises? + \item How sensitive are the conclusions to response encoder, truncation, + projection dimension, projection seed, and the number of response + samples? +\end{enumerate} + +The main hypothesis is that temperature will cause a larger reduction in exact +classification accuracy than top-$p$ over the planned range. Classifier and +distributional-method benefits will be judged by paired accuracy differences +and by a shallower degradation curve, rather than by comparing accuracies from +different tasks. + +\section{Model cohort} + +\subsection{Fixed validated cohort} + +The primary cohort is the 100-model list exported from the latest validated +control manifest. Historical artifacts count only when their exact model ID, +decoding cell, and round match this cohort and grid. Performance was not used +to add or remove models. + +The fixed artifacts are: + +\begin{itemize} + \item cohort: \path{configs/rand_chinese_validated100_latest.jsonl}; + \item canonical manifest: + \path{out/top_p_control_t05_validated100_20260719/top_p_control_t05_t05_p06_r2/manifest.json}; + \item live audit: \path{reports/primary_robustness_run_grid.csv}; and + \item cohort SHA-256: + \texttt{9043a62b95436b9fc92671b6f5b11b15d427fddd745dba2f84c112d97e850e6d}. +\end{itemize} + +Any operational replacement would define a new cohort version and require the +grid and every common-cohort count to be regenerated. + +\subsection{Completion requirement} + +The intended final analysis uses all 100 models. The automated primary analysis +will refuse to run if fewer than 80 models are common to all required training +and test runs. Missing models should first be backfilled. If the final common +cohort remains below 100, the report will provide success rates and failure +reasons by decoding cell and compare retained and excluded models by size and +architecture. + +\section{Prompt set and DNA construction} + +Every model receives the same 100 synthetic classical-Chinese prompts from +\path{data/rand/rand_dataset_chinese.json}. The prompt construction and random +projection seed remain fixed at 42 for every model, setting, and repeat. + +For the original representation, each response is encoded using +\method{all-mpnet-base-v2}. The 100 response embeddings are concatenated and +mapped to a 128-dimensional DNA vector by a seeded Gaussian random projection. +The current maximum response length is 1024 generated tokens. The saved +metadata records at least the model ID, dataset, prompt count, temperature, +top-$p$, sampling flag, generation seed, maximum length, projection seed, +encoder, and DNA dimension. + +Because the sentence encoder is primarily English-oriented and truncates long +inputs, the final study will additionally report response-length and encoder +token-length distributions, the fraction exceeding the encoder limit, and a +sensitivity analysis with a Chinese or multilingual response encoder. + +\section{Decoding design} + +\subsection{Factorial grid} + +The deterministic condition is +\[ + T=0, \qquad \text{sampling disabled}, \qquad p=1.0\text{ (recorded only)}. +\] +Top-$p$ is inactive when sampling is disabled; consequently, $T=0$ appears once +and is not repeated as three nominal top-$p$ cells. + +For stochastic decoding, the complete grid is +\[ + T\in\{0.2,0.3,0.5,0.7\}, \qquad + p\in\{0.8,0.9,1.0\}. +\] +This gives 13 decoding conditions in total. Two generation rounds are planned +for every condition, giving 26 run cells and +\[ + 100\ \text{models}\times13\ \text{conditions}\times2\ \text{rounds} + =2{,}600 +\] +successful artifacts before retries. + +\subsection{Generation rounds and seeds} + +The response-sampling seeds are: + +\begin{table}[ht] +\centering +\caption{Generation-repeat assignment. The projection seed remains 42 in all rows.} +\label{tab:repeat-assignment} +\begin{tabular}{ccc} +\toprule +Repeat & Generation seed & Primary role \\ +\midrule +1 & 42001 (42 in existing $T=0.5$ control) & training/reference \\ +2 & 42002 (42 in existing $T=0.5$ control) & held-out query \\ +\bottomrule +\end{tabular} +\end{table} + +The shared $T=0.5$ cells require a duplicate/independence audit because both +existing rounds use seed 42. Historical sweep artifacts also did not record a +separate response-generation seed. In the August 5 snapshot, 572 of 2,528 +successful artifacts record an explicit generation seed and 1,956 do not. +These limitations affect causal +temperature interpretation but do not invalidate paired method comparisons +when every method reads exactly the same responses. + +No response, DNA vector, or round may appear in both the training/reference +split and the held-out query split. Generation seed and projection seed are +stored separately so that response variability is not confounded with a change +in the random projection. + +\section{Primary exact-model task} + +Let $\mathcal{M}$ be the fixed common model cohort. Given a query DNA vector +$\mathbf{d}_{m,s,r}$ generated by model $m$, decoding setting $s=(T,p)$, and +held-out repeat $r=2$ in Table~\ref{tab:repeat-assignment}, each method +evaluates every candidate +$j\in\mathcal{M}$. Let $\mathcal{D}^{\mathrm{train}}_j$ denote the set of +training DNA vectors available for candidate model $j$. Its gallery centroid is +\[ + \boldsymbol{\mu}_j + =\frac{1}{|\mathcal{D}^{\mathrm{train}}_j|} + \sum_{\mathbf{x}\in\mathcal{D}^{\mathrm{train}}_j}\mathbf{x}. +\] +The standard cosine similarity and cosine distance both take the query and the +candidate centroid as inputs: +\[ + S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) + =\frac{\mathbf{d}^{\mathsf T}\boldsymbol{\mu}_j} + {\lVert\mathbf{d}\rVert_2\lVert\boldsymbol{\mu}_j\rVert_2}, + \qquad + D_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) + =1-S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j). +\] +Thus, the cosine rule may be written equivalently as +\[ + \widehat{m}^{(\mathrm{cos})} + =\arg\min_{j\in\mathcal{M}} + D_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j) + =\arg\max_{j\in\mathcal{M}} + S_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j). +\] +No square is applied in the standard cosine distance used by the evaluation +code; each Euclidean norm already satisfies +$\lVert\mathbf{d}\rVert_2=(\sum_k d_k^2)^{1/2}$, but the cosine value itself is +not squared. The squared norm appearing below belongs instead to the RBF kernel. +For the SVM rules, the explicit class-$j$ decision scores and predictions are +\[ + f_j^{(a)}(\mathbf{d})= + \begin{cases} + \mathbf{w}_j^{\mathsf T}\mathbf{z}(\mathbf{d})+b_j, + & a=\mathrm{lin},\\[4pt] + \displaystyle + \sum_{i=1}^{n_{\mathrm{SV}}}\alpha_{ij} + \exp\!\left( + -\gamma\lVert\mathbf{z}(\mathbf{d})-\mathbf{z}(\mathbf{x}_i)\rVert_2^2 + \right)+b_j, + & a=\mathrm{rbf}. + \end{cases} + \qquad + \widehat{m}^{(a)} + =\arg\max_{j\in\mathcal{M}}f_j^{(a)}(\mathbf{d}_{m,s,r}), + \quad a\in\{\mathrm{lin},\mathrm{rbf}\}. +\] +Here $\mathbf{z}(\cdot)$ is the feature-wise standardization learned from the +training split, $(\mathbf{w}_j,b_j)$ are the linear-SVM parameters, and +$\{\mathbf{x}_i,\alpha_{ij}\}_{i=1}^{n_{\mathrm{SV}}}$ and $\gamma$ are the +RBF-SVM support vectors, effective class-$j$ coefficients, and kernel +bandwidth parameter, respectively. The prediction is correct only if +$\widehat{m}^{(a)}=m$. Thus, cosine and SVM methods +are evaluated on identical multiclass labels, queries, and candidate sets. + +\subsection{Methods} + +\begin{table}[ht] +\centering +\caption{Planned representation $\times$ classifier comparison.} +\label{tab:method-matrix} +\begin{tabular}{lll} +\toprule +Representation & Decision rule & Purpose \\ +\midrule +Original 128-D DNA & cosine nearest centroid & primary retrieval baseline \\ +Original 128-D DNA & multiclass linear SVM & classifier-only ablation \\ +Original 128-D DNA & multiclass RBF SVM & nonlinear classifier ablation \\ +Matched-$K$ mean response embedding & cosine retrieval & sample-budget baseline \\ +Response distributions & exact RBF-MMD retrieval & nonparametric diagnostic \\ +Uncompressed RFFTrace & squared Euclidean retrieval & scalable MMD approximation \\ +Compact DistDNA (projected RFFTrace) & Euclidean, linear/RBF SVM & proposed reusable vector \\ +\bottomrule +\end{tabular} +\end{table} + +The linear and RBF SVMs consume the full 128-dimensional DNA vector. The legacy +linear SVM using only negative cosine distance is not considered an exact-model +classification improvement because a monotone linear transformation of cosine +distance cannot alter closed-set candidate ranking. It remains only a binary +verification baseline. + +\subsection{Controlled comparison of old and new methods} + +Fairness is defined by holding the data and evaluation unit fixed while changing +only the representation or decision rule. Every paired comparison uses the +same model IDs, prompts, response files, decoding cell, reference/query split, +candidate set, and metric. + +\begin{table}[htbp] +\centering +\small +\caption{Controlled method-comparison matrix.} +\label{tab:controlled-method-comparison} +\begin{tabular}{p{0.28\linewidth}p{0.20\linewidth}p{0.42\linewidth}} +\toprule +Comparison & What changes & What remains fixed \\ +\midrule +Cosine vs linear/RBF SVM & Decision rule only & Same 128-D DNA, queries, +labels, split, and candidate models \\ +Mean cosine vs RBF-MMD at $K$ & Representation/distance only & Same $K$ +responses per prompt, encoder, prompts, tokens, reference and query sets \\ +Exact RBF-MMD vs RFFTrace & Kernel approximation only & Same embeddings, +bandwidth, samples, prompts, reference and query distributions \\ +RFFTrace vs compact DistDNA & Projection only & Same shared RFF map and data; +one frozen Gaussian projection for every model and setting \\ +$K=1$ point check & Kernel distance only & Same single response on each side; +explicitly not evidence for a multi-sample advantage \\ +\bottomrule +\end{tabular} +\end{table} + +Linear/RBF-SVM hyperparameters are selected only within repeat 1. The RBF-MMD +bandwidth, shared random Fourier map, and optional compact projection are +calibrated or sampled once using only reference/calibration data and then frozen. +Method differences are computed per identical held-out query and summarized by +a paired model-cluster bootstrap. Historical unknown seeds are shared by all +methods in an exploratory comparison rather than being treated as a method +difference. + +\subsection{Known-setting descriptive evaluation} + +For each held-out condition $(T,p)$ and each method $a$, the experiment reports +\[ + A_a(T,p)=\frac{1}{|\mathcal{Q}_{T,p}|} + \sum_{q\in\mathcal{Q}_{T,p}}\mathbb{I}[\widehat{m}_{a,q}=m_q]. +\] +Top-1 is primary; Top-3, Top-5, and mean reciprocal rank (MRR) are secondary. +The change from the deterministic condition is +\[ + \Delta A_a(T,p)=A_a(T,p)-A_a(0). +\] +All per-setting comparisons will use the same held-out model queries. + +\section{Unknown-temperature bounded deployment} + +The main deployment scenario assumes that the exact decoding parameters are +not provided to the classifier. For a temperature bound +\[ + \tau\in\{0.2,0.3,0.5,0.7\}, +\] +one classifier is trained using repeat 1 from every allowed setting with +$T\leq\tau$. Temperature and top-$p$ are omitted from its features. It is +then evaluated on held-out repeat 2 from all allowed settings. + +To avoid overweighting temperatures that happen to have more nominal cells, +repeats are first averaged within each $(T,p)$ cell. The primary mixed accuracy +is the equal-cell mean +\[ + A_a^{\mathrm{mix}}(\tau) + =\frac{1}{|\mathcal{S}_\tau|} + \sum_{s\in\mathcal{S}_\tau} A_a(s), + \qquad + \mathcal{S}_\tau=\{(T,p):T\leq\tau\}. +\] +The robustness guarantee is summarized by the worst observed setting +\[ + A_a^{\mathrm{worst}}(\tau) + =\min_{s\in\mathcal{S}_\tau} A_a(s). +\] +These quantities directly answer how accurately the system performs when the +decoding setting is hidden but its temperature is bounded. + +\section{Distributional and matched-budget evaluation} + +The formal method follows \path{docs/main (2).pdf}, where it is named +RFFTrace and used as the proposed distributional DNA. For prompt $q$, model +$m$, and decoding setting $s$, let +$z_{m,s,q,r}\in\mathbb{R}^{d}$ be the +\method{all-mpnet-base-v2} embedding of independent response $r$. The RBF +kernel and its shared random Fourier feature map are +\[ + k_\sigma(a,b)=\exp\!\left(-\frac{\lVert a-b\rVert_2^2}{2\sigma^2}\right), + \qquad + \psi_\sigma(z)=\sqrt{\frac{2}{D}} + \bigl[\cos(\omega_j^{\mathsf T}z+b_j)\bigr]_{j=1}^{D}, +\] +where $\omega_j\sim\mathcal{N}(0,\sigma^{-2}I_d)$ and +$b_j\sim\mathrm{Uniform}(0,2\pi)$. A single draw of all +$\{\omega_j,b_j\}$ is shared across every model, prompt, setting, and split. +The empirical distribution embedding for one prompt, followed by the complete +uncompressed RFFTrace, is +\[ + \widehat{\mu}_{m,s,q}=\frac{1}{K}\sum_{r=1}^{K} + \psi_\sigma(z_{m,s,q,r}),\qquad + \widehat{v}_{m,s}=\frac{1}{\sqrt{t}} + [\widehat{\mu}_{m,s,1};\ldots;\widehat{\mu}_{m,s,t}]\in\mathbb{R}^{tD}. +\] +Consequently, +\[ + \lVert\widehat{v}_{a}-\widehat{v}_{b}\rVert_2^2 + \approx \frac{1}{t}\sum_{q=1}^{t} + \widehat{\mathrm{MMD}}_{\sigma}^{,2} + (\{z_{a,q,r}\}_{r=1}^{K},\{z_{b,q,r}\}_{r=1}^{K}), +\] +where exact MMD uses the biased/V-statistic estimator, including diagonal +terms. The compact DistDNA vector is +$\widehat{\tau}_{m,s}=A\widehat{v}_{m,s}$, with one fixed Gaussian +projection $A_{ij}\sim\mathcal{N}(0,1/L)$. This vector can be used directly +for Euclidean retrieval or as input to a linear/RBF SVM. + +The RBF bandwidth is selected by the median pairwise-distance heuristic using +only a disjoint subset of reference prompts. The calibration prompts, +evaluation prompts, encoder, bandwidth, RFF draw, projection, and all seeds are +then frozen. The implementation in +\path{src/llm_dna/distributional.py} provides exact RBF-MMD, the shared RFF +map, prompt-wise kernel means, $1/\sqrt{t}$ concatenation, and compact Gaussian +projection. The executable \path{scripts/run_rfftrace_experiment.py} uses the +real MPNet encoder and exports exact-MMD, RFFTrace, compact-DistDNA, single +cosine, and matched-$K$ mean-cosine predictions on identical data. The earlier +\path{scripts/run_rbf_mmd_pilot.py} is now explicitly a legacy hashing pilot; +it is not the formal proposed method. +The separate \path{scripts/evaluate_rfftrace_svm.py} consumes exported vectors +with explicitly disjoint train/test keys and enforces at least two independent +training vectors per model by default. It implements the downstream linear and +RBF SVM probe, but does not remove the data requirement for valid tuning. + +The planned sample-budget ablation is $K\in\{1,2,4,8\}$, subject to available +independent responses. For each $K$, the report will include Top-1, MRR, +generated tokens, reference storage, and analysis time. Single cosine always +uses one response; the primary fairness comparison at each $K$ is matched-$K$ +mean cosine versus exact MMD, RFFTrace, and compact DistDNA. Exact MMD versus +RFFTrace isolates RFF approximation error, while RFFTrace versus compact +DistDNA isolates projection loss. + +With the present two-round grid, only a disjoint $K=1$ reference/query +comparison is immediately possible. At $K=1$, exact RBF-MMD is a kernel +point-distance check and cannot demonstrate a multi-sample distributional +advantage. A valid $K=2$ experiment needs four independent response sets per +prompt---for example, reference rounds 1 and 3 and query rounds 2 and 4---with +recorded, distinct generation seeds. The same-setting evaluation measures +repeat stability; cross-setting evaluation freezes the reference setting and +varies the query setting to measure decoding robustness. + +The proposed method will be declared better than the strongest matched-budget +baseline only if the paired Top-1 improvement has a model-cluster bootstrap +95\% confidence interval whose lower endpoint is above zero on held-out data. +MRR, worst-setting accuracy, compute, and storage are secondary checks. A +compact DistDNA+SVM comparison additionally requires enough independent +training vectors per class for nested hyperparameter selection; until those +vectors exist, Euclidean retrieval is the primary formal test and the SVM +result remains planned rather than implied. + +\section{Secondary verification and tree-aware analysis} + +\subsection{Binary verification} + +If binary pairwise verification is reported, its tasks are separated into: + +\begin{enumerate} + \item exact same-model versus unrelated; + \item lineage-only (off-diagonal related) versus unrelated; and + \item the legacy same-or-related versus unrelated task. +\end{enumerate} + +Training may downsample negative pairs or use class weighting. Testing uses all +available pairs at their natural prevalence. The main binary metrics are +precision--recall AUC, precision, recall, specificity, Matthews correlation +coefficient, false positives per query, and ROC-AUC. Balanced-test accuracy may +be shown only as an explicitly labelled diagnostic. + +\subsection{Model-tree distance} + +The Hugging Face relation exporter stores direct base-model edges in addition +to connected components. Using the undirected direct-edge graph, the shortest +tree distance is +\[ + d_{\mathrm{tree}}(i,j)= + \begin{cases} + 0, & i=j,\\ + 1, & i\text{ and }j\text{ share a direct edge},\\ + 2,3,\ldots, & \text{connected by the corresponding number of edges},\\ + \infty, & \text{disconnected}. + \end{cases} +\] + +The final report will show DNA-distance distributions for exact, direct +one-hop, indirect two-hop, indirect three-or-more-hop, same-component with +unknown hop distance, and disconnected pairs. For Top-1 predictions, cumulative +tree-aware accuracy will be reported as exact, within one hop, within two hops, +and within the same connected component. This analysis describes error severity +and does not replace exact-model accuracy as the primary endpoint. + +\section{Statistical analysis plan} + +\subsection{Uncertainty} + +Every accuracy and MRR estimate will include a 95\% confidence interval. +Resampling will preserve dependence among observations: + +\begin{itemize} + \item model IDs are resampled as clusters across methods and settings; + \item tree-related analyses use model-tree component clusters where relevant. +\end{itemize} + +Pair-level observations sharing a query model, reference model, or tree +component will not be treated as independent Bernoulli trials. With fixed +training and test roles for rounds 1 and 2, the primary confidence intervals +quantify model-level rather than generation-repeat uncertainty. + +\subsection{Paired comparisons} + +Method differences are computed on identical held-out queries. The principal +quantity is +\[ + \delta_{a,b}(T,p)=A_a(T,p)-A_b(T,p), +\] +with a paired cluster-bootstrap confidence interval. The analysis will also +test whether a method changes the temperature degradation slope, for example +with a mixed-effects logistic model of the form +\[ + \operatorname{logit}\Pr(\mathrm{correct}) + =\beta_0+\beta_1 T+\beta_2 p+\beta_3\mathrm{method} + +\beta_4(T\times\mathrm{method}) + +\beta_5(p\times\mathrm{method})+u_{\mathrm{model}}+u_{\mathrm{repeat}}. +\] +The interaction terms, rather than isolated cell differences, address whether +a method mitigates robustness loss. + +\subsection{Multiplicity and exploratory analyses} + +Top-1 accuracy and bounded mixed accuracy are primary endpoints. Top-3, Top-5, +MRR, tree-aware accuracy, encoder ablations, and projection ablations are +secondary. Any unplanned subgroup or hyperparameter analysis will be labelled +exploratory. If many method-by-setting significance tests are presented, a +false-discovery-rate correction will be applied. + +\section{Ablations and validity checks} + +The following checks are planned where artifacts and compute permit: + +\begin{enumerate} + \item projection dimensions $\{64,128,256,512\}$ and at least five projection + seeds; + \item an unprojected or prompt-level distance baseline; + \item prompt bootstrap and split-half reliability; + \item response encoder comparison including a Chinese or multilingual + encoder; + \item response-token and encoder-token length distributions and truncation + rates by setting; + \item fixed $T=0$ gallery, same-setting repeat gallery, and multi-setting + centroid gallery; + \item leave-one-setting-out evaluation for unseen decoding configurations; + \item performance by model size, architecture, and chat/base status; and + \item failure probability by model attributes and decoding setting. +\end{enumerate} + +\clearpage +\section{Small-scale demonstration report using existing artifacts} +\label{sec:small-demo} + +\subsection{Scope and split} + +To check that the analyses in Sections 6--11 are executable before the primary +sweep is complete, small exploratory demonstrations were run on July 23, 2026, +using only artifacts already present under \path{out/rand_chinese}. No new model +responses were generated. Repeat 1 was used for training/reference data and +repeat 2 for testing. Six complete stochastic cells were available: +\[ + T\in\{0.2,0.3\},\qquad p\in\{0.8,0.9,1.0\}. +\] +The $T=0.5$ and $T=0.7$ cells were excluded because they did not have a +sufficiently complete independent-repeat split. These demonstrations use +convenience cohorts determined by artifact availability, not the fixed +100-model primary cohort. They are therefore pipeline checks and exploratory +effect estimates only. + +\subsection{Executive summary} + +\noindent\fbox{\parbox{0.94\linewidth}{ +\textbf{Demo outcome.} The available artifacts support an end-to-end +demonstration of exact classification, bounded unknown-temperature evaluation, +paired model-cluster uncertainty, exact-only binary verification, a limited +distributional comparison, a limited tree-aware analysis, and a gallery +sensitivity check. Across the six complete cells, the pooled linear SVM had the +highest equal-cell Top-1 (0.722), followed by RBF SVM (0.688) and cosine +retrieval (0.621). The hardest observed cell was $(T,p)=(0.3,1.0)$. These are +exploratory results from availability-selected cohorts and must not be read as +the outcome of the 100-model primary experiment. +}} + +\medskip +Table~\ref{tab:demo-coverage} maps each planned protocol component to what +could actually be demonstrated. ``Partial'' means that the code path ran but +the available cohort or repeat structure could not exercise the full planned +analysis. + +\begin{table}[htbp] +\centering +\caption{Coverage of Sections 6--11 by the small-scale demonstration.} +\label{tab:demo-coverage} +\begin{tabular}{p{0.13\linewidth}p{0.35\linewidth}p{0.23\linewidth}p{0.15\linewidth}} +\toprule +Protocol section & Demonstrated analysis & Available scale & Status \\ +\midrule +6: Exact task + & Cosine centroid, linear SVM, and RBF SVM on identical queries + & Six cells; 84 common models & Complete demo \\ +7: Bounded deployment + & Equal-cell mixed and worst-setting accuracy for $\tau=0.2,0.3$ + & 89 and 84 models & Complete demo \\ +8: Distributional + & Single cosine, mean cosine, and exact RBF-MMD + & Eight models; 20 prompts; at most two repeats & Partial \\ +9: Verification + & Exact-only linear probe at natural pair prevalence + & 95 models; 9,025 pairs per direction & Complete demo \\ +9: Tree-aware + & Exact versus disconnected error and distance summaries + & 95 models; no related pairs & Partial \\ +10: Statistics + & Model-cluster CIs and paired method differences + & One held-out repeat per cell & Partial \\ +11: Validity + & Fixed-$T=0$ versus pooled-gallery diagnostic + & 86 versus 84 models & Partial \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Exact classification and bounded deployment demo} + +For $\tau=0.2$, the common cohort contained 89 models and the training pool +contained the three repeat-1 cells at $T=0.2$. For $\tau=0.3$, requiring all six +cells reduced the common cohort to 84 models. The classifier was not given +temperature or top-$p$. Table~\ref{tab:demo-per-setting} reports held-out +repeat-2 Top-1 accuracy for the $\tau=0.3$ pooled training experiment. + +\begin{table}[ht] +\centering +\caption{Exploratory held-out Top-1 accuracy with 84 common models.} +\label{tab:demo-per-setting} +\begin{tabular}{ccrrr} +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM \\ +\midrule +0.2 & 0.8 & 0.643 & 0.821 & 0.702 \\ +0.2 & 0.9 & 0.726 & 0.845 & 0.762 \\ +0.2 & 1.0 & 0.595 & 0.643 & 0.643 \\ +0.3 & 0.8 & 0.774 & 0.869 & 0.857 \\ +0.3 & 0.9 & 0.690 & 0.786 & 0.762 \\ +0.3 & 1.0 & 0.298 & 0.369 & 0.405 \\ +\bottomrule +\end{tabular} +\end{table} + +Figure~\ref{fig:demo-exact-heatmaps} makes the setting-level pattern explicit. +The linear SVM led in four cells and tied in one, while the RBF SVM was highest +at $(0.3,1.0)$. All methods dropped sharply in that cell, so the demo does not +support a general monotone temperature effect independent of top-$p$. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/exact_top1_heatmaps.png} +\caption{Exploratory exact-model Top-1 by method and decoding cell. Darker +cells indicate higher accuracy. The equal-cell means correspond to the +$\tau=0.3$ pooled-training experiment.} +\label{fig:demo-exact-heatmaps} +\end{figure} + +The equal-cell bounded summaries are shown in +Table~\ref{tab:demo-bounded}. Confidence intervals resample model IDs as +clusters across the included cells. + +\begin{table}[ht] +\centering +\caption{Exploratory bounded-deployment Top-1 summaries.} +\label{tab:demo-bounded} +\begin{tabular}{clrrr} +\toprule +$\tau$ & Method & Mixed accuracy (95\% CI) & Worst cell & Models \\ +\midrule +0.2 & Cosine centroid & 0.596 (0.528, 0.667) & 0.562 & 89 \\ + & Linear SVM & 0.682 (0.614, 0.745) & 0.652 & 89 \\ + & RBF SVM & 0.629 (0.562, 0.700) & 0.584 & 89 \\ +\addlinespace +0.3 & Cosine centroid & 0.621 (0.554, 0.688) & 0.298 & 84 \\ + & Linear SVM & 0.722 (0.675, 0.772) & 0.369 & 84 \\ + & RBF SVM & 0.688 (0.633, 0.742) & 0.405 & 84 \\ +\bottomrule +\end{tabular} +\end{table} + +On identical queries, the paired model-cluster bootstrap difference between +linear SVM and cosine was $0.086$ (95\% CI $0.037$ to $0.135$) at +$\tau=0.2$ and $0.101$ (95\% CI $0.058$ to $0.147$) at $\tau=0.3$. The +corresponding RBF-SVM differences were $0.034$ (95\% CI $0.004$ to $0.071$) +and $0.067$ (95\% CI $0.026$ to $0.111$). These intervals quantify only this +availability-selected demo and do not test the primary hypotheses. + +Figure~\ref{fig:demo-bounded-paired} combines three views that should remain +distinct in interpretation: bounded performance, paired within-query method +gains, and the unpaired gallery diagnostic. In particular, the fixed-gallery +bars are not a controlled estimate of gallery effects because the gallery +cohort and training budget differ from the pooled condition. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/bounded_paired_gallery.png} +\caption{Bounded-deployment and sensitivity summary. Panel A shows equal-cell +mixed accuracy with model-cluster 95\% intervals and the worst observed cell. +Panel B shows paired Top-1 differences relative to cosine. Panel C is an +explicitly unpaired fixed-gallery versus pooled-gallery diagnostic.} +\label{fig:demo-bounded-paired} +\end{figure} + +\subsection{Distributional and matched-budget demo} + +The exact RBF-MMD pilot ran successfully on eight lexicographically selected +models, 20 common evaluation prompts, and two response repeats, using +$T=0.2,p=0.8$ as the reference setting. Single-sample cosine, mean-cosine, and +exact RBF-MMD each achieved Top-1 and MRR of 1.000 for the same-setting +comparison and for cross-setting queries at $(0.3,0.8)$ and $(0.3,0.9)$. The +same-setting split has only one repeat on each side, whereas the cross-setting +comparisons pool both repeats per model on each side. This is a successful +end-to-end demonstration, but the tiny cohort saturated all methods and +therefore provides no evidence that the distributional representation is +better. The available two repeats also do not support the planned +$K\in\{1,2,4,8\}$ ablation. + +\begin{table}[htbp] +\centering +\caption{Distributional pilot Top-1 (MRR was also 1.000 in every entry).} +\label{tab:demo-distributional} +\begin{tabular}{lrrr} +\toprule +Query comparison & Single cosine & Mean cosine & Exact RBF-MMD \\ +\midrule +Same $(0.2,0.8)$ & 1.000 & 1.000 & 1.000 \\ +Cross $(0.2,0.8)\rightarrow(0.3,0.8)$ & 1.000 & 1.000 & 1.000 \\ +Cross $(0.2,0.8)\rightarrow(0.3,0.9)$ & 1.000 & 1.000 & 1.000 \\ +\bottomrule +\end{tabular} +\end{table} + +This complete saturation is itself a useful demo diagnostic: a larger and more +difficult cohort is required before representation differences can be +estimated. It would be inappropriate to select a method from these pilot +numbers. + +\subsection{Binary verification and tree-aware demo} + +An exact-only linear verification probe was trained on one $T=0.2,p=0.8$ +repeat and tested on the other, in both directions, over 95 common models. +Testing retained all 8,930 negative pairs per direction, giving a natural +positive prevalence of $1/95$. Results are in +Table~\ref{tab:demo-verification}. + +\begin{table}[ht] +\centering +\caption{Exploratory exact-only binary verification at natural prevalence.} +\label{tab:demo-verification} +\begin{tabular}{lrrrrrr} +\toprule +Test repeat & PR-AUC & ROC-AUC & Precision & Recall & Specificity & MCC \\ +\midrule +1 & 0.334 & 0.832 & 0.049 & 0.726 & 0.851 & 0.163 \\ +2 & 0.373 & 0.869 & 0.045 & 0.747 & 0.831 & 0.155 \\ +\bottomrule +\end{tabular} +\end{table} + +Despite the apparently strong ROC-AUC, the probe produced 1,329 and 1,511 +false positives in the two directions, respectively. The resulting precision +near 5\% demonstrates why the primary verification report emphasizes PR-AUC, +MCC, specificity, and false positives per query rather than balanced accuracy +alone. + +The tree-aware cosine demo used repeat 1 at $(0.2,0.8)$ as the gallery and 95 +common models. Exact Top-1 was 0.526 for the same-setting repeat-2 query and +0.379 for the $(0.3,0.8)$ repeat-2 query. All 104 combined errors were between +models disconnected in the exported relation graph, so within-one-hop, +within-two-hop, and same-component accuracy did not exceed exact accuracy. +Mean cosine distance was 0.445 versus 0.929 for exact and disconnected pairs +in the same-setting comparison, and 0.566 versus 0.929 in the cross-setting +comparison. No direct or indirect related pair occurred within this +availability-selected cohort, so lineage-error severity could not be +demonstrated. + +Figure~\ref{fig:demo-verification-tree} emphasizes two practical limitations. +First, ROC-AUC obscures the false-positive burden when only one of 95 candidate +pairs is positive. Second, the tree-aware demo separates exact and disconnected +pairs but cannot validate the intended one-hop and indirect strata because no +such pair occurred in the common cohort. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\linewidth] + {docs/figures/small_scale_section_6_11/verification_tree_diagnostics.png} +\caption{Verification and tree-aware diagnostics. Panels A--B show binary +metrics and false positives per query at natural prevalence. Panel C compares +mean cosine distance for exact and disconnected pairs and annotates exact +retrieval accuracy.} +\label{fig:demo-verification-tree} +\end{figure} + +\subsection{Validity-check demo and deferred items} + +As a limited gallery check, a fixed $T=0$ repeat-1 gallery was compared with +the multi-setting pooled gallery on the same six query settings. Equal-cell +mixed Top-1 with the fixed gallery was 0.403 for cosine, 0.310 for linear SVM, +and 0.341 for RBF SVM over 86 common models. The corresponding pooled-gallery +values were 0.621, 0.722, and 0.688 over 84 common models. Because the cohorts +and number of training examples differ, this is a diagnostic rather than a +paired method comparison; it merely confirms that gallery construction is +consequential. + +The remaining Section 10--11 analyses were deferred. Two temperatures and one +held-out repeat are inadequate for the planned mixed-effects degradation model. +Alternative projection dimensions/seeds, encoder comparisons, reliable +encoder-token truncation counts, $K=4$ and $K=8$, lineage-only verification, +and related-pair tree strata are not supported by the currently complete +artifacts. Leave-one-setting-out and subgroup analyses are feasible later but +were omitted to keep this demonstration deliberately small. + +\subsection{Demo conclusions and primary-study handoff} + +The demonstration supports the following limited conclusions: + +\begin{itemize} + \item The exact-classification and bounded-deployment pipelines operate on + disjoint generation repeats and produce aligned per-query scores for + all three classifiers. + \item Within this demo, the linear SVM had the highest mixed Top-1 at both + temperature bounds, and its paired advantage over cosine was positive. + \item Robustness was setting-dependent: $(0.3,1.0)$ was substantially harder + than the other five cells for every method. + \item Natural-prevalence verification exposed a large operational + false-positive burden that ROC-AUC alone would conceal. + \item The distributional and tree-aware code paths run, but the present data + are respectively too easy and too sparse in related pairs to compare + the intended methods. + \item Gallery construction materially changes the diagnostic accuracies, so + the primary report must keep gallery definitions and training budgets + fixed in paired comparisons. +\end{itemize} + +Before a final primary claim is made, the study still requires recovery to at +least the preregistered 80-model strict common cohort, a duplicate/independence +audit of the two existing rounds, and the frozen paired evaluation on repeat 2. +The current two-round design supports the exact-model cosine/SVM comparison. +It does not yet support a multi-sample advantage claim: a fair $K=2$ test needs +two independent reference responses and two independent query responses per +prompt, while $K=4$ and $K=8$ require additional generation. The small-scale +numbers remain exploratory until rerun on the frozen primary cohort. + +Machine-readable outputs are stored under +\path{out/small_scale_section_6_11}. The exact-classification, verification, +distributional, fixed-gallery, and tree-aware subdirectories contain the +selected cohorts, per-query predictions where applicable, aggregate CSV files, +and JSON summaries. The three report figures are regenerated without plotting +library dependencies using: + +\begin{lstlisting}[language=bash] +python scripts/plot_small_scale_section_6_11_demo.py +\end{lstlisting} + +\clearpage + +\section{Execution and resource plan} + +The completed first backfill sequence used +\path{scripts/resume_primary_robustness_grid.sh}, the exact validated-100 +cohort, and GPUs 0, 1, 2, and 4. It preserved successful historical artifacts +and queued only missing or failed model--cell--round keys. The sequence ended +on August 5 with 72 terminal failures. A failure-specific pass using +\path{scripts/recover_primary_robustness_failures_3090.sh} was launched on the +current A100 host on August 13 using GPUs 2--5. It writes to +\path{out/primary_robustness_recovery_20260810} and preserves the original +artifacts. The frozen counts in this report must be refreshed after it exits. + +Twenty-four affected models lack only one slot. Restoring any 21 of these +models across their missing cells is the minimum theoretical route from 59 to +the preregistered 80-model strict common cohort. Full 100-model completion +remains the target, but this prioritized recovery makes the analysis gate +explicit. + +The expected completion schedule below is measured from the August 5 status +snapshot and assumes continued access to the existing GPUs. It separates the +minimum analysis gate from optional multi-sample generation so that feasibility +is visible rather than implied. + +\begin{table}[htbp] +\centering +\small +\caption{Expected completion schedule from the August 5 status snapshot.} +\label{tab:completion-schedule} +\begin{tabular}{p{0.38\linewidth}p{0.20\linewidth}p{0.32\linewidth}} +\toprule +Milestone & Expected time & Exit condition \\ +\midrule +Diagnose 72 terminal failures & 1 working day & Failure-specific repair list \\ +Targeted recovery and audit & 2--3 working days & Strict common $N\geq80$ and independent rounds \\ +Frozen cosine/linear/RBF evaluation & 1 working day & Paired tables and 95\% model-cluster CIs \\ +Report integration and review & 1 working day & Primary report ready for supervisor review \\ +Optional fair $K=2$ RBF-MMD generation & 3--5 additional working days & Two independent samples on both sides \\ +\bottomrule +\end{tabular} +\end{table} + +Therefore, the controlled cosine/SVM primary update is expected within five to +six working days if recovery reaches $N\geq80$. The multi-sample proposed-method +claim has a separate, later completion date and is not placed on the critical +path for the classifier comparison. + +Transient failures are retried and recorded. Progress reports include planned, +successful, failed, and missing models plus failure-status counts. Missing +cells are never represented as zero accuracy and curves are not interpolated +through absent cells. + +\section{Reproducibility and provenance} + +The experiment will preserve: + +\begin{itemize} + \item exact cohort file and SHA-256 hash; + \item prompt dataset and seed; + \item model ID and available model metadata; + \item temperature, top-$p$, sampling flag, generation seed, and token limit; + \item response files and independent repeat suffixes; + \item sentence encoder, DNA dimension, projection method, and projection seed; + \item run manifests, status journals, failure summaries, predictions, and + machine-readable metric tables; and + \item the evaluation code and report version used to produce final figures. +\end{itemize} + +For clarity, the August 5 completion snapshot has the following generation-seed +provenance: + +\begin{table}[ht] +\centering +\caption{Generation-seed provenance among the 2,528 successful artifacts in +the pre-recovery snapshot.} +\begin{tabular}{lr} +\toprule +Provenance & Successful artifacts \\ +\midrule +Explicit generation seed recorded & 572 \\ +Historical manifest without a separate recorded generation seed & 1,956 \\ +\midrule +Total artifact coverage & 2,528 \\ +\bottomrule +\end{tabular} +\end{table} + +This table answers a counting issue directly: the 2,528 numerator does not +exclude unknown-seed history. Any seed-clean analysis must construct and report +its own denominator instead of reusing 2,600. + +The unsuffixed historical gallery will not be used for the primary causal +interpretation because its temperature and top-$p$ were not recorded. It may be +retained as a clearly labelled legacy reference-sensitivity analysis. + +\section{Planned results} + +\subsection{Cohort and completion table} + +\begin{table}[ht] +\centering +\caption{Frozen August 5 pre-recovery completion across both planned rounds.} +\begin{tabular}{lrrr} +\toprule +Condition & Required & Successful & Remaining \\ +\midrule +$T=0$ & 200 & 200 & 0 \\ +$T=0.2$, all $p$ & 600 & 597 & 3 \\ +$T=0.3$, all $p$ & 600 & 573 & 27 \\ +$T=0.5$, all $p$ & 600 & 570 & 30 \\ +$T=0.7$, all $p$ & 600 & 588 & 12 \\ +\midrule +Total & 2,600 & 2,528 & 72 \\ +\bottomrule +\end{tabular} +\end{table} + +The strict common cohort across all 26 runs is 59. Setting-specific paired +cohorts are reported in Table~\ref{tab:paired-cohorts}; therefore 97.2\% slot +completion must not be described as a 97-model complete-case cohort. + +\subsection{Per-setting exact classification} + +\begin{longtable}{ccrrrr} +\caption{Available held-out exact-model Top-1. Starred entries are exploratory +84-model results, not final validated-100 estimates.}\label{tab:per-setting}\\ +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ +\midrule +\endfirsthead +\toprule +$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ +\midrule +\endhead +0.0 & N/A & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.2 & 0.8 & \demovalue{0.643} & \demovalue{0.821} & \demovalue{0.702} & \todoresult \\ +0.2 & 0.9 & \demovalue{0.726} & \demovalue{0.845} & \demovalue{0.762} & \todoresult \\ +0.2 & 1.0 & \demovalue{0.595} & \demovalue{0.643} & \demovalue{0.643} & \todoresult \\ +0.3 & 0.8 & \demovalue{0.774} & \demovalue{0.869} & \demovalue{0.857} & \todoresult \\ +0.3 & 0.9 & \demovalue{0.690} & \demovalue{0.786} & \demovalue{0.762} & \todoresult \\ +0.3 & 1.0 & \demovalue{0.298} & \demovalue{0.369} & \demovalue{0.405} & \todoresult \\ +0.5 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.5 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ +0.7 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{longtable} + +\noindent\textsuperscript{*}Repeat 1 was used for pooled training/reference +construction and repeat 2 for testing on the same 84 availability-selected +models. The Proposed column remains pending: a same-query $K=1$ RBF-MMD check +is feasible, but a multi-sample claim requires additional matched responses. + +\subsection{Bounded unknown-temperature deployment} + +\begin{table}[ht] +\centering +\caption{Temperature-agnostic deployment results. The classifier is not given +temperature or top-$p$.} +\begin{tabular}{clrrr} +\toprule +$\tau$ & Method & Mixed accuracy & Worst setting & MRR \\ +\midrule +0.2 & Original cosine & \demovalue{0.596} & \demovalue{0.562} & \demovalue{0.672} \\ + & Linear SVM & \demovalue{0.682} & \demovalue{0.652} & \demovalue{0.744} \\ + & RBF SVM & \demovalue{0.629} & \demovalue{0.584} & \demovalue{0.710} \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.3 & Original cosine & \demovalue{0.621} & \demovalue{0.298} & \demovalue{0.698} \\ + & Linear SVM & \demovalue{0.722} & \demovalue{0.369} & \demovalue{0.777} \\ + & RBF SVM & \demovalue{0.688} & \demovalue{0.405} & \demovalue{0.754} \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.5 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\addlinespace +0.7 & Original cosine & \todoresult & \todoresult & \todoresult \\ + & Linear SVM & \todoresult & \todoresult & \todoresult \\ + & RBF SVM & \todoresult & \todoresult & \todoresult \\ + & Proposed & \todoresult & \todoresult & \todoresult \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Planned figures} + +The final report will contain: + +\begin{enumerate} + \item exact Top-1 versus temperature, faceted by top-$p$, with one curve per + representation/classifier and 95\% confidence bands; + \item accuracy loss relative to $T=0$ and estimated degradation slopes; + \item mixed and worst-setting accuracy versus temperature bound $\tau$; + \item stacked tree-aware Top-1 error severity versus temperature; and + \item accuracy versus response-sample/token budget for the mean and + distributional representations. +\end{enumerate} + +\section{Planned interpretation} + +The final conclusions will be limited to what the completed protocol supports: + +\begin{itemize} + \item the effect of query-side decoding conditions relative to explicit, + independently generated galleries; + \item classifier mitigation on the same exact-model task; + \item representation gains under matched budgets; and + \item expected and worst observed performance when decoding parameters are + hidden but temperature is bounded. +\end{itemize} + +No lineage-robustness conclusion will be based on aggregate positives dominated +by same-model pairs. No method will be declared superior from a point estimate +alone, and no causal temperature conclusion will use the historical +unknown-setting gallery. + +\appendix + +\section{Execution commands} + +Build or verify the fixed cohort and refresh the live grid: + +\begin{lstlisting}[language=bash] +python3 scripts/build_validated100_config.py \ + --manifest out/top_p_control_t05_validated100_20260719/\ +top_p_control_t05_t05_p06_r2/manifest.json \ + --output configs/rand_chinese_validated100_latest.jsonl +python3 scripts/build_primary_run_grid.py +\end{lstlisting} + +The completed missing/failed backfill sequence was launched with: + +\begin{lstlisting}[language=bash] +env GPUS=0,1,2,4 MAX_CONCURRENT_GPUS=4 \ + bash scripts/resume_primary_robustness_grid.sh +\end{lstlisting} + +After targeted recovery and the independence audit, run the exact-model and +tree-aware analyses: + +\begin{lstlisting}[language=bash] +bash scripts/run_primary_analysis.sh +\end{lstlisting} + +Run the implemented formal $K=1$ RFFTrace diagnostic after the common cohort +reaches the minimum gate: + +\begin{lstlisting}[language=bash] +env ENCODER_DEVICE=cuda:0 bash scripts/run_primary_rfftrace_k1.sh +\end{lstlisting} + +The same Python entry point supports a future valid $K=2$ experiment by passing +\method{--reference-repeats 1,3 --query-repeats 2,4 +--samples-per-side 2} after independent rounds 3 and 4 exist. It must not be +run by reusing the same response twice. + +The primary analysis outputs are written beneath +\path{out/primary_robustness_100_analysis}. The exact-model evaluator produces +per-setting metrics, per-query predictions, bounded-deployment tables, cohort +metadata, and JSON summaries. + +\section{Implementation map} + +\begin{longtable}{p{0.34\linewidth}p{0.58\linewidth}} +\toprule +File & Purpose \\ +\midrule +\endfirsthead +\toprule +File & Purpose \\ +\midrule +\endhead +\path{scripts/run_primary_robustness_experiment.sh} + & Launch the 100-model, 13-condition, two-round extraction sweep. \\ +\path{scripts/resume_primary_robustness_grid.sh} + & Resume missing and failed validated-100 grid cells without regenerating + successful artifacts. \\ +\path{scripts/build_primary_run_grid.py} + & Produce slot-completion and repeat-level cohort audits. \\ +\path{scripts/run_primary_analysis.sh} + & Construct the fixed train/test repeat split and run primary analyses. \\ +\path{scripts/evaluate_exact_dna_classification.py} + & Compare cosine centroids and multiclass SVMs on exact identity; produce + bounded unknown-temperature results and confidence intervals. \\ +\path{scripts/analyze_tree_aware_errors.py} + & Produce hierarchical retrieval and tree-distance error summaries. \\ +\path{scripts/evaluate_dna_probe_sweep.py} + & Run legacy and separated binary verification tasks using all test + negatives by default. \\ +\path{scripts/run_rbf_mmd_pilot.py} + & Legacy character-hashing pilot for single-sample, mean-cosine, and exact + RBF-MMD retrieval; not the formal DistDNA implementation. \\ +\path{src/llm_dna/distributional.py} + & Implement exact biased RBF-MMD, shared random Fourier features, prompt-wise + RFFTrace vectors, and optional compact Gaussian projection. \\ +\path{scripts/run_rfftrace_experiment.py} + & Run formal MPNet-based, calibration-separated, matched-budget exact-MMD, + RFFTrace, and compact-DistDNA retrieval and export reusable vectors. \\ +\path{scripts/run_primary_rfftrace_k1.sh} + & Freeze the primary-cohort $K=1$ same/cross-setting diagnostic command. \\ +\path{scripts/evaluate_rfftrace_svm.py} + & Evaluate exported RFFTrace or compact-DistDNA vectors with held-out linear + and RBF SVMs while enforcing disjoint vector keys. \\ +\path{tests/test_distributional.py} + & Check kernel geometry, MMD/RFF convergence, scaling, projection, seeds, and + package exports. \\ +\path{tests/test_rbf_mmd_pilot.py} + & Preserve legacy pilot MMD, repeat-split, and all-record prompt-intersection + regression checks. \\ +\path{tests/test_rfftrace_experiment.py} + & Check response discovery, actual model IDs, common prompts, required + repeats, and disjoint prompt splitting. \\ +\path{tests/test_rfftrace_svm.py} + & Check SVM split leakage rejection and held-out multiclass evaluation. \\ +\path{scripts/export_hf_model_tree.py} + & Export direct parent/base edges and connected components. \\ +\path{scripts/report_sweep_progress.py} + & Report completion, missing models, and failure-status counts. \\ +\bottomrule +\end{longtable} + +\end{document} diff --git a/scripts/evaluate_rfftrace_svm.py b/scripts/evaluate_rfftrace_svm.py new file mode 100755 index 0000000..39150eb --- /dev/null +++ b/scripts/evaluate_rfftrace_svm.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python3 +"""Evaluate exported RFFTrace/DistDNA vectors with held-out multiclass SVMs. + +The caller must choose disjoint train and test matrix keys from +``rfftrace_vectors.npz``. At least two independent training vectors per model +are required by default; this prevents a one-example-per-class probe from being +mistaken for a validated DistDNA+SVM result. +""" + +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path + +import numpy as np + + +def load_matrices( + path: Path, train_keys: list[str], test_keys: list[str] +) -> tuple[list[str], dict[str, np.ndarray]]: + if set(train_keys) & set(test_keys): + raise ValueError("Train and test vector keys must be disjoint") + with np.load(path) as payload: + if "models" not in payload: + raise ValueError(f"{path} has no models array") + models = [str(value) for value in payload["models"].tolist()] + matrices: dict[str, np.ndarray] = {} + for key in [*train_keys, *test_keys]: + if key not in payload: + raise ValueError(f"Vector key {key!r} is absent from {path}") + matrix = np.asarray(payload[key], dtype=np.float32) + if matrix.ndim != 2 or matrix.shape[0] != len(models): + raise ValueError( + f"{key!r} must have shape ({len(models)}, features), got {matrix.shape}" + ) + matrices[key] = matrix + dimensions = {matrix.shape[1] for matrix in matrices.values()} + if len(dimensions) != 1: + raise ValueError(f"Vector dimensions differ across selected keys: {dimensions}") + return models, matrices + + +def rank_from_scores(scores: np.ndarray, classes: np.ndarray, true_label: str) -> int: + values = np.asarray(scores) + if values.ndim == 0 and len(classes) == 2: + values = np.asarray([-float(values), float(values)]) + if values.shape != (len(classes),): + raise ValueError( + f"Expected one decision score per class, got {values.shape} for {len(classes)} classes" + ) + order = np.argsort(-values, kind="stable") + ranked = np.asarray(classes)[order] + return int(np.flatnonzero(ranked == true_label)[0]) + 1 + + +def evaluate_classifier( + name: str, + classifier: object, + models: list[str], + matrices: dict[str, np.ndarray], + train_keys: list[str], + test_keys: list[str], +) -> tuple[list[dict[str, object]], list[dict[str, object]]]: + from sklearn.pipeline import make_pipeline + from sklearn.preprocessing import StandardScaler + + train_x = np.concatenate([matrices[key] for key in train_keys], axis=0) + train_y = np.tile(np.asarray(models), len(train_keys)) + pipeline = make_pipeline(StandardScaler(), classifier) + pipeline.fit(train_x, train_y) + estimator = pipeline[-1] + + predictions: list[dict[str, object]] = [] + summaries: list[dict[str, object]] = [] + for key in test_keys: + matrix = matrices[key] + predicted = pipeline.predict(matrix) + scores = pipeline.decision_function(matrix) + ranks = [ + rank_from_scores(scores[index], estimator.classes_, model) + for index, model in enumerate(models) + ] + for index, model in enumerate(models): + predictions.append( + { + "classifier": name, + "vector_key": key, + "model_id": model, + "predicted_model": str(predicted[index]), + "rank": ranks[index], + "correct": int(predicted[index] == model), + } + ) + summaries.append( + { + "classifier": name, + "vector_key": key, + "model_count": len(models), + "train_vectors_per_model": len(train_keys), + "top1": float(np.mean(predicted == np.asarray(models))), + "top3": float(np.mean(np.asarray(ranks) <= 3)), + "top5": float(np.mean(np.asarray(ranks) <= 5)), + "mrr": float(np.mean(1.0 / np.asarray(ranks))), + } + ) + return summaries, predictions + + +def write_csv(path: Path, rows: list[dict[str, object]]) -> None: + fields = list(rows[0]) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields, lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Held-out linear/RBF SVM evaluation for exported DistDNA vectors" + ) + parser.add_argument("--vectors", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--train-key", action="append", required=True) + parser.add_argument("--test-key", action="append", required=True) + parser.add_argument("--minimum-train-vectors-per-class", type=int, default=2) + parser.add_argument("--linear-c", type=float, default=1.0) + parser.add_argument("--rbf-c", type=float, default=1.0) + parser.add_argument("--rbf-gamma", type=parse_gamma, default="scale") + parser.add_argument("--seed", type=int, default=42) + return parser.parse_args() + + +def parse_gamma(raw: str) -> str | float: + if raw in {"scale", "auto"}: + return raw + try: + value = float(raw) + except ValueError as exc: + raise argparse.ArgumentTypeError("RBF gamma must be scale, auto, or positive") from exc + if not np.isfinite(value) or value <= 0: + raise argparse.ArgumentTypeError("RBF gamma must be scale, auto, or positive") + return value + + +def main() -> int: + args = parse_args() + if len(args.train_key) < args.minimum_train_vectors_per_class: + raise SystemExit( + f"Need at least {args.minimum_train_vectors_per_class} independent train " + f"vectors per class, but only {len(args.train_key)} --train-key values were given" + ) + if args.linear_c <= 0 or args.rbf_c <= 0: + raise SystemExit("SVM C values must be positive") + + from sklearn.svm import LinearSVC, SVC + + models, matrices = load_matrices(args.vectors, args.train_key, args.test_key) + classifiers = [ + ("linear_svm", LinearSVC(C=args.linear_c, dual="auto", random_state=args.seed)), + ( + "rbf_svm", + SVC( + C=args.rbf_c, + gamma=args.rbf_gamma, + kernel="rbf", + decision_function_shape="ovr", + ), + ), + ] + summaries: list[dict[str, object]] = [] + predictions: list[dict[str, object]] = [] + for name, classifier in classifiers: + method_summary, method_predictions = evaluate_classifier( + name, + classifier, + models, + matrices, + args.train_key, + args.test_key, + ) + summaries.extend(method_summary) + predictions.extend(method_predictions) + + args.output_dir.mkdir(parents=True, exist_ok=True) + write_csv(args.output_dir / "summary.csv", summaries) + write_csv(args.output_dir / "predictions.csv", predictions) + (args.output_dir / "summary.json").write_text( + json.dumps(summaries, indent=2, ensure_ascii=False), encoding="utf-8" + ) + (args.output_dir / "config.json").write_text( + json.dumps( + { + "vectors": str(args.vectors), + "train_keys": args.train_key, + "test_keys": args.test_key, + "minimum_train_vectors_per_class": args.minimum_train_vectors_per_class, + "linear_c": args.linear_c, + "rbf_c": args.rbf_c, + "rbf_gamma": args.rbf_gamma, + "seed": args.seed, + }, + indent=2, + ), + encoding="utf-8", + ) + print(json.dumps(summaries, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_primary_rfftrace_k1.sh b/scripts/run_primary_rfftrace_k1.sh new file mode 100755 index 0000000..8076021 --- /dev/null +++ b/scripts/run_primary_rfftrace_k1.sh @@ -0,0 +1,52 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Formal RFFTrace/DistDNA K=1 diagnostic on the validated-100 robustness grid. +# K=1 is an implementation and point-kernel comparison only; it is not evidence +# for a multi-sample advantage. A valid K=2 run needs independent rounds 1/3 +# for reference and 2/4 for query. + +PYTHON_BIN="${PYTHON_BIN:-python3}" +ENCODER_DEVICE="${ENCODER_DEVICE:-cpu}" +OUTPUT_DIR="${OUTPUT_DIR:-out/rfftrace_primary_k1}" +EMBEDDING_CACHE_DIR="${EMBEDDING_CACHE_DIR:-out/response_embedding_cache}" +MINIMUM_MODELS="${MINIMUM_MODELS:-80}" +RFF_DIMENSION="${RFF_DIMENSION:-1024}" +COMPACT_DIMENSION="${COMPACT_DIMENSION:-128}" +SEED="${SEED:-42}" + +normalize_args=() +if [[ "${NORMALIZE_EMBEDDINGS:-0}" == "1" ]]; then + normalize_args+=(--normalize-embeddings) +fi + +"$PYTHON_BIN" scripts/run_rfftrace_experiment.py \ + --data-dir out/primary_robustness_validated100_20260730/rand_chinese \ + --data-dir out/primary_robustness_recovery_20260810 \ + --output-dir "$OUTPUT_DIR" \ + --cohort-file configs/rand_chinese_validated100_latest.jsonl \ + --reference-setting 0.2:0.8 \ + --query-setting 0.0:1.0 \ + --query-setting 0.2:0.8 \ + --query-setting 0.2:0.9 \ + --query-setting 0.2:1.0 \ + --query-setting 0.3:0.8 \ + --query-setting 0.3:0.9 \ + --query-setting 0.3:1.0 \ + --query-setting 0.5:0.8 \ + --query-setting 0.5:0.9 \ + --query-setting 0.5:1.0 \ + --query-setting 0.7:0.8 \ + --query-setting 0.7:0.9 \ + --query-setting 0.7:1.0 \ + --reference-repeats 1 \ + --query-repeats 2 \ + --samples-per-side 1 \ + --minimum-models "$MINIMUM_MODELS" \ + --encoder all-mpnet-base-v2 \ + --encoder-device "$ENCODER_DEVICE" \ + --embedding-cache-dir "$EMBEDDING_CACHE_DIR" \ + --rff-dimension "$RFF_DIMENSION" \ + --compact-dimension "$COMPACT_DIMENSION" \ + --seed "$SEED" \ + "${normalize_args[@]}" diff --git a/scripts/run_rbf_mmd_pilot.py b/scripts/run_rbf_mmd_pilot.py index f920df4..57393cb 100644 --- a/scripts/run_rbf_mmd_pilot.py +++ b/scripts/run_rbf_mmd_pilot.py @@ -122,25 +122,20 @@ def select_models(records: dict[tuple[str, str, int], RunRecord], settings: list def select_prompts(records: dict[tuple[str, str, int], RunRecord], selected_models: list[str], settings: list[SettingSpec], prompt_limit: int) -> list[str]: - prompt_sets: list[list[str]] = [] - for setting in settings: - reference = None - for model_name in selected_models: - for repeat in sorted({repeat for (m, code, repeat) in records if m == model_name and code == setting.code}): - reference = records[(model_name, setting.code, repeat)] - break - if reference is not None: - break - if reference is None: - continue - prompt_sets.append(reference.prompts) - - if not prompt_sets: + selected_codes = {setting.code for setting in settings} + selected_model_set = set(selected_models) + prompt_lists = [ + record.prompts + for (model_name, code, _repeat), record in sorted(records.items()) + if model_name in selected_model_set and code in selected_codes + ] + if not prompt_lists: return [] - common = list(prompt_sets[0]) - for prompts in prompt_sets[1:]: - common = [prompt for prompt in common if prompt in prompts] + common_set = set(prompt_lists[0]) + for prompts in prompt_lists[1:]: + common_set.intersection_update(prompts) + common = [prompt for prompt in prompt_lists[0] if prompt in common_set] return common if prompt_limit <= 0 else common[:prompt_limit] diff --git a/scripts/run_rfftrace_experiment.py b/scripts/run_rfftrace_experiment.py new file mode 100755 index 0000000..c186fa2 --- /dev/null +++ b/scripts/run_rfftrace_experiment.py @@ -0,0 +1,596 @@ +#!/usr/bin/env python3 +"""Evaluate DistDNA/RFFTrace with matched-sample retrieval baselines. + +This is the executable counterpart of ``docs/main (2).pdf``. It uses a real +sentence encoder, calibrates the RBF bandwidth on disjoint reference prompts, +shares one RFF map (and optional JL projection) across every model/setting, and +compares single cosine, matched-K mean cosine, exact RBF-MMD, and RFFTrace on +identical held-out prompts and response samples. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import re +import time +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Iterable + +import numpy as np + +from llm_dna.distributional import ( + RandomFourierMap, + compact_rfftrace_vector, + gaussian_projection_matrix, + median_bandwidth, + prompt_averaged_mmd2, + rfftrace_vector, +) + + +@dataclass(frozen=True, order=True) +class Setting: + temperature: float + top_p: float + + @property + def code(self) -> str: + return f"t{int(round(self.temperature * 10)):02d}_p{int(round(self.top_p * 10)):02d}" + + +@dataclass(frozen=True) +class ResponseRun: + model_id: str + setting: Setting + repeat: int + path: Path + prompts: tuple[str, ...] + responses: tuple[str, ...] + + +RUN_SUFFIX = re.compile(r"_t(?P\d{2})_p(?P

\d{2})_r(?P\d+)$") + + +def parse_setting(raw: str) -> Setting: + try: + temperature, top_p = raw.split(":", maxsplit=1) + return Setting(float(temperature), float(top_p)) + except (TypeError, ValueError) as exc: + raise argparse.ArgumentTypeError( + f"Invalid setting {raw!r}; expected temperature:top_p" + ) from exc + + +def parse_repeats(raw: str) -> tuple[int, ...]: + try: + repeats = tuple(int(part.strip()) for part in raw.split(",") if part.strip()) + except ValueError as exc: + raise argparse.ArgumentTypeError("Repeats must be comma-separated integers") from exc + if not repeats or len(set(repeats)) != len(repeats): + raise argparse.ArgumentTypeError("At least one unique repeat is required") + return repeats + + +def parse_run_path(path: Path) -> tuple[Setting, int] | None: + match = RUN_SUFFIX.search(path.parent.name) + if match is None: + return None + return ( + Setting(int(match.group("t")) / 10.0, int(match.group("p")) / 10.0), + int(match.group("repeat")), + ) + + +def load_response_run(path: Path) -> ResponseRun: + parsed = parse_run_path(path) + if parsed is None: + raise ValueError(f"Run directory has no decoding suffix: {path.parent}") + payload = json.loads(path.read_text(encoding="utf-8")) + items = payload.get("items", []) + prompts = tuple(str(item.get("prompt", "")) for item in items) + responses = tuple(str(item.get("response", "")) for item in items) + if not prompts or len(prompts) != len(responses): + raise ValueError(f"Invalid prompt/response items in {path}") + if any(not prompt.strip() for prompt in prompts): + raise ValueError(f"Empty prompt in {path}") + if any(not response.strip() for response in responses): + raise ValueError(f"Empty response in {path}") + model_id = str(payload.get("model", "")).strip() + if not model_id: + # Old payloads may omit the model field; the suffix is unambiguous but + # the sanitized directory name is then retained as the identity label. + model_id = RUN_SUFFIX.sub("", path.parent.name) + setting, repeat = parsed + return ResponseRun(model_id, setting, repeat, path, prompts, responses) + + +def discover_runs( + data_dirs: Iterable[Path], + settings: set[Setting], +) -> dict[tuple[str, Setting, int], ResponseRun]: + records: dict[tuple[str, Setting, int], ResponseRun] = {} + mtimes: dict[tuple[str, Setting, int], float] = {} + for data_dir in data_dirs: + for path in sorted(data_dir.rglob("responses.json")): + parsed = parse_run_path(path) + if parsed is None or parsed[0] not in settings: + continue + try: + record = load_response_run(path) + except (OSError, ValueError, json.JSONDecodeError): + continue + key = (record.model_id, record.setting, record.repeat) + modified = path.stat().st_mtime + if key not in records or modified > mtimes[key]: + records[key] = record + mtimes[key] = modified + return records + + +def select_models( + records: dict[tuple[str, Setting, int], ResponseRun], + reference_setting: Setting, + query_settings: list[Setting], + reference_repeats: tuple[int, ...], + query_repeats: tuple[int, ...], + samples_per_side: int, + cohort_file: Path | None, + model_limit: int, +) -> list[str]: + required = [ + (reference_setting, reference_repeats[:samples_per_side]), + *[(setting, query_repeats[:samples_per_side]) for setting in query_settings], + ] + available = {model for model, _setting, _repeat in records} + models = [ + model + for model in available + if all( + all((model, setting, repeat) in records for repeat in repeats) + for setting, repeats in required + ) + ] + if cohort_file is not None: + order = [ + str(json.loads(line)["model_id"]) + for line in cohort_file.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + selected = [model for model in order if model in set(models)] + else: + selected = sorted(models) + return selected[:model_limit] if model_limit > 0 else selected + + +def common_prompts(runs: Iterable[ResponseRun]) -> list[str]: + records = list(runs) + if not records: + return [] + common = set(records[0].prompts) + for record in records[1:]: + common.intersection_update(record.prompts) + return [prompt for prompt in records[0].prompts if prompt in common] + + +def split_prompts( + prompts: list[str], calibration_ratio: float, seed: int +) -> tuple[list[str], list[str]]: + if len(prompts) < 2: + raise ValueError("At least two common prompts are required") + if not 0.0 < calibration_ratio < 1.0: + raise ValueError("calibration_ratio must lie strictly between zero and one") + rng = np.random.default_rng(seed) + order = rng.permutation(len(prompts)) + count = min(len(prompts) - 1, max(1, round(len(prompts) * calibration_ratio))) + calibration_indices = set(int(index) for index in order[:count]) + calibration = [prompt for index, prompt in enumerate(prompts) if index in calibration_indices] + evaluation = [prompt for index, prompt in enumerate(prompts) if index not in calibration_indices] + return calibration, evaluation + + +def _cache_key(record: ResponseRun, encoder_name: str, normalize: bool) -> str: + digest = hashlib.sha256() + digest.update(encoder_name.encode("utf-8")) + digest.update(str(normalize).encode("ascii")) + for prompt, response in zip(record.prompts, record.responses): + digest.update(prompt.encode("utf-8")) + digest.update(b"\0") + digest.update(response.encode("utf-8")) + digest.update(b"\0") + return digest.hexdigest() + + +class SentenceEmbeddingStore: + def __init__( + self, + encoder_name: str, + device: str, + batch_size: int, + normalize: bool, + cache_dir: Path, + ) -> None: + self.encoder_name = encoder_name + self.device = device + self.batch_size = batch_size + self.normalize = normalize + self.cache_dir = cache_dir + self.cache_dir.mkdir(parents=True, exist_ok=True) + self._encoder = None + + def encode(self, record: ResponseRun) -> np.ndarray: + cache_path = self.cache_dir / f"{_cache_key(record, self.encoder_name, self.normalize)}.npz" + if cache_path.exists(): + with np.load(cache_path) as payload: + return np.asarray(payload["embeddings"], dtype=np.float32) + if self._encoder is None: + from sentence_transformers import SentenceTransformer + + self._encoder = SentenceTransformer(self.encoder_name, device=self.device) + matrix = self._encoder.encode( + list(record.responses), + convert_to_numpy=True, + show_progress_bar=False, + batch_size=self.batch_size, + normalize_embeddings=self.normalize, + ) + matrix = np.asarray(matrix, dtype=np.float32) + temporary = cache_path.with_suffix(".tmp.npz") + np.savez_compressed(temporary, embeddings=matrix) + temporary.replace(cache_path) + return matrix + + +def prompt_samples( + model_id: str, + setting: Setting, + repeats: tuple[int, ...], + prompts: list[str], + records: dict[tuple[str, Setting, int], ResponseRun], + store: SentenceEmbeddingStore, +) -> np.ndarray: + per_repeat: list[np.ndarray] = [] + for repeat in repeats: + record = records[(model_id, setting, repeat)] + embeddings = store.encode(record) + prompt_to_index = {prompt: index for index, prompt in enumerate(record.prompts)} + per_repeat.append(np.stack([embeddings[prompt_to_index[prompt]] for prompt in prompts])) + # (repeats, prompts, features) -> (prompts, repeats, features) + return np.stack(per_repeat, axis=0).transpose(1, 0, 2).astype(np.float32) + + +def cosine_distance_matrix(query: np.ndarray, reference: np.ndarray) -> np.ndarray: + query = query / np.maximum(np.linalg.norm(query, axis=1, keepdims=True), 1e-12) + reference = reference / np.maximum(np.linalg.norm(reference, axis=1, keepdims=True), 1e-12) + return (1.0 - query @ reference.T).astype(np.float32) + + +def squared_distance_matrix(query: np.ndarray, reference: np.ndarray) -> np.ndarray: + squared = ( + np.sum(query * query, axis=1, keepdims=True) + + np.sum(reference * reference, axis=1, keepdims=True).T + - 2.0 * query @ reference.T + ) + return np.maximum(squared, 0.0).astype(np.float32) + + +def ranking_rows( + comparison: str, + method: str, + models: list[str], + distances: np.ndarray, +) -> tuple[dict[str, object], list[dict[str, object]]]: + ranks: list[int] = [] + predictions: list[dict[str, object]] = [] + for row, model_id in enumerate(models): + order = np.argsort(distances[row], kind="stable") + rank = int(np.flatnonzero(order == row)[0]) + 1 + ranks.append(rank) + predicted = models[int(order[0])] + predictions.append( + { + "comparison": comparison, + "method": method, + "model_id": model_id, + "predicted_model": predicted, + "rank": rank, + "correct": int(rank == 1), + "self_distance": float(distances[row, row]), + "nearest_distance": float(distances[row, order[0]]), + } + ) + metrics: dict[str, object] = { + "comparison": comparison, + "method": method, + "model_count": len(models), + "top1": float(np.mean([rank <= 1 for rank in ranks])), + "top3": float(np.mean([rank <= 3 for rank in ranks])), + "top5": float(np.mean([rank <= 5 for rank in ranks])), + "mrr": float(np.mean([1.0 / rank for rank in ranks])), + "mean_self_distance": float(np.mean(np.diag(distances))), + } + return metrics, predictions + + +def write_csv(path: Path, rows: list[dict[str, object]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + fields: list[str] = [] + for row in rows: + fields.extend(key for key in row if key not in fields) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields, lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Matched-budget exact RBF-MMD and DistDNA/RFFTrace evaluation" + ) + parser.add_argument("--data-dir", type=Path, action="append", required=True) + parser.add_argument("--output-dir", type=Path, default=Path("out/rfftrace_experiment")) + parser.add_argument("--reference-setting", type=parse_setting, default=parse_setting("0.2:0.8")) + parser.add_argument("--query-setting", type=parse_setting, action="append", required=True) + parser.add_argument("--reference-repeats", type=parse_repeats, default=parse_repeats("1")) + parser.add_argument("--query-repeats", type=parse_repeats, default=parse_repeats("2")) + parser.add_argument("--samples-per-side", type=int, default=1) + parser.add_argument("--cohort-file", type=Path) + parser.add_argument("--minimum-models", type=int, default=80) + parser.add_argument("--model-limit", type=int, default=0) + parser.add_argument("--prompt-limit", type=int, default=0) + parser.add_argument("--calibration-ratio", type=float, default=0.2) + parser.add_argument("--encoder", default="all-mpnet-base-v2") + parser.add_argument("--encoder-device", default="cpu") + parser.add_argument("--encoder-batch-size", type=int, default=32) + parser.add_argument("--normalize-embeddings", action="store_true") + parser.add_argument("--embedding-cache-dir", type=Path, default=Path("out/response_embedding_cache")) + parser.add_argument("--rff-dimension", type=int, default=1024) + parser.add_argument("--compact-dimension", type=int, default=128) + parser.add_argument("--bandwidth-multiplier", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=42) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + if args.samples_per_side <= 0: + raise SystemExit("--samples-per-side must be positive") + if len(args.reference_repeats) < args.samples_per_side: + raise SystemExit("Not enough --reference-repeats for --samples-per-side") + if len(args.query_repeats) < args.samples_per_side: + raise SystemExit("Not enough --query-repeats for --samples-per-side") + if args.rff_dimension <= 0 or args.compact_dimension < 0: + raise SystemExit("RFF dimension must be positive and compact dimension non-negative") + if not np.isfinite(args.bandwidth_multiplier) or args.bandwidth_multiplier <= 0: + raise SystemExit("--bandwidth-multiplier must be finite and positive") + + started = time.time() + settings = {args.reference_setting, *args.query_setting} + records = discover_runs(args.data_dir, settings) + models = select_models( + records, + args.reference_setting, + args.query_setting, + args.reference_repeats, + args.query_repeats, + args.samples_per_side, + args.cohort_file, + args.model_limit, + ) + if len(models) < args.minimum_models: + raise SystemExit( + f"Only {len(models)} models have the required runs; minimum is {args.minimum_models}" + ) + + selected_runs = [] + for model in models: + selected_runs.extend( + records[(model, args.reference_setting, repeat)] + for repeat in args.reference_repeats[: args.samples_per_side] + ) + for setting in args.query_setting: + selected_runs.extend( + records[(model, setting, repeat)] + for repeat in args.query_repeats[: args.samples_per_side] + ) + prompts = common_prompts(selected_runs) + if args.prompt_limit > 0: + prompts = prompts[: args.prompt_limit] + calibration_prompts, evaluation_prompts = split_prompts( + prompts, args.calibration_ratio, args.seed + ) + + store = SentenceEmbeddingStore( + args.encoder, + args.encoder_device, + args.encoder_batch_size, + args.normalize_embeddings, + args.embedding_cache_dir, + ) + reference_repeats = args.reference_repeats[: args.samples_per_side] + query_repeats = args.query_repeats[: args.samples_per_side] + + calibration = [] + for model in models: + samples = prompt_samples( + model, + args.reference_setting, + reference_repeats, + calibration_prompts, + records, + store, + ) + calibration.append(samples.reshape(-1, samples.shape[-1])) + bandwidth = median_bandwidth(np.concatenate(calibration), seed=args.seed) + bandwidth *= args.bandwidth_multiplier + if bandwidth <= 0: + raise SystemExit("Calibrated bandwidth is not positive") + + first_record = records[(models[0], args.reference_setting, reference_repeats[0])] + embedding_dimension = store.encode(first_record).shape[1] + feature_map = RandomFourierMap.sample( + embedding_dimension, args.rff_dimension, bandwidth, seed=args.seed + ) + raw_dimension = len(evaluation_prompts) * args.rff_dimension + projection = ( + gaussian_projection_matrix(raw_dimension, args.compact_dimension, seed=args.seed) + if args.compact_dimension > 0 + else None + ) + + reference_samples = { + model: prompt_samples( + model, + args.reference_setting, + reference_repeats, + evaluation_prompts, + records, + store, + ) + for model in models + } + reference_single = np.stack( + [reference_samples[model][:, 0, :].reshape(-1) for model in models] + ) + reference_mean = np.stack( + [reference_samples[model].mean(axis=1).reshape(-1) for model in models] + ) + reference_rff = np.stack( + [rfftrace_vector(reference_samples[model], feature_map) for model in models] + ) + reference_compact = ( + np.stack( + [ + compact_rfftrace_vector(reference_samples[model], feature_map, projection) + for model in models + ] + ) + if projection is not None + else None + ) + + summaries: list[dict[str, object]] = [] + predictions: list[dict[str, object]] = [] + vector_payload: dict[str, np.ndarray] = {"models": np.asarray(models)} + vector_payload[f"reference_{args.reference_setting.code}_rfftrace"] = reference_rff + if reference_compact is not None: + vector_payload[f"reference_{args.reference_setting.code}_compact"] = reference_compact + + for setting in args.query_setting: + comparison = f"{args.reference_setting.code}_to_{setting.code}_K{args.samples_per_side}" + query_samples = { + model: prompt_samples( + model, + setting, + query_repeats, + evaluation_prompts, + records, + store, + ) + for model in models + } + query_single = np.stack( + [query_samples[model][:, 0, :].reshape(-1) for model in models] + ) + query_mean = np.stack( + [query_samples[model].mean(axis=1).reshape(-1) for model in models] + ) + query_rff = np.stack( + [rfftrace_vector(query_samples[model], feature_map) for model in models] + ) + query_compact = ( + np.stack( + [ + compact_rfftrace_vector(query_samples[model], feature_map, projection) + for model in models + ] + ) + if projection is not None + else None + ) + + matrices: list[tuple[str, np.ndarray]] = [ + ("single_cosine", cosine_distance_matrix(query_single, reference_single)), + ("mean_cosine", cosine_distance_matrix(query_mean, reference_mean)), + ("rfftrace", squared_distance_matrix(query_rff, reference_rff)), + ] + exact = np.empty((len(models), len(models)), dtype=np.float32) + for row, query_model in enumerate(models): + for column, reference_model in enumerate(models): + exact[row, column] = prompt_averaged_mmd2( + query_samples[query_model], reference_samples[reference_model], bandwidth + ) + matrices.append(("exact_rbf_mmd", exact)) + if query_compact is not None and reference_compact is not None: + matrices.append( + ("compact_rfftrace", squared_distance_matrix(query_compact, reference_compact)) + ) + + for method, matrix in matrices: + metrics, method_predictions = ranking_rows(comparison, method, models, matrix) + metrics.update( + { + "samples_per_side": args.samples_per_side, + "prompt_count": len(evaluation_prompts), + "bandwidth": bandwidth, + "rff_dimension": args.rff_dimension, + "compact_dimension": args.compact_dimension, + } + ) + summaries.append(metrics) + predictions.extend(method_predictions) + vector_payload[f"query_{setting.code}_rfftrace"] = query_rff + if query_compact is not None: + vector_payload[f"query_{setting.code}_compact"] = query_compact + + args.output_dir.mkdir(parents=True, exist_ok=True) + write_csv(args.output_dir / "summary.csv", summaries) + write_csv(args.output_dir / "predictions.csv", predictions) + (args.output_dir / "summary.json").write_text( + json.dumps(summaries, indent=2, ensure_ascii=False), encoding="utf-8" + ) + (args.output_dir / "selected_models.txt").write_text( + "\n".join(models) + "\n", encoding="utf-8" + ) + (args.output_dir / "calibration_prompts.txt").write_text( + "\n".join(calibration_prompts) + "\n", encoding="utf-8" + ) + (args.output_dir / "evaluation_prompts.txt").write_text( + "\n".join(evaluation_prompts) + "\n", encoding="utf-8" + ) + np.savez_compressed(args.output_dir / "rfftrace_vectors.npz", **vector_payload) + config = { + "data_dirs": [str(path) for path in args.data_dir], + "reference_setting": asdict(args.reference_setting), + "query_settings": [asdict(setting) for setting in args.query_setting], + "reference_repeats": list(reference_repeats), + "query_repeats": list(query_repeats), + "samples_per_side": args.samples_per_side, + "model_count": len(models), + "calibration_prompt_count": len(calibration_prompts), + "evaluation_prompt_count": len(evaluation_prompts), + "encoder": args.encoder, + "normalize_embeddings": args.normalize_embeddings, + "embedding_dimension": embedding_dimension, + "bandwidth": bandwidth, + "bandwidth_multiplier": args.bandwidth_multiplier, + "rff_dimension": args.rff_dimension, + "compact_dimension": args.compact_dimension, + "seed": args.seed, + "elapsed_seconds": time.time() - started, + } + (args.output_dir / "config.json").write_text( + json.dumps(config, indent=2, ensure_ascii=False), encoding="utf-8" + ) + print(json.dumps({"output_dir": str(args.output_dir), **config}, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/llm_dna/__init__.py b/src/llm_dna/__init__.py index 8ac9e80..3da37ba 100644 --- a/src/llm_dna/__init__.py +++ b/src/llm_dna/__init__.py @@ -4,6 +4,7 @@ "__version__", "DNAExtractionConfig", "DNAExtractionResult", + "RandomFourierMap", "calc_dna", "calc_dna_parallel", "calc_dna_batch", @@ -15,6 +16,10 @@ def __getattr__(name: str): + if name == "RandomFourierMap": + from .distributional import RandomFourierMap + + return RandomFourierMap if name in { "DNAExtractionConfig", "DNAExtractionResult", diff --git a/src/llm_dna/distributional.py b/src/llm_dna/distributional.py new file mode 100644 index 0000000..d2cd924 --- /dev/null +++ b/src/llm_dna/distributional.py @@ -0,0 +1,269 @@ +"""Distributional LLM fingerprints based on RBF-MMD and random Fourier features. + +The implementation follows the RFFTrace construction in ``docs/main (2).pdf``: +responses are embedded per prompt, RFF kernel features are averaged over +independent generations, prompt means are concatenated with ``1/sqrt(t)`` +scaling, and an optional shared Gaussian projection produces a compact DNA. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Iterable, Optional + +import numpy as np + + +Array = np.ndarray + + +def _as_float_matrix(values: Array, name: str) -> Array: + matrix = np.asarray(values, dtype=np.float32) + if matrix.ndim != 2: + raise ValueError(f"{name} must have shape (samples, features), got {matrix.shape}") + if matrix.shape[0] == 0 or matrix.shape[1] == 0: + raise ValueError(f"{name} must be non-empty, got {matrix.shape}") + if not np.all(np.isfinite(matrix)): + raise ValueError(f"{name} contains non-finite values") + return matrix + + +def _as_prompt_samples(values: Array, name: str) -> Array: + samples = np.asarray(values, dtype=np.float32) + if samples.ndim != 3: + raise ValueError( + f"{name} must have shape (prompts, samples, features), got {samples.shape}" + ) + if any(size == 0 for size in samples.shape): + raise ValueError(f"{name} must be non-empty, got {samples.shape}") + if not np.all(np.isfinite(samples)): + raise ValueError(f"{name} contains non-finite values") + return samples + + +def pairwise_squared_distances(a: Array, b: Array) -> Array: + """Return all squared Euclidean distances between two row matrices.""" + + left = _as_float_matrix(a, "a") + right = _as_float_matrix(b, "b") + if left.shape[1] != right.shape[1]: + raise ValueError(f"Feature dimensions differ: {left.shape[1]} vs {right.shape[1]}") + squared = ( + np.sum(left * left, axis=1, keepdims=True) + + np.sum(right * right, axis=1, keepdims=True).T + - 2.0 * left @ right.T + ) + return np.maximum(squared, 0.0).astype(np.float32, copy=False) + + +def rbf_kernel( + a: Array, + b: Array, + bandwidths: float | Iterable[float], +) -> Array: + """Evaluate a single- or multi-scale RBF kernel. + + A sequence implements Eq. (9) by averaging equally weighted kernels. + """ + + raw = [bandwidths] if np.isscalar(bandwidths) else list(bandwidths) + sigmas = np.asarray(raw, dtype=np.float64) + if sigmas.ndim != 1 or sigmas.size == 0 or np.any(~np.isfinite(sigmas)): + raise ValueError("At least one finite RBF bandwidth is required") + if np.any(sigmas <= 0): + raise ValueError(f"RBF bandwidths must be positive, got {sigmas.tolist()}") + + squared = pairwise_squared_distances(a, b).astype(np.float64, copy=False) + kernels = [np.exp(-squared / (2.0 * sigma * sigma)) for sigma in sigmas] + return np.mean(kernels, axis=0).astype(np.float32) + + +def exact_mmd2( + a: Array, + b: Array, + bandwidths: float | Iterable[float], +) -> float: + """Biased/V-statistic squared MMD used in Eqs. (6)--(7). + + The diagonal terms are intentionally retained: RFF mean-vector distances + approximate this estimator exactly as the feature dimension grows. + """ + + left = _as_float_matrix(a, "a") + right = _as_float_matrix(b, "b") + value = ( + float(rbf_kernel(left, left, bandwidths).mean()) + + float(rbf_kernel(right, right, bandwidths).mean()) + - 2.0 * float(rbf_kernel(left, right, bandwidths).mean()) + ) + # Floating point cancellation may produce a tiny negative value. + return max(value, 0.0) + + +def prompt_averaged_mmd2( + query_samples: Array, + reference_samples: Array, + bandwidths: float | Iterable[float], +) -> float: + """Average exact MMD over aligned prompts, as in Eq. (7).""" + + query = _as_prompt_samples(query_samples, "query_samples") + reference = _as_prompt_samples(reference_samples, "reference_samples") + if query.shape[0] != reference.shape[0] or query.shape[2] != reference.shape[2]: + raise ValueError( + "Prompt and feature dimensions must match: " + f"{query.shape} vs {reference.shape}" + ) + return float( + np.mean( + [ + exact_mmd2(query[index], reference[index], bandwidths) + for index in range(query.shape[0]) + ] + ) + ) + + +def median_bandwidth( + calibration_embeddings: Array, + *, + maximum_samples: int = 5_000, + seed: int = 42, +) -> float: + """Median pairwise-distance bandwidth on a calibration-only sample.""" + + embeddings = _as_float_matrix(calibration_embeddings, "calibration_embeddings") + if embeddings.shape[0] < 2: + raise ValueError("Bandwidth calibration needs at least two embeddings") + if maximum_samples > 0 and embeddings.shape[0] > maximum_samples: + rng = np.random.default_rng(seed) + indices = rng.choice(embeddings.shape[0], size=maximum_samples, replace=False) + embeddings = embeddings[indices] + distances = np.sqrt(pairwise_squared_distances(embeddings, embeddings)) + upper = distances[np.triu_indices(distances.shape[0], k=1)] + usable = upper[np.isfinite(upper) & (upper > 0)] + if usable.size == 0: + raise ValueError("Calibration embeddings contain no positive pairwise distance") + return float(np.median(usable)) + + +@dataclass(frozen=True) +class RandomFourierMap: + """Shared RFF map for an RBF kernel, corresponding to Eq. (3).""" + + frequencies: Array + phases: Array + bandwidth: float + seed: int + + @classmethod + def sample( + cls, + input_dimension: int, + feature_dimension: int, + bandwidth: float, + *, + seed: int = 42, + ) -> "RandomFourierMap": + if input_dimension <= 0 or feature_dimension <= 0: + raise ValueError("RFF input and feature dimensions must be positive") + if not np.isfinite(bandwidth) or bandwidth <= 0: + raise ValueError("RFF bandwidth must be finite and positive") + rng = np.random.default_rng(seed) + frequencies = rng.normal( + loc=0.0, + scale=1.0 / bandwidth, + size=(feature_dimension, input_dimension), + ).astype(np.float32) + phases = rng.uniform(0.0, 2.0 * np.pi, size=feature_dimension).astype(np.float32) + return cls(frequencies, phases, float(bandwidth), int(seed)) + + @property + def input_dimension(self) -> int: + return int(self.frequencies.shape[1]) + + @property + def feature_dimension(self) -> int: + return int(self.frequencies.shape[0]) + + def transform(self, embeddings: Array, *, chunk_size: int = 4096) -> Array: + matrix = _as_float_matrix(embeddings, "embeddings") + if matrix.shape[1] != self.input_dimension: + raise ValueError( + f"Expected {self.input_dimension} input features, got {matrix.shape[1]}" + ) + if chunk_size <= 0: + chunk_size = len(matrix) + scale = np.sqrt(2.0 / self.feature_dimension) + output = np.empty((len(matrix), self.feature_dimension), dtype=np.float32) + for start in range(0, len(matrix), chunk_size): + stop = min(start + chunk_size, len(matrix)) + angles = matrix[start:stop] @ self.frequencies.T + angles += self.phases + output[start:stop] = scale * np.cos(angles) + return output + + +def rfftrace_prompt_means(samples: Array, feature_map: RandomFourierMap) -> Array: + """Compute one empirical kernel mean vector per prompt (Eq. 4).""" + + prompt_samples = _as_prompt_samples(samples, "samples") + prompts, repeats, dimensions = prompt_samples.shape + if dimensions != feature_map.input_dimension: + raise ValueError( + f"Expected embedding dimension {feature_map.input_dimension}, got {dimensions}" + ) + features = feature_map.transform(prompt_samples.reshape(prompts * repeats, dimensions)) + return features.reshape(prompts, repeats, -1).mean(axis=1).astype(np.float32) + + +def rfftrace_vector(samples: Array, feature_map: RandomFourierMap) -> Array: + """Construct the uncompressed ``1/sqrt(t)`` prompt concatenation (Eq. 4).""" + + means = rfftrace_prompt_means(samples, feature_map) + return (means.reshape(-1) / np.sqrt(means.shape[0])).astype(np.float32) + + +def gaussian_projection_matrix( + input_dimension: int, + output_dimension: int, + *, + seed: int = 42, +) -> Array: + """Sample the shared compact-DNA projection in Eq. (10).""" + + if input_dimension <= 0 or output_dimension <= 0: + raise ValueError("Projection dimensions must be positive") + rng = np.random.default_rng(seed) + return rng.normal( + 0.0, + 1.0 / np.sqrt(output_dimension), + size=(output_dimension, input_dimension), + ).astype(np.float32) + + +def compact_rfftrace_vector( + samples: Array, + feature_map: RandomFourierMap, + projection: Optional[Array], +) -> Array: + """Return RFFTrace directly, or its shared JL-projected compact DNA.""" + + vector = rfftrace_vector(samples, feature_map) + if projection is None: + return vector + matrix = np.asarray(projection, dtype=np.float32) + if matrix.ndim != 2 or matrix.shape[1] != vector.size: + raise ValueError( + f"Projection must have shape (L, {vector.size}), got {matrix.shape}" + ) + return (matrix @ vector).astype(np.float32) + + +def squared_euclidean_distance(a: Array, b: Array) -> float: + left = np.asarray(a, dtype=np.float32).reshape(-1) + right = np.asarray(b, dtype=np.float32).reshape(-1) + if left.shape != right.shape: + raise ValueError(f"Vector shapes differ: {left.shape} vs {right.shape}") + delta = left - right + return float(delta @ delta) diff --git a/tests/test_distributional.py b/tests/test_distributional.py new file mode 100644 index 0000000..ac6ba80 --- /dev/null +++ b/tests/test_distributional.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import numpy as np +import pytest + +import llm_dna +from llm_dna.distributional import ( + RandomFourierMap, + compact_rfftrace_vector, + exact_mmd2, + gaussian_projection_matrix, + median_bandwidth, + pairwise_squared_distances, + prompt_averaged_mmd2, + rbf_kernel, + rfftrace_vector, + squared_euclidean_distance, +) + + +def test_public_api_preserves_existing_exports() -> None: + assert "calc_dna" in llm_dna.__all__ + assert "RandomFourierMap" in llm_dna.__all__ + assert llm_dna.RandomFourierMap is RandomFourierMap + + +def test_pairwise_squared_distances_has_expected_geometry() -> None: + left = np.asarray([[0.0, 0.0], [1.0, 0.0]], dtype=np.float32) + right = np.asarray([[0.0, 1.0]], dtype=np.float32) + np.testing.assert_allclose( + pairwise_squared_distances(left, right), + np.asarray([[1.0], [2.0]], dtype=np.float32), + ) + + +def test_exact_mmd_is_zero_for_identical_samples() -> None: + samples = np.asarray([[1.0, 0.0], [0.0, 1.0]], dtype=np.float32) + assert exact_mmd2(samples, samples, 1.0) == pytest.approx(0.0, abs=1e-7) + + +def test_multiscale_kernel_is_mean_of_individual_kernels() -> None: + samples = np.asarray([[0.0], [1.0]], dtype=np.float32) + expected = (rbf_kernel(samples, samples, 0.5) + rbf_kernel(samples, samples, 2.0)) / 2 + np.testing.assert_allclose(rbf_kernel(samples, samples, [0.5, 2.0]), expected) + + +def test_median_bandwidth_uses_positive_pairwise_distances() -> None: + samples = np.asarray([[0.0], [1.0], [3.0]], dtype=np.float32) + assert median_bandwidth(samples) == pytest.approx(2.0) + + +def test_rff_map_is_reproducible_and_shared() -> None: + first = RandomFourierMap.sample(3, 64, 1.5, seed=7) + second = RandomFourierMap.sample(3, 64, 1.5, seed=7) + np.testing.assert_array_equal(first.frequencies, second.frequencies) + np.testing.assert_array_equal(first.phases, second.phases) + + +def test_rff_inner_products_approximate_rbf_kernel() -> None: + samples = np.asarray( + [[0.2, -0.1], [1.1, 0.4], [-0.7, 0.8]], + dtype=np.float32, + ) + feature_map = RandomFourierMap.sample(2, 50_000, 1.2, seed=11) + features = feature_map.transform(samples) + approximation = features @ features.T + exact = rbf_kernel(samples, samples, 1.2) + np.testing.assert_allclose(approximation, exact, atol=0.02) + + +def test_rfftrace_distance_approximates_prompt_averaged_mmd() -> None: + query = np.asarray( + [ + [[0.0, 0.0], [0.1, 0.0], [-0.1, 0.0]], + [[1.0, 0.9], [0.9, 1.0], [1.1, 1.0]], + ], + dtype=np.float32, + ) + reference = np.asarray( + [ + [[0.4, 0.0], [0.5, 0.1], [0.3, -0.1]], + [[0.8, 0.5], [0.9, 0.6], [0.7, 0.4]], + ], + dtype=np.float32, + ) + feature_map = RandomFourierMap.sample(2, 80_000, 0.8, seed=19) + rff_distance = squared_euclidean_distance( + rfftrace_vector(query, feature_map), + rfftrace_vector(reference, feature_map), + ) + exact_distance = prompt_averaged_mmd2(query, reference, 0.8) + assert rff_distance == pytest.approx(exact_distance, abs=0.015) + + +def test_compact_projection_is_reproducible() -> None: + rng = np.random.default_rng(3) + samples = rng.normal(size=(4, 2, 3)).astype(np.float32) + feature_map = RandomFourierMap.sample(3, 16, 1.0, seed=5) + projection_a = gaussian_projection_matrix(4 * 16, 12, seed=9) + projection_b = gaussian_projection_matrix(4 * 16, 12, seed=9) + np.testing.assert_array_equal(projection_a, projection_b) + np.testing.assert_array_equal( + compact_rfftrace_vector(samples, feature_map, projection_a), + compact_rfftrace_vector(samples, feature_map, projection_b), + ) + + +def test_invalid_bandwidth_is_rejected() -> None: + samples = np.asarray([[0.0], [1.0]], dtype=np.float32) + with pytest.raises(ValueError, match="positive"): + rbf_kernel(samples, samples, 0.0) diff --git a/tests/test_rbf_mmd_pilot.py b/tests/test_rbf_mmd_pilot.py new file mode 100644 index 0000000..430b82a --- /dev/null +++ b/tests/test_rbf_mmd_pilot.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +import numpy as np + + +SCRIPT = Path(__file__).parents[1] / "scripts" / "run_rbf_mmd_pilot.py" +SPEC = importlib.util.spec_from_file_location("run_rbf_mmd_pilot", SCRIPT) +assert SPEC is not None and SPEC.loader is not None +PILOT = importlib.util.module_from_spec(SPEC) +sys.modules[SPEC.name] = PILOT +SPEC.loader.exec_module(PILOT) + + +def test_exact_mmd_is_zero_for_identical_samples() -> None: + samples = np.asarray([[1.0, 0.0], [0.0, 1.0]], dtype=np.float32) + assert np.isclose(PILOT.exact_mmd2(samples, samples, 1.0), 0.0) + + +def test_same_setting_split_uses_disjoint_repeat_halves() -> None: + setting = PILOT.SettingSpec(0.2, 0.8) + runs = [ + PILOT.RunRecord("m", setting, repeat, Path(f"/tmp/r{repeat}"), ["p"], ["r"]) + for repeat in range(1, 5) + ] + reference, query = PILOT.chunk_runs_for_same_setting(runs) + assert [run.repeat for run in reference] == [1, 2] + assert [run.repeat for run in query] == [3, 4] + + +def test_select_prompts_intersects_every_selected_record() -> None: + setting = PILOT.SettingSpec(0.2, 0.8) + records = { + ("a", setting.code, 1): PILOT.RunRecord( + "a", setting, 1, Path("/tmp/a"), ["shared", "a-only"], ["", ""] + ), + ("b", setting.code, 1): PILOT.RunRecord( + "b", setting, 1, Path("/tmp/b"), ["shared", "b-only"], ["", ""] + ), + } + assert PILOT.select_prompts(records, ["a", "b"], [setting], 0) == ["shared"] diff --git a/tests/test_rfftrace_experiment.py b/tests/test_rfftrace_experiment.py new file mode 100644 index 0000000..18d1f53 --- /dev/null +++ b/tests/test_rfftrace_experiment.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +import importlib.util +import json +import sys +from pathlib import Path + + +_SCRIPT = Path(__file__).resolve().parents[1] / "scripts" / "run_rfftrace_experiment.py" +_SPEC = importlib.util.spec_from_file_location("run_rfftrace_experiment", _SCRIPT) +assert _SPEC is not None and _SPEC.loader is not None +experiment = importlib.util.module_from_spec(_SPEC) +sys.modules[_SPEC.name] = experiment +_SPEC.loader.exec_module(experiment) + + +def write_run( + root: Path, + model: str, + setting: experiment.Setting, + repeat: int, + prompts: list[str], +) -> experiment.ResponseRun: + safe = model.replace("/", "_") + run_dir = root / f"{safe}_{setting.code}_r{repeat}" + run_dir.mkdir(parents=True) + path = run_dir / "responses.json" + path.write_text( + json.dumps( + { + "model": model, + "items": [ + {"prompt": prompt, "response": f"{model}-{repeat}-{prompt}"} + for prompt in prompts + ], + } + ), + encoding="utf-8", + ) + return experiment.load_response_run(path) + + +def test_load_response_run_preserves_unsanitized_model_id(tmp_path: Path) -> None: + setting = experiment.Setting(0.2, 0.8) + record = write_run(tmp_path, "org/model", setting, 2, ["p1", "p2"]) + assert record.model_id == "org/model" + assert record.setting == setting + assert record.repeat == 2 + + +def test_common_prompts_intersects_every_selected_run(tmp_path: Path) -> None: + setting = experiment.Setting(0.2, 0.8) + first = write_run(tmp_path, "org/a", setting, 1, ["shared", "only-a"]) + second = write_run(tmp_path, "org/b", setting, 1, ["shared", "only-b"]) + assert experiment.common_prompts([first, second]) == ["shared"] + + +def test_select_models_requires_every_requested_repeat(tmp_path: Path) -> None: + reference = experiment.Setting(0.2, 0.8) + query = experiment.Setting(0.3, 1.0) + records = {} + for model in ["complete", "missing"]: + for setting, repeats in [(reference, [1, 3]), (query, [2, 4])]: + for repeat in repeats: + if model == "missing" and setting == query and repeat == 4: + continue + record = write_run(tmp_path, model, setting, repeat, ["p"]) + records[(model, setting, repeat)] = record + selected = experiment.select_models( + records, + reference, + [query], + (1, 3), + (2, 4), + 2, + None, + 0, + ) + assert selected == ["complete"] + + +def test_split_prompts_is_disjoint_and_reproducible() -> None: + prompts = [f"p{index}" for index in range(10)] + first = experiment.split_prompts(prompts, 0.2, 42) + second = experiment.split_prompts(prompts, 0.2, 42) + assert first == second + assert set(first[0]).isdisjoint(first[1]) + assert set(first[0]) | set(first[1]) == set(prompts) diff --git a/tests/test_rfftrace_svm.py b/tests/test_rfftrace_svm.py new file mode 100644 index 0000000..28e166f --- /dev/null +++ b/tests/test_rfftrace_svm.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import importlib.util +from pathlib import Path + +import numpy as np +import pytest +from sklearn.svm import LinearSVC + + +SCRIPT = Path(__file__).parents[1] / "scripts" / "evaluate_rfftrace_svm.py" +SPEC = importlib.util.spec_from_file_location("evaluate_rfftrace_svm", SCRIPT) +assert SPEC is not None and SPEC.loader is not None +MODULE = importlib.util.module_from_spec(SPEC) +SPEC.loader.exec_module(MODULE) + + +def write_vectors(path: Path) -> None: + models = np.asarray(["a", "b", "c"]) + base = np.asarray([[0.0, 0.0], [5.0, 0.0], [0.0, 5.0]], dtype=np.float32) + np.savez_compressed( + path, + models=models, + train_1=base, + train_2=base + 0.05, + test=base + 0.02, + ) + + +def test_load_matrices_rejects_split_overlap(tmp_path: Path) -> None: + path = tmp_path / "vectors.npz" + write_vectors(path) + with pytest.raises(ValueError, match="disjoint"): + MODULE.load_matrices(path, ["train_1"], ["train_1"]) + + +def test_linear_svm_recovers_separable_held_out_vectors(tmp_path: Path) -> None: + path = tmp_path / "vectors.npz" + write_vectors(path) + models, matrices = MODULE.load_matrices(path, ["train_1", "train_2"], ["test"]) + summaries, predictions = MODULE.evaluate_classifier( + "linear_svm", + LinearSVC(C=1.0, dual="auto", random_state=42), + models, + matrices, + ["train_1", "train_2"], + ["test"], + ) + assert summaries[0]["top1"] == pytest.approx(1.0) + assert all(row["correct"] == 1 for row in predictions) + + +def test_binary_rank_expands_single_decision_score() -> None: + classes = np.asarray(["a", "b"]) + assert MODULE.rank_from_scores(np.asarray(-2.0), classes, "a") == 1 + assert MODULE.rank_from_scores(np.asarray(2.0), classes, "b") == 1 + + +def test_parse_gamma_accepts_named_and_numeric_values() -> None: + assert MODULE.parse_gamma("scale") == "scale" + assert MODULE.parse_gamma("0.25") == pytest.approx(0.25) From 01d42155314d4231dd323eb0ecd0538b1202aee9 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Thu, 13 Aug 2026 13:39:37 +0800 Subject: [PATCH 25/28] Update August 13 recovery and DistDNA figures --- .../distdna_k2_pilot_design.png | Bin 0 -> 130910 bytes .../live_grid_seed_recovery.png | Bin 0 -> 120330 bytes .../primary_robustness_20260813/snapshot.json | 24 ++ ...ess_experiment_full_updated_2026-08-13.pdf | Bin 383905 -> 815006 bytes ...ess_experiment_full_updated_2026-08-13.tex | 191 +++++++----- .../plot_primary_status_and_distdna_design.py | 288 ++++++++++++++++++ 6 files changed, 431 insertions(+), 72 deletions(-) create mode 100644 docs/figures/primary_robustness_20260813/distdna_k2_pilot_design.png create mode 100644 docs/figures/primary_robustness_20260813/live_grid_seed_recovery.png create mode 100644 docs/figures/primary_robustness_20260813/snapshot.json create mode 100644 scripts/plot_primary_status_and_distdna_design.py diff --git a/docs/figures/primary_robustness_20260813/distdna_k2_pilot_design.png b/docs/figures/primary_robustness_20260813/distdna_k2_pilot_design.png new file mode 100644 index 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zMz9!#JrPw%gutF)83yB+Le>wdT*zjaD2kCS1wQdJAL%4+gS?ESRjmg_wanBqV<5CkTle3TUBmbc(=UfDIhNtsJ~A!QmPr zp^#+g!v4-=z-U1UGN5b{g3$yERH*4--z8Z8K@jCP5K@FpiZFvrxbz`72`pI9J()NK z?)xA$@i)#uP$n-!b6~{NWr#c*Hi{-5k&)!JQ{bFDAmX$vBoe*>InFmbO ImageFont.FreeTypeFont: + name = "DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf" + return ImageFont.truetype(f"/usr/share/fonts/truetype/dejavu/{name}", size) + + +def rounded_panel(draw: ImageDraw.ImageDraw, box: tuple[int, int, int, int]) -> None: + draw.rounded_rectangle(box, radius=22, fill="#f8fafc", outline="#cbd5e1", width=3) + + +def status_map(path: Path) -> dict[str, str]: + final: dict[str, str] = {} + if not path.exists(): + return final + for line in path.read_text(encoding="utf-8", errors="ignore").splitlines(): + try: + row = json.loads(line) + except json.JSONDecodeError: + continue + if row.get("model_id"): + final[str(row["model_id"])] = str(row.get("status", "")) + return final + + +def seed_sources(cohort: set[str]) -> dict[tuple[str, float, float, int], set[str]]: + sources: dict[tuple[str, float, float, int], set[str]] = defaultdict(set) + for manifest_path in (ROOT / "out").rglob("manifest.json"): + try: + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + temperature = float(manifest["temperature"]) + top_p = float(manifest["top_p"]) + except (OSError, ValueError, KeyError, TypeError, json.JSONDecodeError): + continue + suffix = str(manifest.get("output_suffix", "")) + match = re.search(r"_r([12])$", suffix) + repeat = int(match.group(1)) if match else None + if ( + repeat is None + and manifest_path.parent.name == "rand_chinese_0p3b_7b_temp07" + and temperature == 0.7 + and top_p == 0.9 + ): + repeat = 1 + if ( + repeat not in (1, 2) + or temperature not in {0.0, 0.2, 0.3, 0.5, 0.7} + or top_p not in {0.8, 0.9, 1.0} + or (temperature == 0.0 and top_p != 1.0) + ): + continue + explicit = manifest.get("generation_seed") is not None + for model, status in status_map(manifest_path.parent / "status.jsonl").items(): + if model in cohort and status == "success": + sources[(model, temperature, top_p, repeat)].add( + "explicit" if explicit else "unknown" + ) + return sources + + +def collect_snapshot() -> dict[str, object]: + grid = load_grid_module() + cohort = grid.load_cohort() + runs, _ignored = grid.collect_runs(cohort) + rows: list[dict[str, object]] = [] + successes: set[tuple[str, float, float, int]] = set() + for temperature in grid.TEMPERATURES: + for top_p in grid.TOP_PS: + if not grid.planned_condition(temperature, top_p): + continue + for repeat in grid.REPEATS: + summary = grid.summarize_run(runs[(temperature, top_p, repeat)], cohort) + rows.append( + { + "temperature": temperature, + "top_p": top_p, + "repeat": repeat, + **summary, + } + ) + successes.update( + (model, temperature, top_p, repeat) + for model, state in runs[(temperature, top_p, repeat)]["final"].items() + if model in cohort and state == "success" + ) + total = sum(int(row["success"]) for row in rows) + strict = sum( + all( + (model, temperature, top_p, repeat) in successes + for temperature in grid.TEMPERATURES + for top_p in grid.TOP_PS + if grid.planned_condition(temperature, top_p) + for repeat in grid.REPEATS + ) + for model in cohort + ) + per_setting: dict[str, int] = defaultdict(int) + for row in rows: + key = f"{float(row['temperature']):g}:{float(row['top_p']):g}" + per_setting[key] += int(row["success"]) + source_tags = seed_sources(cohort) + provenance: Counter[str] = Counter() + for key in successes: + tags = source_tags.get(key, set()) + provenance["explicit"] += int("explicit" in tags) + provenance["unknown_only"] += int("explicit" not in tags) + return { + "generated_at": datetime.now().astimezone().isoformat(timespec="seconds"), + "successful": total, + "remaining": 2600 - total, + "recovered_since_august_5": max(0, total - BASELINE_SUCCESS), + "strict_common_models": strict, + "explicit_seed_successes": provenance["explicit"], + "unknown_seed_only_successes": provenance["unknown_only"], + "per_setting_successes": dict(per_setting), + } + + +def interpolate(start: tuple[int, int, int], end: tuple[int, int, int], ratio: float) -> str: + values = [round(a + (b - a) * ratio) for a, b in zip(start, end)] + return "#" + "".join(f"{value:02x}" for value in values) + + +def draw_progress(snapshot: dict[str, object], output: Path) -> None: + image = Image.new("RGB", (1800, 1050), "white") + draw = ImageDraw.Draw(image) + draw.text((70, 42), "Primary robustness grid — live recovery snapshot", font=font(42, True), fill="#0f172a") + stamp = str(snapshot["generated_at"]).replace("T", " ") + draw.text((70, 96), f"Generated {stamp}; 100 models × 13 settings × 2 rounds", font=font(24), fill="#475569") + + rounded_panel(draw, (55, 155, 970, 975)) + draw.text((90, 185), "A. Successful artifacts by decoding setting", font=font(29, True), fill="#0f172a") + temperatures = [0.0, 0.2, 0.3, 0.5, 0.7] + top_ps = [0.8, 0.9, 1.0] + left, top, cell_w, cell_h = 250, 275, 215, 118 + for column, top_p in enumerate(top_ps): + draw.text((left + column * cell_w + 63, top - 52), f"p={top_p:g}", font=font(25, True), fill="#334155") + values = snapshot["per_setting_successes"] + assert isinstance(values, dict) + for row, temperature in enumerate(temperatures): + draw.text((92, top + row * cell_h + 38), f"T={temperature:g}", font=font(25, True), fill="#334155") + for column, top_p in enumerate(top_ps): + box = (left + column * cell_w, top + row * cell_h, left + (column + 1) * cell_w - 15, top + (row + 1) * cell_h - 15) + if temperature == 0.0 and top_p != 1.0: + draw.rounded_rectangle(box, radius=12, fill="#e2e8f0") + draw.text((box[0] + 71, box[1] + 31), "N/A", font=font(25, True), fill="#64748b") + continue + count = int(values[f"{temperature:g}:{top_p:g}"]) + color = interpolate((254, 226, 226), (22, 163, 74), count / 200.0) + draw.rounded_rectangle(box, radius=12, fill=color, outline="#94a3b8", width=2) + draw.text((box[0] + 47, box[1] + 23), f"{count}/200", font=font(27, True), fill="#0f172a") + draw.text((box[0] + 65, box[1] + 59), f"{count / 2:.1f}%", font=font(20), fill="#334155") + + rounded_panel(draw, (1010, 155, 1745, 555)) + draw.text((1045, 185), "B. Recovery and complete-case progress", font=font(29, True), fill="#0f172a") + successful = int(snapshot["successful"]) + recovered = int(snapshot["recovered_since_august_5"]) + remaining = int(snapshot["remaining"]) + bar = (1050, 285, 1695, 350) + base_width = round((BASELINE_SUCCESS / 2600) * (bar[2] - bar[0])) + recovery_width = round((recovered / 2600) * (bar[2] - bar[0])) + draw.rounded_rectangle(bar, radius=16, fill="#fee2e2") + draw.rounded_rectangle((bar[0], bar[1], bar[0] + base_width, bar[3]), radius=16, fill="#3b82f6") + draw.rectangle((bar[0] + base_width - 10, bar[1], bar[0] + base_width + recovery_width, bar[3]), fill="#22c55e") + draw.text((1050, 370), f"{successful}/2,600 successful ({100 * successful / 2600:.1f}%)", font=font(28, True), fill="#0f172a") + draw.text((1050, 415), f"August 5 baseline 2,528 + recovered {recovered} | remaining {remaining}", font=font(21), fill="#334155") + strict = int(snapshot["strict_common_models"]) + draw.text((1050, 468), f"Strict all-26-run common cohort: {strict}/100 models", font=font(24, True), fill="#7c2d12") + + rounded_panel(draw, (1010, 595, 1745, 975)) + draw.text((1045, 625), "C. Generation-seed provenance", font=font(29, True), fill="#0f172a") + explicit = int(snapshot["explicit_seed_successes"]) + unknown = int(snapshot["unknown_seed_only_successes"]) + total = max(1, explicit + unknown) + x0, y0, width = 1050, 735, 645 + explicit_width = round(width * explicit / total) + draw.rounded_rectangle((x0, y0, x0 + width, y0 + 65), radius=16, fill="#f59e0b") + draw.rounded_rectangle((x0, y0, x0 + explicit_width, y0 + 65), radius=16, fill="#14b8a6") + draw.text((1050, 820), f"Explicit generation seed: {explicit}", font=font(24, True), fill="#0f766e") + draw.text((1050, 860), f"Historical unknown-only seed: {unknown}", font=font(24, True), fill="#b45309") + draw.text((1050, 915), "Coverage is not the same as seed-clean coverage.", font=font(21), fill="#475569") + output.parent.mkdir(parents=True, exist_ok=True) + image.save(output, optimize=True) + + +def arrow(draw: ImageDraw.ImageDraw, start: tuple[int, int], end: tuple[int, int], color: str = "#334155") -> None: + draw.line((start, end), fill=color, width=7) + x, y = end + draw.polygon([(x, y), (x - 20, y - 13), (x - 20, y + 13)], fill=color) + + +def draw_pilot_design(output: Path) -> None: + image = Image.new("RGB", (1800, 1000), "white") + draw = ImageDraw.Draw(image) + draw.text((70, 40), "Same-setting K=2 DistDNA pilot: leakage-free, matched-budget comparison", font=font(39, True), fill="#0f172a") + draw.text((70, 92), "Preselect a difficult 20–30 model cohort; use identical prompts, setting, encoder, and token budget.", font=font(23), fill="#475569") + + rounded_panel(draw, (55, 155, 855, 565)) + draw.text((90, 185), "A. Four independent response sets", font=font(29, True), fill="#0f172a") + boxes = [ + (100, 285, 305, 395, "r1", "seed 42001", "#bfdbfe"), + (350, 285, 555, 395, "r3", "seed 42003", "#bfdbfe"), + (100, 440, 305, 550, "r2", "seed 42002", "#ddd6fe"), + (350, 440, 555, 550, "r4", "seed 42004", "#ddd6fe"), + ] + for x1, y1, x2, y2, name, seed, color in boxes: + draw.rounded_rectangle((x1, y1, x2, y2), radius=16, fill=color, outline="#64748b", width=2) + draw.text((x1 + 78, y1 + 14), name, font=font(29, True), fill="#0f172a") + draw.text((x1 + 28, y1 + 62), seed, font=font(20), fill="#334155") + draw.text((610, 305), "Reference\ndistribution\nK=2", font=font(25, True), fill="#1d4ed8", spacing=8) + draw.text((610, 455), "Held-out query\ndistribution\nK=2", font=font(25, True), fill="#6d28d9", spacing=8) + arrow(draw, (555, 340), (595, 340), "#1d4ed8") + arrow(draw, (555, 495), (595, 495), "#6d28d9") + + rounded_panel(draw, (895, 155, 1745, 565)) + draw.text((930, 185), "B. Compare on identical responses", font=font(29, True), fill="#0f172a") + methods = [ + ("Lower-budget diagnostic", "Single-response cosine", "K=1"), + ("Strong matched baseline", "Mean-DNA cosine", "K=2"), + ("Exact distribution distance", "RBF-MMD", "K=2"), + ("Scalable proposed vector", "RFFTrace Euclidean", "K=2"), + ("Compact proposed vector", "Projected DistDNA", "K=2"), + ] + y = 250 + for label, name, budget in methods: + draw.rounded_rectangle((935, y, 1695, y + 52), radius=12, fill="#ffffff", outline="#cbd5e1", width=2) + draw.text((955, y + 10), label, font=font(18), fill="#64748b") + draw.text((1220, y + 8), name, font=font(20, True), fill="#0f172a") + draw.text((1620, y + 10), budget, font=font(18, True), fill="#334155") + y += 61 + + rounded_panel(draw, (55, 605, 1745, 940)) + draw.text((90, 635), "C. Decision rule for claiming an improvement", font=font(29, True), fill="#0f172a") + draw.text((105, 705), "Primary endpoint", font=font(22, True), fill="#475569") + draw.text((360, 698), "Top-1 exact-model retrieval; report MRR, Top-3/5, time, and storage secondarily.", font=font(24), fill="#0f172a") + draw.text((105, 770), "Fairness", font=font(22, True), fill="#475569") + draw.text((360, 763), "Compare DistDNA against matched-K=2 mean cosine, not only the cheaper K=1 baseline.", font=font(24), fill="#0f172a") + draw.text((105, 835), "Statistical gate", font=font(22, True), fill="#475569") + draw.text((360, 828), "Paired per-model Top-1 difference; model-cluster bootstrap 95% CI lower bound > 0.", font=font(24), fill="#0f172a") + draw.text((105, 895), "No leakage", font=font(22, True), fill="#475569") + draw.text((360, 888), "Never reuse r1+r2 on both gallery and query; that forces self-distance toward zero.", font=font(24), fill="#9a3412") + output.parent.mkdir(parents=True, exist_ok=True) + image.save(output, optimize=True) + + +def main() -> int: + snapshot = collect_snapshot() + OUT_DIR.mkdir(parents=True, exist_ok=True) + draw_progress(snapshot, OUT_DIR / "live_grid_seed_recovery.png") + draw_pilot_design(OUT_DIR / "distdna_k2_pilot_design.png") + (OUT_DIR / "snapshot.json").write_text( + json.dumps(snapshot, indent=2, ensure_ascii=False), encoding="utf-8" + ) + print(json.dumps(snapshot, indent=2, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) From 11caed76716be9d678b17965e656cf3c5fd3f877 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Thu, 13 Aug 2026 14:03:47 +0800 Subject: [PATCH 26/28] Polish August 13 progress report --- .../live_grid_seed_recovery.png | Bin 120330 -> 120898 bytes .../primary_robustness_20260813/snapshot.json | 2 +- ...ss_experiment_full_updated_2026-08-13.html | 334 +++++ ...ess_experiment_full_updated_2026-08-13.pdf | Bin 815006 -> 437694 bytes ...ess_experiment_full_updated_2026-08-13.tex | 1318 ----------------- .../plot_primary_status_and_distdna_design.py | 2 +- scripts/render_aug13_progress_report.sh | 17 + 7 files changed, 353 insertions(+), 1320 deletions(-) create mode 100644 docs/primary_robustness_experiment_full_updated_2026-08-13.html delete mode 100644 docs/primary_robustness_experiment_full_updated_2026-08-13.tex create mode 100755 scripts/render_aug13_progress_report.sh diff --git a/docs/figures/primary_robustness_20260813/live_grid_seed_recovery.png b/docs/figures/primary_robustness_20260813/live_grid_seed_recovery.png index d7917e8eb0b23ae6869381c9277aad953841d7fe..5a46ff3162a3a4a73de696f6a49baac0c01fb1dc 100644 GIT binary patch literal 120898 zcmeFYcT`hd_ce<8C@KPq6a|47QBbNVMY@7Y@4W;Bq&Mk3K?Omo)KCPJDmApwLlFTX z6d^!pA@tr`fDpKw=ly-(y?5OI?-+NCdq(~^VQfy$*?aA|=A3JtFY2mtl-C)rlaY~8 zD#*XmBqJlgN=Eh%M%7=*Ds?ugC_lAOme(z z2MXosUfNy{3kwVlyRWE0`}kv~`&TX$VN&To@n64wogC+=$Fr_h^VFxl^XocIZ=GK2 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b/docs/figures/primary_robustness_20260813/snapshot.json index 4771021..39efa1e 100644 --- a/docs/figures/primary_robustness_20260813/snapshot.json +++ b/docs/figures/primary_robustness_20260813/snapshot.json @@ -1,5 +1,5 @@ { - "generated_at": "2026-08-13T13:31:28+08:00", + "generated_at": "2026-08-13T13:56:24+08:00", "successful": 2552, "remaining": 48, "recovered_since_august_5": 24, diff --git a/docs/primary_robustness_experiment_full_updated_2026-08-13.html b/docs/primary_robustness_experiment_full_updated_2026-08-13.html new file mode 100644 index 0000000..d5a7435 --- /dev/null +++ b/docs/primary_robustness_experiment_full_updated_2026-08-13.html @@ -0,0 +1,334 @@ + + + + +Primary LLM-DNA Robustness Experiment — Weekly Progress Update + + + + +

+
LLM-DNA · Primary robustness study
+

Weekly Progress Update

+
Recovery status, seed provenance, and the four-seed DistDNA validation plan
+
Reporting period: 5–13 August 2026 · Snapshot: 13 August, 13:56 SGT
+
+ +

This note updates the 5 August report. It records only what changed during the week, what the current data can support, and the next experiment needed to test DistDNA fairly. The full experimental protocol and previously reported formulas remain in the earlier report and are not repeated here.

+ +
+
2,552
successful artifacts
of 2,600 planned
+
+24
failed slots recovered
since 5 August
+
68
models complete across
all 26 planned runs
+
48
slots still lacking a
successful artifact
+
+ +

What changed this week

+
    +
  • A failure-specific recovery pass was launched on the current A100 host and increased grid coverage from 97.2% to 98.2%.
    • +
  • The strict all-run cohort increased from 59 to 68 models. Recovery is still active, so this is a timestamped interim count.
    • +
  • Seed provenance was separated from artifact coverage: 1,956 successful slots still rely only on historical runs with no recorded generation seed.
    • +
  • The repository now contains the formal RFFTrace/DistDNA implementation, exact MMD baseline, matched-sample evaluator, compact projection, and tests.
    • +
  • A leakage-free four-seed pilot is specified so that DistDNA and cosine baselines consume exactly the same generated responses.
    • +
+ +
+

Recommended decision. First run a 10-model, one-setting, four-seed debugging pilot. Use it to validate the data split, implementation, and effect direction. Then start the 100-model extension at the same setting. Expand to all 12 stochastic settings only after the pilot is stable.

+
+ +
+
Current job
+
Recovery was running at report build time. The active cell had moved to T=0.5, top-p=0.9, repeat 1, using generation seed 42.
+
+
Primary LLM-DNA robustness experiment · 13 August 20261
+
+ +
+

1. Recovery progress since 5 August

+ + + + + + + + + + +
Measure5 Aug frozen snapshot13 Aug live snapshotChange
Successful artifacts2,528 / 2,6002,552 / 2,600+24
Artifact coverage97.2%98.2%+1.0 pp
Slots without success7248−24
Strict all-26-run cohort59 / 10068 / 100+9 models
Explicit-seed successes572596+24
Historical unknown-seed only1,9561,956unchanged
+ + Live primary grid recovery status +

Figure 1. Live grid snapshot at 13:56 SGT on 13 August. Green recovery gains are real successful artifacts, but the seed-provenance panel shows why high coverage is not yet equivalent to a four-seed DistDNA dataset.

+ +
+

Interpretation. The recovery pass repairs the preregistered two-round grid. It should finish before a final primary-grid analysis is frozen. It does not, by itself, create four independent samples per model and setting.

+
+ +

Settings that remain most incomplete

+

The largest remaining gap is T=0.5, top-p=1.0 with 176/200 successful artifacts. All other stochastic settings are between 195/200 and 200/200. Failures are distributed across models, which is why the strict common cohort remains lower than the overall coverage percentage.

+
Weekly progress update · Recovery2
+
+ +
+

2. Seed provenance: what is reusable

+

The response-generation seed and the DNA projection seed are different variables. This report concerns the response-generation seed. The projection seed remains fixed and does not create an independent response sample.

+ + + + + + + + + +
Artifact sourceRecorded generation seedUse in current analysisUse in four-seed DistDNA
Stochastic T=0.2, 0.3, 0.7 backfillsr1: 42001
r2: 42002		ValidReusable as independent seeds
T=0.5 control and recoveryr1: 42
r2: 42		Valid for coverageReplace for seed-clean study
Historical sweepNot recordedValid for matched method comparisons on the same responsesReplace; independence cannot be established
New four-seed extension42001, 42002, 42003, 42004ValidPrimary DistDNA evidence
+ +
+
+

The current 98.2% grid supports

+
    +
  • Two-round cosine, linear SVM, and RBF-SVM comparisons.
    • +
  • Temperature/top-p robustness analysis on a fixed matched cohort.
    • +
  • Exploratory representation comparisons when every method reads identical artifacts.
    • +
+
+
+

It does not yet support

+
    +
  • A leakage-free same-setting K=2 reference and K=2 query split.
    • +
  • A claim that seed 42 duplicates or unknown-seed runs are independent samples.
    • +
  • A fair DistDNA advantage claim based only on the easier single-vector cosine baseline.
    • +
+
+
+ +

Current seed-clean inventory

+

Across the 12 stochastic settings, the live repository contains 320 unique successful model–setting artifacts with explicit seed 42001 or 42002. A further 176 successful stochastic artifacts use seed 42 and are not counted as seed-clean four-seed evidence. The 100 deterministic artifacts are also excluded because repeated sampling is inactive at T=0.

+ +
+

Operational rule for the new queue: skip an artifact only when the same model, decoding setting, prompt set, token budget, and exact generation seed already have a successful output. Unknown-seed and seed-42 artifacts remain in the historical grid but do not satisfy a 42001–42004 slot.

+
+
Weekly progress update · Seed provenance3
+
+ +
+

3. The immediate DistDNA pilot

+

The pilot asks one narrow question: when the generated-response budget is matched, does a distributional representation identify the model more reliably than averaging ordinary DNA vectors and using cosine retrieval?

+ + Four-seed DistDNA pilot design +

Figure 2. Four independent response sets at one fixed decoding setting. Seeds 42001 and 42003 form the reference distribution; seeds 42002 and 42004 form the held-out query distribution. Every K=2 method reads the same four sets.

+ +

Recommended first setting

+
+

T=0.7, top-p=1.0. This is deliberately stochastic and therefore more likely to reveal whether distributional information helps. It also has broad explicit seed-42002 coverage in the current grid, reducing the amount of new generation needed for the later 100-model extension.

+
+ +

Why four seeds are needed

+

Two seeds are used to estimate the reference distribution and two different seeds estimate the query distribution. Reusing a response set on both sides would make a model artificially close to itself. Four seeds are therefore the minimum clean design for a K=2 versus K=2 same-setting comparison.

+ +

What the 10-model result means

+

The 10-model run is a debugging and effect-direction pilot. It can reveal implementation errors, leakage, saturation, or an obviously unhelpful representation. It is not a final statistical claim: with ten queries, one corrected model changes Top-1 by ten percentage points. If the pipeline behaves sensibly, expand to 20–30 deliberately confusable models before interpreting confidence intervals.

+
Weekly progress update · DistDNA pilot design4
+
+ +
+

4. Proposed 10-model small-scale cohort

+

All candidates below are already members of the fixed 100-model cohort and are advertised below 0.8B parameters. Selection is based on scale and family structure, not on DistDNA outcomes. The repeated Pythia and Qwen families intentionally make the retrieval task less likely to saturate.

+ + + + + + + + + + + + + + + +
#ModelScaleRole in pilot
1ibm-granite/granite-4.0-h-350m-base≈0.35BGranite baseline
2EleutherAI/pythia-410m-seed1≈0.41BConfusable same-family trio
3EleutherAI/pythia-410m-seed2≈0.41B
4EleutherAI/pythia-410m-seed3≈0.41B
5unsloth/Qwen2.5-0.5B≈0.50BRelated Qwen variants
6unsloth/Qwen2.5-0.5B-Instruct≈0.50B
7Ihor/Text2Graph-R1-Qwen2.5-0.5b≈0.50B
8robowaifudev/megatron-gpt2-345m≈0.35BGPT-2 architecture
9state-spaces/mamba-370m-hf≈0.37BState-space architecture
10unsloth/Qwen3-0.6B-Base≈0.60BNewer Qwen family
+ +
+

Pre-launch check. Parameter labels above are model-name/model-card scale labels. The runner should still validate configuration compatibility, non-empty generation, tokenizer access, and cached model availability before the four-seed queue starts.

+
+ +

Frozen generation contract

+ + + + + + + + + +
Decoding settingT=0.7, top-p=1.0, sampling enabled
Seeds42001, 42002, 42003, 42004
PromptsThe same 100 classical-Chinese prompts already used by the primary grid
Response budgetThe same maximum generated-token limit as the primary grid
RepresentationThe same response encoder and projection configuration for every method
Output policyNever overwrite historical artifacts; write to a seed-explicit extension directory
+
Weekly progress update · Pilot cohort5
+
+ +
+

5. Fair comparison and expected pilot time

+

Methods evaluated on identical responses

+ + + + + + + + + +
MethodSample budgetPurposeInterpretation
Single-response cosineK=1 vs K=1Original low-budget diagnosticUseful, but not the fairness gate
Mean-DNA cosineK=2 vs K=2Strong matched-budget baselinePrimary comparator
Exact RBF-MMDK=2 vs K=2Exact distribution distanceAccuracy reference for approximation
RFFTraceK=2 vs K=2Scalable distributional vectorPrimary DistDNA result
Compact DistDNAK=2 vs K=2Projected representationAccuracy–storage trade-off
+ +

The decision rule is simple: for each held-out query distribution, rank the ten reference models by distance and check whether the true model is ranked first. Report Top-1, MRR, Top-3, runtime, and storage. The meaningful improvement is DistDNA minus mean-DNA cosine on the same query models.

+ +

Time from an available four-GPU A100 allocation

+
+
0–0.5 h
Audit reusable seed-explicit artifacts, freeze the ten-model manifest, and dry-run the queue.
+
0.5–3.5 h
Generate the missing seed-specific responses. Historical small-model observations have a 2.4-minute median per artifact, with a long retry tail; 1–3 hours is the realistic wall-clock range on four GPUs.
+
3.5–4.5 h
Build embeddings and run cosine, exact MMD, RFFTrace, and compact DistDNA on the identical split.
+
4.5–8 h
Inspect nearest-neighbour errors, verify no leakage, adjust implementation if necessary, rerun evaluation, and produce the first result table.
+
+ +
+
1–3 h
expected generation time
for the 10-model pilot
+
4–8 h
expected time to a checked
first DistDNA comparison
+
+ +
+

Practical advantage. Changes to RFF dimension, bandwidth, projection, or the DistDNA vector do not require regenerating model responses. Once the four raw response sets are stored correctly, representation development can iterate quickly.

+
+
Weekly progress update · Comparison and timing6
+
+ +
+

6. Scaling from 10 models to the full cohort

+

“Four seeds for 100 models” has two materially different interpretations. The operational plan should name the scope explicitly.

+ + + + + + + +
ScopeTarget artifactsReusable nowStill requiredExpected wall time
One setting
T=0.7, top-p=1.0		100 × 4 = 400≈103 explicit 42001/42002 artifacts≈2974 A100s: 2–4 days
8 A100s: 1.5–3 days
All 12 stochastic settings100 × 12 × 4 = 4,800320 explicit 42001/42002 artifacts4,4804 A100s: 4–6 weeks
8 A100s: 2–3 weeks
+ +

Theoretical compute for the all-setting extension is about 2,335 A100 GPU-hours, estimated from the 2,552 successful primary artifacts (31.3 GPU-minutes per artifact on average). Wall-clock ranges add allowance for retries, timeouts, incompatible models, scheduling gaps, and audit passes.

+ +

Recommended staged execution

+
Stage A
Let the current 72-slot recovery pass finish and freeze its final status. This preserves a clean endpoint for the original two-round primary grid.
+
Stage B
Run the 10-model four-seed pilot at T=0.7, top-p=1.0. Obtain a checked DistDNA versus matched mean-cosine result within one working day.
+
Stage C
Start the 100-model extension at the same setting. Skip only successful artifacts with the exact required seed; regenerate unknown-seed and seed-42 slots.
+
Stage D
While Stage C runs, iterate on DistDNA using already generated raw responses. No new generation is needed for representation-only changes.
+
Stage E
Expand to all settings only if the pipeline passes leakage checks and DistDNA shows a useful effect or a clearly motivated diagnostic value.
+ +
+

Why not immediately queue all 4,480 missing artifacts? It is safe to generate raw responses while representation code changes, but the full queue commits several weeks of A100 time. A same-day pilot can first catch split errors, saturated retrieval, an unsuitable bandwidth, or a DistDNA construction that needs revision.

+
+ +

Seed replacement policy

+

Existing unknown-seed and seed-42 artifacts are retained for provenance and the original analysis. They are not deleted or overwritten. The four-seed extension writes new, seed-explicit artifacts alongside them and the audit chooses the correct artifact by exact seed.

+
Weekly progress update · Scale-out plan7
+
+ +
+

7. Implementation status and next deliverables

+ + + + + + + + + + +
StatusComponentRepository location
ImplementedExact MMD, shared RFF map, RFFTrace, compact projectionsrc/llm_dna/distributional.py
ImplementedMatched-sample retrieval evaluator and exportscripts/run_rfftrace_experiment.py
ImplementedOptional DistDNA multiclass SVM evaluationscripts/evaluate_rfftrace_svm.py
VerifiedDistributional and evaluator unit teststests/test_distributional.py
tests/test_rfftrace_experiment.py
NextSeed-exact audit and four-seed generation queueNew pilot manifest and resumable runner
NextPilot result table, paired errors, and saturation diagnosticGenerated after the four-seed pilot
+ +

Acceptance checks before interpreting accuracy

+
    +
  1. Exact split: no response artifact appears in both reference and query distributions.
    2. +
  2. Matched budget: DistDNA and mean-DNA cosine use the same four response sets.
    3. +
  3. Shared map: every model uses the same response encoder, RFF parameters, and optional projection.
    4. +
  4. No outcome-based cohort changes: the ten models are frozen before DistDNA accuracy is inspected.
    5. +
  5. Failure transparency: failed/empty responses are reported and never silently replaced by a different model.
    6. +
  6. Saturation check: if all methods score 100%, move to a larger or more confusable cohort rather than claiming equivalence or improvement.
    7. +
+ +
+

Deliverable sequence

+

1 Final recovery audit   2 10-model seed manifest   3 Four-seed generation   4 Matched comparison   5 Error analysis   6 100-model scale-out

+
+ +

Bottom line

+

A first technically meaningful DistDNA comparison is a same-day task once GPUs are available. A full 100-model, one-setting four-seed dataset is a multi-day task. A four-seed replacement across the entire 12-setting grid is a separate multi-week compute commitment and should follow, not precede, the pilot gate.

+ +
+

Provenance. Counts in this note were generated from the live repository on 13 August 2026 at 13:56 SGT. Recovery was still running. Final figures must be regenerated after that process exits. The 5 August report and PDF remain unchanged.

+
+
Primary LLM-DNA robustness experiment · End of update8
+
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-\newcommand{\todoresult}{\textcolor{gray}{\textit{Not yet available}}} -\newcommand{\method}[1]{\texttt{#1}} -\newcommand{\demovalue}[1]{\textcolor{blue!65!black}{#1\textsuperscript{*}}} - -\title{Primary LLM-DNA Robustness Experiment\\ -\large Updated Full Experimental Protocol and Progress Report} -\author{} -\date{Updated August 13, 2026} - -\begin{document} -\maketitle - -\section*{Executive summary} -This document records the design and current execution status of the primary -LLM-DNA decoding robustness experiment. The study -will measure how temperature and top-$p$ affect exact-model identification, -whether a classifier can mitigate the resulting accuracy loss, and whether a -multi-sample distributional representation improves robustness under a matched -generation budget. The fixed primary cohort contains 100 validated models. -The experiment contains one deterministic condition and a complete -$4\times3$ stochastic grid, with two generation rounds per condition. The -principal deployment setting -assumes that temperature and top-$p$ are hidden at inference time and only an -upper bound on temperature is known. The frozen pre-recovery audit of August 5 -contains 2,528 successful artifacts out of 2,600 planned slots. Of those -successful artifacts, 572 record an explicit generation seed and 1,956 come -from historical manifests that do not record a separate generation seed. -Thus, 97.2\% is an artifact-coverage statistic, not a seed-clean completion -rate. The 72 unsuccessful slots affect 41 distinct models, leaving a strict -all-grid common cohort of 59 in that snapshot. Targeted recovery was launched -on August 13. By the live 13:31 Singapore-time snapshot, recovery had raised -coverage to 2,552/2,600 and the strict common cohort to 68. Small-scale -exploratory results are reported separately and are not substitutes for the -final primary analysis. -\par - -\section{Status and scope} - -\textbf{Primary experiment status (pre-recovery snapshot, August 5, 2026):} -the grid contains 2,528/2,600 successful artifacts (97.2\%). Two of 13 settings -are fully complete. The 72 remaining slots are -52 \texttt{failed\_missing\_file} and 20 -\texttt{failed\_empty\_response}; no ordinary pending slots remain. The strict -intersection across all 26 runs contains 59 models and is below the -preregistered minimum of 80. Section~\ref{sec:small-demo} contains explicitly -labelled exploratory demonstrations that must not be interpreted as final -primary findings. A failure-specific recovery job is running from the fixed -72-slot plan under \path{out/primary_robustness_recovery_20260810}; all final -counts must be regenerated after that job finishes. - -A resource-feasibility review reduced the plan from 300 models and four repeats -to 100 models and two rounds. Repeat 1 is the reference/training split and -repeat 2 is held out. Exact identification is a 100-way closed-set task before -failure filtering. - -The frozen August 5 combined-round counts were 200/200 at the deterministic -control; 199, 200, and 198 at $T=0.2$; 199, 189, and 185 at $T=0.3$; 198, 196, -and 176 at $T=0.5$; and 196, 197, and 195 at $T=0.7$, with stochastic entries -ordered by $p=0.8,0.9,1.0$. - -The live recovery snapshot below is deliberately separate from the frozen -August 5 table. At 13:31 Singapore time on August 13, 24 of the 72 failed slots -had been repaired, giving 2,552/2,600 successes (98.2\%), 48 slots still -without a successful artifact, and a strict all-26-run common cohort of 68. -Among the current successes, 596 have at least one explicitly recorded -generation seed and 1,956 rely only on historical unknown-seed artifacts. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/primary_robustness_20260813/live_grid_seed_recovery.png} -\caption{Live primary-grid snapshot generated at 13:31 Singapore time on -August 13. Panel A shows successful artifacts out of 200 per setting, Panel B -separates the August 5 baseline from recovery gains and reports the strict -complete-case cohort, and Panel C distinguishes artifact coverage from -explicit generation-seed provenance. Recovery was still running, so this is a -timestamped snapshot rather than a final result.} -\label{fig:live-recovery-aug13} -\end{figure} - -The high slot-completion rate and the smaller complete-case cohort are not -contradictory. Failures occur in different models across cells: 72 failed slots -are distributed over 41 models, so $100-41=59$ models succeed everywhere. -The 2,528 successful slots do include historical runs whose generation seed is -unknown: 572 successes have an explicit generation seed and 1,956 do not. -Unknown-seed runs remain usable for paired representation comparisons when -every method reads the identical responses, but they are not treated as -independent seeded evidence for causal decoding claims. -For setting-specific paired evaluation, the usable r1--r2 cohorts remain much -larger: - -At the August 13 13:31 snapshot, the paired r1--r2 cohort sizes were 100 at -the deterministic control; 99, 100, and 99 at $T=0.2$; 99, 98, and 99 at -$T=0.3$; 98, 97, and 77 at $T=0.5$; and 96, 97, and 95 at $T=0.7$, again -ordered by $p=0.8,0.9,1.0$ for stochastic decoding. - -\clearpage - -The live common cohorts for bounded analyses are 98 at $\tau=0.2$, 97 at -$\tau=0.3$, 73 at $\tau=0.5$, and 68 at $\tau=0.7$. Recovery continues to -target the remaining failure-specific slots; the final cohort audit must be -rerun after the recovery process exits. - -This experiment replaces the 77-model interim analysis as the primary -robustness study. The earlier analysis remains a legacy diagnostic because it -used an incomplete sweep, one generation repeat per reported cell, an -historical reference gallery with unknown decoding parameters, and a binary -same-or-related task that was not directly comparable to exact-model Top-1 -retrieval. - -\section{Research questions and hypotheses} - -\subsection{Primary research questions} - -\begin{enumerate}[label=\textbf{RQ\arabic*.}] - \item How do temperature and top-$p$ affect exact-model classification from - LLM-DNA representations? - \item On the same exact-model task, can a classifier trained on the full DNA - vector reduce the accuracy degradation relative to cosine retrieval? - \item Does the proposed multi-sample distributional representation improve - robustness beyond original DNA under the same response and token - budget? - \item If temperature and top-$p$ are unknown at inference time, but - temperature is known to satisfy $T\leq\tau$, what average and - worst-setting accuracy can be expected? -\end{enumerate} - -\subsection{Secondary research questions} - -\begin{enumerate}[label=\textbf{SRQ\arabic*.}] - \item When exact identification fails, does the predicted model remain close - to the true model in the Hugging Face model tree? - \item Do direct parent--child, indirect, and disconnected model pairs exhibit - different DNA-distance distributions as decoding stochasticity rises? - \item How sensitive are the conclusions to response encoder, truncation, - projection dimension, projection seed, and the number of response - samples? -\end{enumerate} - -The main hypothesis is that temperature will cause a larger reduction in exact -classification accuracy than top-$p$ over the planned range. Classifier and -distributional-method benefits will be judged by paired accuracy differences -and by a shallower degradation curve, rather than by comparing accuracies from -different tasks. - -\section{Model cohort} - -\subsection{Fixed validated cohort} - -The primary cohort is the 100-model list exported from the latest validated -control manifest. Historical artifacts count only when their exact model ID, -decoding cell, and round match this cohort and grid. Performance was not used -to add or remove models. - -The fixed artifacts are: - -\begin{itemize} - \item cohort: \path{configs/rand_chinese_validated100_latest.jsonl}; - \item canonical manifest: - \path{out/top_p_control_t05_validated100_20260719/top_p_control_t05_t05_p06_r2/manifest.json}; - \item live audit: \path{reports/primary_robustness_run_grid.csv}; and - \item cohort SHA-256: - \texttt{9043a62b95436b9fc92671b6f5b11b15d427fddd745dba2f84c112d97e850e6d}. -\end{itemize} - -Any operational replacement would define a new cohort version and require the -grid and every common-cohort count to be regenerated. - -\subsection{Completion requirement} - -The intended final analysis uses all 100 models. The automated primary analysis -will refuse to run if fewer than 80 models are common to all required training -and test runs. Missing models should first be backfilled. If the final common -cohort remains below 100, the report will provide success rates and failure -reasons by decoding cell and compare retained and excluded models by size and -architecture. - -\section{Prompt set and DNA construction} - -Every model receives the same 100 synthetic classical-Chinese prompts from -\path{data/rand/rand_dataset_chinese.json}. The prompt construction and random -projection seed remain fixed at 42 for every model, setting, and repeat. - -For the original representation, each response is encoded using -\method{all-mpnet-base-v2}. The 100 response embeddings are concatenated and -mapped to a 128-dimensional DNA vector by a seeded Gaussian random projection. -The current maximum response length is 1024 generated tokens. The saved -metadata records at least the model ID, dataset, prompt count, temperature, -top-$p$, sampling flag, generation seed, maximum length, projection seed, -encoder, and DNA dimension. - -Because the sentence encoder is primarily English-oriented and truncates long -inputs, the final study will additionally report response-length and encoder -token-length distributions, the fraction exceeding the encoder limit, and a -sensitivity analysis with a Chinese or multilingual response encoder. - -\section{Decoding design} - -\subsection{Factorial grid} - -The deterministic condition is -\[ - T=0, \qquad \text{sampling disabled}, \qquad p=1.0\text{ (recorded only)}. -\] -Top-$p$ is inactive when sampling is disabled; consequently, $T=0$ appears once -and is not repeated as three nominal top-$p$ cells. - -For stochastic decoding, the complete grid is -\[ - T\in\{0.2,0.3,0.5,0.7\}, \qquad - p\in\{0.8,0.9,1.0\}. -\] -This gives 13 decoding conditions in total. Two generation rounds are planned -for every condition, giving 26 run cells and -\[ - 100\ \text{models}\times13\ \text{conditions}\times2\ \text{rounds} - =2{,}600 -\] -successful artifacts before retries. - -\subsection{Generation rounds and seeds} - -The response-sampling seeds are: - -\begin{table}[ht] -\centering -\caption{Generation-repeat assignment. The projection seed remains 42 in all rows.} -\label{tab:repeat-assignment} -\begin{tabular}{ccc} -\toprule -Repeat & Generation seed & Primary role \\ -\midrule -1 & 42001 (42 in existing $T=0.5$ control) & training/reference \\ -2 & 42002 (42 in existing $T=0.5$ control) & held-out query \\ -\bottomrule -\end{tabular} -\end{table} - -The shared $T=0.5$ cells require a duplicate/independence audit because both -existing rounds use seed 42. Historical sweep artifacts also did not record a -separate response-generation seed. In the August 5 snapshot, 572 of 2,528 -successful artifacts record an explicit generation seed and 1,956 do not. -These limitations affect causal -temperature interpretation but do not invalidate paired method comparisons -when every method reads exactly the same responses. - -No response, DNA vector, or round may appear in both the training/reference -split and the held-out query split. Generation seed and projection seed are -stored separately so that response variability is not confounded with a change -in the random projection. - -\section{Primary exact-model task} - -Let $\mathcal{M}$ be the fixed common model cohort. Given a query DNA vector -$\mathbf{d}_{m,s,r}$ generated by model $m$, decoding setting $s=(T,p)$, and -held-out repeat $r=2$ in Table~\ref{tab:repeat-assignment}, each method -evaluates every candidate -$j\in\mathcal{M}$. Let $\mathcal{D}^{\mathrm{train}}_j$ denote the set of -training DNA vectors available for candidate model $j$. Its gallery centroid is -\[ - \boldsymbol{\mu}_j - =\frac{1}{|\mathcal{D}^{\mathrm{train}}_j|} - \sum_{\mathbf{x}\in\mathcal{D}^{\mathrm{train}}_j}\mathbf{x}. -\] -The standard cosine similarity and cosine distance both take the query and the -candidate centroid as inputs: -\[ - S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) - =\frac{\mathbf{d}^{\mathsf T}\boldsymbol{\mu}_j} - {\lVert\mathbf{d}\rVert_2\lVert\boldsymbol{\mu}_j\rVert_2}, - \qquad - D_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j) - =1-S_{\mathrm{cos}}(\mathbf{d},\boldsymbol{\mu}_j). -\] -Thus, the cosine rule may be written equivalently as -\[ - \widehat{m}^{(\mathrm{cos})} - =\arg\min_{j\in\mathcal{M}} - D_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j) - =\arg\max_{j\in\mathcal{M}} - S_{\mathrm{cos}}(\mathbf{d}_{m,s,r},\boldsymbol{\mu}_j). -\] -No square is applied in the standard cosine distance used by the evaluation -code; each Euclidean norm already satisfies -$\lVert\mathbf{d}\rVert_2=(\sum_k d_k^2)^{1/2}$, but the cosine value itself is -not squared. The squared norm appearing below belongs instead to the RBF kernel. -For the SVM rules, the explicit class-$j$ decision scores and predictions are -\[ - f_j^{(a)}(\mathbf{d})= - \begin{cases} - \mathbf{w}_j^{\mathsf T}\mathbf{z}(\mathbf{d})+b_j, - & a=\mathrm{lin},\\[4pt] - \displaystyle - \sum_{i=1}^{n_{\mathrm{SV}}}\alpha_{ij} - \exp\!\left( - -\gamma\lVert\mathbf{z}(\mathbf{d})-\mathbf{z}(\mathbf{x}_i)\rVert_2^2 - \right)+b_j, - & a=\mathrm{rbf}. - \end{cases} - \qquad - \widehat{m}^{(a)} - =\arg\max_{j\in\mathcal{M}}f_j^{(a)}(\mathbf{d}_{m,s,r}), - \quad a\in\{\mathrm{lin},\mathrm{rbf}\}. -\] -Here $\mathbf{z}(\cdot)$ is the feature-wise standardization learned from the -training split, $(\mathbf{w}_j,b_j)$ are the linear-SVM parameters, and -$\{\mathbf{x}_i,\alpha_{ij}\}_{i=1}^{n_{\mathrm{SV}}}$ and $\gamma$ are the -RBF-SVM support vectors, effective class-$j$ coefficients, and kernel -bandwidth parameter, respectively. The prediction is correct only if -$\widehat{m}^{(a)}=m$. Thus, cosine and SVM methods -are evaluated on identical multiclass labels, queries, and candidate sets. - -\subsection{Methods} - -\begin{table}[ht] -\centering -\caption{Planned representation $\times$ classifier comparison.} -\label{tab:method-matrix} -\begin{tabular}{lll} -\toprule -Representation & Decision rule & Purpose \\ -\midrule -Original 128-D DNA & cosine nearest centroid & primary retrieval baseline \\ -Original 128-D DNA & multiclass linear SVM & classifier-only ablation \\ -Original 128-D DNA & multiclass RBF SVM & nonlinear classifier ablation \\ -Matched-$K$ mean response embedding & cosine retrieval & sample-budget baseline \\ -Response distributions & exact RBF-MMD retrieval & nonparametric diagnostic \\ -Uncompressed RFFTrace & squared Euclidean retrieval & scalable MMD approximation \\ -Compact DistDNA (projected RFFTrace) & Euclidean, linear/RBF SVM & proposed reusable vector \\ -\bottomrule -\end{tabular} -\end{table} - -The linear and RBF SVMs consume the full 128-dimensional DNA vector. The legacy -linear SVM using only negative cosine distance is not considered an exact-model -classification improvement because a monotone linear transformation of cosine -distance cannot alter closed-set candidate ranking. It remains only a binary -verification baseline. - -\subsection{Controlled comparison of old and new methods} - -Fairness is defined by holding the data and evaluation unit fixed while changing -only the representation or decision rule. Every paired comparison uses the -same model IDs, prompts, response files, decoding cell, reference/query split, -candidate set, and metric. - -\begin{table}[htbp] -\centering -\small -\caption{Controlled method-comparison matrix.} -\label{tab:controlled-method-comparison} -\begin{tabular}{p{0.28\linewidth}p{0.20\linewidth}p{0.42\linewidth}} -\toprule -Comparison & What changes & What remains fixed \\ -\midrule -Cosine vs linear/RBF SVM & Decision rule only & Same 128-D DNA, queries, -labels, split, and candidate models \\ -Mean cosine vs RBF-MMD at $K$ & Representation/distance only & Same $K$ -responses per prompt, encoder, prompts, tokens, reference and query sets \\ -Exact RBF-MMD vs RFFTrace & Kernel approximation only & Same embeddings, -bandwidth, samples, prompts, reference and query distributions \\ -RFFTrace vs compact DistDNA & Projection only & Same shared RFF map and data; -one frozen Gaussian projection for every model and setting \\ -$K=1$ point check & Kernel distance only & Same single response on each side; -explicitly not evidence for a multi-sample advantage \\ -\bottomrule -\end{tabular} -\end{table} - -Linear/RBF-SVM hyperparameters are selected only within repeat 1. The RBF-MMD -bandwidth, shared random Fourier map, and optional compact projection are -calibrated or sampled once using only reference/calibration data and then frozen. -Method differences are computed per identical held-out query and summarized by -a paired model-cluster bootstrap. Historical unknown seeds are shared by all -methods in an exploratory comparison rather than being treated as a method -difference. - -\subsection{Known-setting descriptive evaluation} - -For each held-out condition $(T,p)$ and each method $a$, the experiment reports -\[ - A_a(T,p)=\frac{1}{|\mathcal{Q}_{T,p}|} - \sum_{q\in\mathcal{Q}_{T,p}}\mathbb{I}[\widehat{m}_{a,q}=m_q]. -\] -Top-1 is primary; Top-3, Top-5, and mean reciprocal rank (MRR) are secondary. -The change from the deterministic condition is -\[ - \Delta A_a(T,p)=A_a(T,p)-A_a(0). -\] -All per-setting comparisons will use the same held-out model queries. - -\section{Unknown-temperature bounded deployment} - -The main deployment scenario assumes that the exact decoding parameters are -not provided to the classifier. For a temperature bound -\[ - \tau\in\{0.2,0.3,0.5,0.7\}, -\] -one classifier is trained using repeat 1 from every allowed setting with -$T\leq\tau$. Temperature and top-$p$ are omitted from its features. It is -then evaluated on held-out repeat 2 from all allowed settings. - -To avoid overweighting temperatures that happen to have more nominal cells, -repeats are first averaged within each $(T,p)$ cell. The primary mixed accuracy -is the equal-cell mean -\[ - A_a^{\mathrm{mix}}(\tau) - =\frac{1}{|\mathcal{S}_\tau|} - \sum_{s\in\mathcal{S}_\tau} A_a(s), - \qquad - \mathcal{S}_\tau=\{(T,p):T\leq\tau\}. -\] -The robustness guarantee is summarized by the worst observed setting -\[ - A_a^{\mathrm{worst}}(\tau) - =\min_{s\in\mathcal{S}_\tau} A_a(s). -\] -These quantities directly answer how accurately the system performs when the -decoding setting is hidden but its temperature is bounded. - -\section{Distributional and matched-budget evaluation} - -The formal method follows \path{docs/main (2).pdf}, where it is named -RFFTrace and used as the proposed distributional DNA. For prompt $q$, model -$m$, and decoding setting $s$, let -$z_{m,s,q,r}\in\mathbb{R}^{d}$ be the -\method{all-mpnet-base-v2} embedding of independent response $r$. The RBF -kernel and its shared random Fourier feature map are -\[ - k_\sigma(a,b)=\exp\!\left(-\frac{\lVert a-b\rVert_2^2}{2\sigma^2}\right), - \qquad - \psi_\sigma(z)=\sqrt{\frac{2}{D}} - \bigl[\cos(\omega_j^{\mathsf T}z+b_j)\bigr]_{j=1}^{D}, -\] -where $\omega_j\sim\mathcal{N}(0,\sigma^{-2}I_d)$ and -$b_j\sim\mathrm{Uniform}(0,2\pi)$. A single draw of all -$\{\omega_j,b_j\}$ is shared across every model, prompt, setting, and split. -The empirical distribution embedding for one prompt, followed by the complete -uncompressed RFFTrace, is -\[ - \widehat{\mu}_{m,s,q}=\frac{1}{K}\sum_{r=1}^{K} - \psi_\sigma(z_{m,s,q,r}),\qquad - \widehat{v}_{m,s}=\frac{1}{\sqrt{t}} - [\widehat{\mu}_{m,s,1};\ldots;\widehat{\mu}_{m,s,t}]\in\mathbb{R}^{tD}. -\] -Consequently, -\[ - \lVert\widehat{v}_{a}-\widehat{v}_{b}\rVert_2^2 - \approx \frac{1}{t}\sum_{q=1}^{t} - \widehat{\mathrm{MMD}}_{\sigma}^{2} - (\{z_{a,q,r}\}_{r=1}^{K},\{z_{b,q,r}\}_{r=1}^{K}), -\] -where exact MMD uses the biased/V-statistic estimator, including diagonal -terms. The compact DistDNA vector is -$\widehat{\tau}_{m,s}=A\widehat{v}_{m,s}$, with one fixed Gaussian -projection $A_{ij}\sim\mathcal{N}(0,1/L)$. This vector can be used directly -for Euclidean retrieval or as input to a linear/RBF SVM. - -The RBF bandwidth is selected by the median pairwise-distance heuristic using -only a disjoint subset of reference prompts. The calibration prompts, -evaluation prompts, encoder, bandwidth, RFF draw, projection, and all seeds are -then frozen. The implementation in -\path{src/llm_dna/distributional.py} provides exact RBF-MMD, the shared RFF -map, prompt-wise kernel means, $1/\sqrt{t}$ concatenation, and compact Gaussian -projection. The executable \path{scripts/run_rfftrace_experiment.py} uses the -real MPNet encoder and exports exact-MMD, RFFTrace, compact-DistDNA, single -cosine, and matched-$K$ mean-cosine predictions on identical data. The earlier -\path{scripts/run_rbf_mmd_pilot.py} is now explicitly a legacy hashing pilot; -it is not the formal proposed method. -The separate \path{scripts/evaluate_rfftrace_svm.py} consumes exported vectors -with explicitly disjoint train/test keys and enforces at least two independent -training vectors per model by default. It implements the downstream linear and -RBF SVM probe, but does not remove the data requirement for valid tuning. - -The planned sample-budget ablation is $K\in\{1,2,4,8\}$, subject to available -independent responses. For each $K$, the report will include Top-1, MRR, -generated tokens, reference storage, and analysis time. Single cosine always -uses one response; the primary fairness comparison at each $K$ is matched-$K$ -mean cosine versus exact MMD, RFFTrace, and compact DistDNA. Exact MMD versus -RFFTrace isolates RFF approximation error, while RFFTrace versus compact -DistDNA isolates projection loss. - -With the present two-round grid, a same-setting disjoint comparison is limited -to $K=1$: repeat 1 is the reference and repeat 2 is the query. At $K=1$, exact -RBF-MMD is a kernel point-distance check and cannot demonstrate a multi-sample -distributional advantage. A same-setting $K=2$ experiment needs four -independent response sets per prompt---for example, reference rounds 1 and 3 -and query rounds 2 and 4---with recorded, distinct generation seeds. Reusing -rounds 1 and 2 on both sides would leak the query into the gallery and drive -the correct model's self-distance artificially toward zero. - -There is one useful exception: a cross-setting $K=2$ pilot can use rounds 1 -and 2 from setting $s_a$ as the reference distribution and rounds 1 and 2 -from a different setting $s_b$ as the query distribution. The four artifacts -are then distinct even though only two repeat labels exist per setting. This -directly probes decoding-setting robustness, but the current historical grid -does not provide four explicitly recorded generation seeds for any large -setting pair, so such a result remains exploratory. The stronger same-setting -pilot will preselect a difficult 20--30-model cohort without looking at -DistDNA outcomes and generate rounds 1--4 with seeds 42001--42004. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/primary_robustness_20260813/distdna_k2_pilot_design.png} -\caption{Leakage-free same-setting $K=2$ DistDNA pilot and method-comparison -gate. Rounds 1 and 3 form the reference distribution; rounds 2 and 4 form the -held-out query distribution. All $K=2$ methods consume the same four response -sets. Single-response cosine is a lower-budget diagnostic, whereas mean-DNA -cosine is the primary matched-budget baseline.} -\label{fig:distdna-k2-pilot-design} -\end{figure} - -For the original-DNA matched baseline, let $d_{m,r}$ denote the ordinary DNA -vector from round $r$. The reference and query vectors are -\[ - \bar d_m^{\mathrm{ref}}=\tfrac12(d_{m,1}+d_{m,3}),\qquad - \bar d_m^{\mathrm{qry}}=\tfrac12(d_{m,2}+d_{m,4}), -\] -and models are ranked by cosine similarity. Exact MMD, RFFTrace, and compact -DistDNA use the identical response sets and prompts. Top-1 exact-model -retrieval is primary; MRR, Top-3/5, time, and storage are secondary. The key -paired contrast is DistDNA minus mean-DNA cosine per query model, summarized -with a model-cluster bootstrap 95\% interval. Improvement is supported only if -the lower endpoint is above zero; beating the cheaper $K=1$ cosine alone is -not sufficient. - -The proposed method will be declared better than the strongest matched-budget -baseline only if the paired Top-1 improvement has a model-cluster bootstrap -95\% confidence interval whose lower endpoint is above zero on held-out data. -MRR, worst-setting accuracy, compute, and storage are secondary checks. A -compact DistDNA+SVM comparison additionally requires enough independent -training vectors per class for nested hyperparameter selection; until those -vectors exist, Euclidean retrieval is the primary formal test and the SVM -result remains planned rather than implied. - -\section{Secondary verification and tree-aware analysis} - -\subsection{Binary verification} - -If binary pairwise verification is reported, its tasks are separated into: - -\begin{enumerate} - \item exact same-model versus unrelated; - \item lineage-only (off-diagonal related) versus unrelated; and - \item the legacy same-or-related versus unrelated task. -\end{enumerate} - -Training may downsample negative pairs or use class weighting. Testing uses all -available pairs at their natural prevalence. The main binary metrics are -precision--recall AUC, precision, recall, specificity, Matthews correlation -coefficient, false positives per query, and ROC-AUC. Balanced-test accuracy may -be shown only as an explicitly labelled diagnostic. - -\subsection{Model-tree distance} - -The Hugging Face relation exporter stores direct base-model edges in addition -to connected components. Using the undirected direct-edge graph, the shortest -tree distance is -\[ - d_{\mathrm{tree}}(i,j)= - \begin{cases} - 0, & i=j,\\ - 1, & i\text{ and }j\text{ share a direct edge},\\ - 2,3,\ldots, & \text{connected by the corresponding number of edges},\\ - \infty, & \text{disconnected}. - \end{cases} -\] - -The final report will show DNA-distance distributions for exact, direct -one-hop, indirect two-hop, indirect three-or-more-hop, same-component with -unknown hop distance, and disconnected pairs. For Top-1 predictions, cumulative -tree-aware accuracy will be reported as exact, within one hop, within two hops, -and within the same connected component. This analysis describes error severity -and does not replace exact-model accuracy as the primary endpoint. - -\section{Statistical analysis plan} - -\subsection{Uncertainty} - -Every accuracy and MRR estimate will include a 95\% confidence interval. -Resampling will preserve dependence among observations: - -\begin{itemize} - \item model IDs are resampled as clusters across methods and settings; - \item tree-related analyses use model-tree component clusters where relevant. -\end{itemize} - -Pair-level observations sharing a query model, reference model, or tree -component will not be treated as independent Bernoulli trials. With fixed -training and test roles for rounds 1 and 2, the primary confidence intervals -quantify model-level rather than generation-repeat uncertainty. - -\subsection{Paired comparisons} - -Method differences are computed on identical held-out queries. The principal -quantity is -\[ - \delta_{a,b}(T,p)=A_a(T,p)-A_b(T,p), -\] -with a paired cluster-bootstrap confidence interval. The analysis will also -test whether a method changes the temperature degradation slope, for example -with a mixed-effects logistic model of the form -\[ - \operatorname{logit}\Pr(\mathrm{correct}) - =\beta_0+\beta_1 T+\beta_2 p+\beta_3\mathrm{method} - +\beta_4(T\times\mathrm{method}) - +\beta_5(p\times\mathrm{method})+u_{\mathrm{model}}+u_{\mathrm{repeat}}. -\] -The interaction terms, rather than isolated cell differences, address whether -a method mitigates robustness loss. - -\subsection{Multiplicity and exploratory analyses} - -Top-1 accuracy and bounded mixed accuracy are primary endpoints. Top-3, Top-5, -MRR, tree-aware accuracy, encoder ablations, and projection ablations are -secondary. Any unplanned subgroup or hyperparameter analysis will be labelled -exploratory. If many method-by-setting significance tests are presented, a -false-discovery-rate correction will be applied. - -\section{Ablations and validity checks} - -The following checks are planned where artifacts and compute permit: - -\begin{enumerate} - \item projection dimensions $\{64,128,256,512\}$ and at least five projection - seeds; - \item an unprojected or prompt-level distance baseline; - \item prompt bootstrap and split-half reliability; - \item response encoder comparison including a Chinese or multilingual - encoder; - \item response-token and encoder-token length distributions and truncation - rates by setting; - \item fixed $T=0$ gallery, same-setting repeat gallery, and multi-setting - centroid gallery; - \item leave-one-setting-out evaluation for unseen decoding configurations; - \item performance by model size, architecture, and chat/base status; and - \item failure probability by model attributes and decoding setting. -\end{enumerate} - -\clearpage -\section{Small-scale demonstration report using existing artifacts} -\label{sec:small-demo} - -\subsection{Scope and split} - -To check that the analyses in Sections 6--11 are executable before the primary -sweep is complete, small exploratory demonstrations were run on July 23, 2026, -using only artifacts already present under \path{out/rand_chinese}. No new model -responses were generated. Repeat 1 was used for training/reference data and -repeat 2 for testing. Six complete stochastic cells were available: -\[ - T\in\{0.2,0.3\},\qquad p\in\{0.8,0.9,1.0\}. -\] -The $T=0.5$ and $T=0.7$ cells were excluded because they did not have a -sufficiently complete independent-repeat split. These demonstrations use -convenience cohorts determined by artifact availability, not the fixed -100-model primary cohort. They are therefore pipeline checks and exploratory -effect estimates only. - -\subsection{Executive summary} - -\noindent\fbox{\parbox{0.94\linewidth}{ -\textbf{Demo outcome.} The available artifacts support an end-to-end -demonstration of exact classification, bounded unknown-temperature evaluation, -paired model-cluster uncertainty, exact-only binary verification, a limited -distributional comparison, a limited tree-aware analysis, and a gallery -sensitivity check. Across the six complete cells, the pooled linear SVM had the -highest equal-cell Top-1 (0.722), followed by RBF SVM (0.688) and cosine -retrieval (0.621). The hardest observed cell was $(T,p)=(0.3,1.0)$. These are -exploratory results from availability-selected cohorts and must not be read as -the outcome of the 100-model primary experiment. -}} - -\medskip -Table~\ref{tab:demo-coverage} maps each planned protocol component to what -could actually be demonstrated. ``Partial'' means that the code path ran but -the available cohort or repeat structure could not exercise the full planned -analysis. - -\begin{table}[htbp] -\centering -\caption{Coverage of Sections 6--11 by the small-scale demonstration.} -\label{tab:demo-coverage} -\begin{tabular}{p{0.13\linewidth}p{0.35\linewidth}p{0.23\linewidth}p{0.15\linewidth}} -\toprule -Protocol section & Demonstrated analysis & Available scale & Status \\ -\midrule -6: Exact task - & Cosine centroid, linear SVM, and RBF SVM on identical queries - & Six cells; 84 common models & Complete demo \\ -7: Bounded deployment - & Equal-cell mixed and worst-setting accuracy for $\tau=0.2,0.3$ - & 89 and 84 models & Complete demo \\ -8: Distributional - & Single cosine, mean cosine, and exact RBF-MMD - & Eight models; 20 prompts; at most two repeats & Partial \\ -9: Verification - & Exact-only linear probe at natural pair prevalence - & 95 models; 9,025 pairs per direction & Complete demo \\ -9: Tree-aware - & Exact versus disconnected error and distance summaries - & 95 models; no related pairs & Partial \\ -10: Statistics - & Model-cluster CIs and paired method differences - & One held-out repeat per cell & Partial \\ -11: Validity - & Fixed-$T=0$ versus pooled-gallery diagnostic - & 86 versus 84 models & Partial \\ -\bottomrule -\end{tabular} -\end{table} - -\subsection{Exact classification and bounded deployment demo} - -For $\tau=0.2$, the common cohort contained 89 models and the training pool -contained the three repeat-1 cells at $T=0.2$. For $\tau=0.3$, requiring all six -cells reduced the common cohort to 84 models. The classifier was not given -temperature or top-$p$. Table~\ref{tab:demo-per-setting} reports held-out -repeat-2 Top-1 accuracy for the $\tau=0.3$ pooled training experiment. - -\begin{table}[ht] -\centering -\caption{Exploratory held-out Top-1 accuracy with 84 common models.} -\label{tab:demo-per-setting} -\begin{tabular}{ccrrr} -\toprule -$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM \\ -\midrule -0.2 & 0.8 & 0.643 & 0.821 & 0.702 \\ -0.2 & 0.9 & 0.726 & 0.845 & 0.762 \\ -0.2 & 1.0 & 0.595 & 0.643 & 0.643 \\ -0.3 & 0.8 & 0.774 & 0.869 & 0.857 \\ -0.3 & 0.9 & 0.690 & 0.786 & 0.762 \\ -0.3 & 1.0 & 0.298 & 0.369 & 0.405 \\ -\bottomrule -\end{tabular} -\end{table} - -Figure~\ref{fig:demo-exact-heatmaps} makes the setting-level pattern explicit. -The linear SVM led in four cells and tied in one, while the RBF SVM was highest -at $(0.3,1.0)$. All methods dropped sharply in that cell, so the demo does not -support a general monotone temperature effect independent of top-$p$. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/small_scale_section_6_11/exact_top1_heatmaps.png} -\caption{Frozen July 23 exploratory exact-model Top-1 by method and decoding -cell. Darker cells indicate higher accuracy. The equal-cell means correspond -to the $\tau=0.3$ pooled-training experiment; this is not the live primary -grid result.} -\label{fig:demo-exact-heatmaps} -\end{figure} - -The equal-cell bounded summaries are shown in -Table~\ref{tab:demo-bounded}. Confidence intervals resample model IDs as -clusters across the included cells. - -\begin{table}[ht] -\centering -\caption{Exploratory bounded-deployment Top-1 summaries.} -\label{tab:demo-bounded} -\begin{tabular}{clrrr} -\toprule -$\tau$ & Method & Mixed accuracy (95\% CI) & Worst cell & Models \\ -\midrule -0.2 & Cosine centroid & 0.596 (0.528, 0.667) & 0.562 & 89 \\ - & Linear SVM & 0.682 (0.614, 0.745) & 0.652 & 89 \\ - & RBF SVM & 0.629 (0.562, 0.700) & 0.584 & 89 \\ -\addlinespace -0.3 & Cosine centroid & 0.621 (0.554, 0.688) & 0.298 & 84 \\ - & Linear SVM & 0.722 (0.675, 0.772) & 0.369 & 84 \\ - & RBF SVM & 0.688 (0.633, 0.742) & 0.405 & 84 \\ -\bottomrule -\end{tabular} -\end{table} - -On identical queries, the paired model-cluster bootstrap difference between -linear SVM and cosine was $0.086$ (95\% CI $0.037$ to $0.135$) at -$\tau=0.2$ and $0.101$ (95\% CI $0.058$ to $0.147$) at $\tau=0.3$. The -corresponding RBF-SVM differences were $0.034$ (95\% CI $0.004$ to $0.071$) -and $0.067$ (95\% CI $0.026$ to $0.111$). These intervals quantify only this -availability-selected demo and do not test the primary hypotheses. - -Figure~\ref{fig:demo-bounded-paired} combines three views that should remain -distinct in interpretation: bounded performance, paired within-query method -gains, and the unpaired gallery diagnostic. In particular, the fixed-gallery -bars are not a controlled estimate of gallery effects because the gallery -cohort and training budget differ from the pooled condition. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/small_scale_section_6_11/bounded_paired_gallery.png} -\caption{Frozen July 23 bounded-deployment and sensitivity summary. Panel A -shows equal-cell mixed accuracy with model-cluster 95\% intervals and the worst -observed cell. Panel B shows paired Top-1 differences relative to cosine. -Panel C is an explicitly unpaired fixed-gallery versus pooled-gallery -diagnostic; none of these panels is a live primary-grid result.} -\label{fig:demo-bounded-paired} -\end{figure} - -\subsection{Distributional and matched-budget demo} - -The exact RBF-MMD pilot ran successfully on eight lexicographically selected -models, 20 common evaluation prompts, and two response repeats, using -$T=0.2,p=0.8$ as the reference setting. Single-sample cosine, mean-cosine, and -exact RBF-MMD each achieved Top-1 and MRR of 1.000 for the same-setting -comparison and for cross-setting queries at $(0.3,0.8)$ and $(0.3,0.9)$. The -same-setting split has only one repeat on each side, whereas the cross-setting -comparisons pool both repeats per model on each side. This is a successful -end-to-end demonstration, but the tiny cohort saturated all methods and -therefore provides no evidence that the distributional representation is -better. The available two repeats also do not support the planned -$K\in\{1,2,4,8\}$ ablation. - -\begin{table}[htbp] -\centering -\caption{Distributional pilot Top-1 (MRR was also 1.000 in every entry).} -\label{tab:demo-distributional} -\begin{tabular}{lrrr} -\toprule -Query comparison & Single cosine & Mean cosine & Exact RBF-MMD \\ -\midrule -Same $(0.2,0.8)$ & 1.000 & 1.000 & 1.000 \\ -Cross $(0.2,0.8)\rightarrow(0.3,0.8)$ & 1.000 & 1.000 & 1.000 \\ -Cross $(0.2,0.8)\rightarrow(0.3,0.9)$ & 1.000 & 1.000 & 1.000 \\ -\bottomrule -\end{tabular} -\end{table} - -This complete saturation is itself a useful demo diagnostic: a larger and more -difficult cohort is required before representation differences can be -estimated. It would be inappropriate to select a method from these pilot -numbers. - -\subsection{Binary verification and tree-aware demo} - -An exact-only linear verification probe was trained on one $T=0.2,p=0.8$ -repeat and tested on the other, in both directions, over 95 common models. -Testing retained all 8,930 negative pairs per direction, giving a natural -positive prevalence of $1/95$. Results are in -Table~\ref{tab:demo-verification}. - -\begin{table}[ht] -\centering -\caption{Exploratory exact-only binary verification at natural prevalence.} -\label{tab:demo-verification} -\begin{tabular}{lrrrrrr} -\toprule -Test repeat & PR-AUC & ROC-AUC & Precision & Recall & Specificity & MCC \\ -\midrule -1 & 0.334 & 0.832 & 0.049 & 0.726 & 0.851 & 0.163 \\ -2 & 0.373 & 0.869 & 0.045 & 0.747 & 0.831 & 0.155 \\ -\bottomrule -\end{tabular} -\end{table} - -Despite the apparently strong ROC-AUC, the probe produced 1,329 and 1,511 -false positives in the two directions, respectively. The resulting precision -near 5\% demonstrates why the primary verification report emphasizes PR-AUC, -MCC, specificity, and false positives per query rather than balanced accuracy -alone. - -The tree-aware cosine demo used repeat 1 at $(0.2,0.8)$ as the gallery and 95 -common models. Exact Top-1 was 0.526 for the same-setting repeat-2 query and -0.379 for the $(0.3,0.8)$ repeat-2 query. All 104 combined errors were between -models disconnected in the exported relation graph, so within-one-hop, -within-two-hop, and same-component accuracy did not exceed exact accuracy. -Mean cosine distance was 0.445 versus 0.929 for exact and disconnected pairs -in the same-setting comparison, and 0.566 versus 0.929 in the cross-setting -comparison. No direct or indirect related pair occurred within this -availability-selected cohort, so lineage-error severity could not be -demonstrated. - -Figure~\ref{fig:demo-verification-tree} emphasizes two practical limitations. -First, ROC-AUC obscures the false-positive burden when only one of 95 candidate -pairs is positive. Second, the tree-aware demo separates exact and disconnected -pairs but cannot validate the intended one-hop and indirect strata because no -such pair occurred in the common cohort. - -\begin{figure}[htbp] -\centering -\includegraphics[width=\linewidth] - {docs/figures/small_scale_section_6_11/verification_tree_diagnostics.png} -\caption{Frozen July 23 verification and tree-aware diagnostics. Panels A--B -show binary metrics and false positives per query at natural prevalence. -Panel C compares mean cosine distance for exact and disconnected pairs and -annotates exact retrieval accuracy; this is not a live primary-grid result.} -\label{fig:demo-verification-tree} -\end{figure} - -\subsection{Validity-check demo and deferred items} - -As a limited gallery check, a fixed $T=0$ repeat-1 gallery was compared with -the multi-setting pooled gallery on the same six query settings. Equal-cell -mixed Top-1 with the fixed gallery was 0.403 for cosine, 0.310 for linear SVM, -and 0.341 for RBF SVM over 86 common models. The corresponding pooled-gallery -values were 0.621, 0.722, and 0.688 over 84 common models. Because the cohorts -and number of training examples differ, this is a diagnostic rather than a -paired method comparison; it merely confirms that gallery construction is -consequential. - -The remaining Section 10--11 analyses were deferred. Two temperatures and one -held-out repeat are inadequate for the planned mixed-effects degradation model. -Alternative projection dimensions/seeds, encoder comparisons, reliable -encoder-token truncation counts, $K=4$ and $K=8$, lineage-only verification, -and related-pair tree strata are not supported by the currently complete -artifacts. Leave-one-setting-out and subgroup analyses are feasible later but -were omitted to keep this demonstration deliberately small. - -\subsection{Demo conclusions and primary-study handoff} - -The demonstration supports the following limited conclusions: - -\begin{itemize} - \item The exact-classification and bounded-deployment pipelines operate on - disjoint generation repeats and produce aligned per-query scores for - all three classifiers. - \item Within this demo, the linear SVM had the highest mixed Top-1 at both - temperature bounds, and its paired advantage over cosine was positive. - \item Robustness was setting-dependent: $(0.3,1.0)$ was substantially harder - than the other five cells for every method. - \item Natural-prevalence verification exposed a large operational - false-positive burden that ROC-AUC alone would conceal. - \item The distributional and tree-aware code paths run, but the present data - are respectively too easy and too sparse in related pairs to compare - the intended methods. - \item Gallery construction materially changes the diagnostic accuracies, so - the primary report must keep gallery definitions and training budgets - fixed in paired comparisons. -\end{itemize} - -Before a final primary claim is made, the study still requires recovery to at -least the preregistered 80-model strict common cohort, a duplicate/independence -audit of the two existing rounds, and the frozen paired evaluation on repeat 2. -The current two-round design supports the exact-model cosine/SVM comparison. -It also supports exploratory cross-setting $K=2$ comparisons because each of -two distinct settings contributes two different artifacts. It does not support -a leakage-free same-setting multi-sample advantage claim: that test needs two -independent reference responses and two independent query responses per -prompt, while $K=4$ and $K=8$ require still more generation. The small-scale -numbers remain exploratory until rerun on the frozen primary cohort. - -Machine-readable outputs for the July demonstration are stored under -\path{out/small_scale_section_6_11}. The exact-classification, verification, -distributional, fixed-gallery, and tree-aware subdirectories contain the -selected cohorts, per-query predictions where applicable, aggregate CSV files, -and JSON summaries. Those three result figures are frozen July 23 artifacts; -they have not been relabelled as current primary results. The new live-status -and DistDNA-design figures are regenerated without plotting-library -dependencies using: - -\begin{lstlisting}[language=bash] -venv/bin/python scripts/plot_primary_status_and_distdna_design.py -\end{lstlisting} - -\clearpage - -\section{Execution and resource plan} - -The completed first backfill sequence used -\path{scripts/resume_primary_robustness_grid.sh}, the exact validated-100 -cohort, and GPUs 0, 1, 2, and 4. It preserved successful historical artifacts -and queued only missing or failed model--cell--round keys. The sequence ended -on August 5 with 72 terminal failures. A failure-specific pass using -\path{scripts/recover_primary_robustness_failures_3090.sh} was launched on the -current A100 host on August 13 using GPUs 2--5. It writes to -\path{out/primary_robustness_recovery_20260810} and preserves the original -artifacts. Its validated generation-seed rule is seed 42 for both $T=0.5$ -rounds, and otherwise seed 42001 for repeat 1 and 42002 for repeat 2. Thus the -cell active at the 13:31 snapshot, $T=0.5,p=0.8$, repeat 2, uses seed 42. The August 5 -tables remain frozen provenance; Figure~\ref{fig:live-recovery-aug13} is the -later live snapshot, and all final counts must be refreshed after recovery -exits. - -Twenty-four affected models lack only one slot. Restoring any 21 of these -models across their missing cells is the minimum theoretical route from 59 to -the preregistered 80-model strict common cohort. Full 100-model completion -remains the target, but this prioritized recovery makes the analysis gate -explicit. - -The expected completion schedule below is measured from the August 5 status -snapshot and assumes continued access to the existing GPUs. It separates the -minimum analysis gate from optional multi-sample generation so that feasibility -is visible rather than implied. - -\begin{table}[htbp] -\centering -\small -\caption{Expected completion schedule from the August 5 status snapshot.} -\label{tab:completion-schedule} -\begin{tabular}{p{0.38\linewidth}p{0.20\linewidth}p{0.32\linewidth}} -\toprule -Milestone & Expected time & Exit condition \\ -\midrule -Diagnose 72 terminal failures & 1 working day & Failure-specific repair list \\ -Targeted recovery and audit & 2--3 working days & Strict common $N\geq80$ and independent rounds \\ -Frozen cosine/linear/RBF evaluation & 1 working day & Paired tables and 95\% model-cluster CIs \\ -Report integration and review & 1 working day & Primary report ready for supervisor review \\ -Optional fair $K=2$ RBF-MMD generation & 3--5 additional working days & Two independent samples on both sides \\ -\bottomrule -\end{tabular} -\end{table} - -Therefore, the controlled cosine/SVM primary update is expected within five to -six working days if recovery reaches $N\geq80$. The multi-sample proposed-method -claim has a separate, later completion date and is not placed on the critical -path for the classifier comparison. - -Transient failures are retried and recorded. Progress reports include planned, -successful, failed, and missing models plus failure-status counts. Missing -cells are never represented as zero accuracy and curves are not interpolated -through absent cells. - -\section{Reproducibility and provenance} - -The experiment will preserve: - -\begin{itemize} - \item exact cohort file and SHA-256 hash; - \item prompt dataset and seed; - \item model ID and available model metadata; - \item temperature, top-$p$, sampling flag, generation seed, and token limit; - \item response files and independent repeat suffixes; - \item sentence encoder, DNA dimension, projection method, and projection seed; - \item run manifests, status journals, failure summaries, predictions, and - machine-readable metric tables; and - \item the evaluation code and report version used to produce final figures. -\end{itemize} - -For clarity, the August 5 completion snapshot has the following generation-seed -provenance: - -\begin{table}[ht] -\centering -\caption{Generation-seed provenance among the 2,528 successful artifacts in -the pre-recovery snapshot.} -\begin{tabular}{lr} -\toprule -Provenance & Successful artifacts \\ -\midrule -Explicit generation seed recorded & 572 \\ -Historical manifest without a separate recorded generation seed & 1,956 \\ -\midrule -Total artifact coverage & 2,528 \\ -\bottomrule -\end{tabular} -\end{table} - -This table answers a counting issue directly: the 2,528 numerator does not -exclude unknown-seed history. Any seed-clean analysis must construct and report -its own denominator instead of reusing 2,600. - -The unsuffixed historical gallery will not be used for the primary causal -interpretation because its temperature and top-$p$ were not recorded. It may be -retained as a clearly labelled legacy reference-sensitivity analysis. - -\section{Planned results} - -\subsection{Cohort and completion table} - -\begin{table}[ht] -\centering -\caption{Frozen August 5 pre-recovery completion across both planned rounds.} -\begin{tabular}{lrrr} -\toprule -Condition & Required & Successful & Remaining \\ -\midrule -$T=0$ & 200 & 200 & 0 \\ -$T=0.2$, all $p$ & 600 & 597 & 3 \\ -$T=0.3$, all $p$ & 600 & 573 & 27 \\ -$T=0.5$, all $p$ & 600 & 570 & 30 \\ -$T=0.7$, all $p$ & 600 & 588 & 12 \\ -\midrule -Total & 2,600 & 2,528 & 72 \\ -\bottomrule -\end{tabular} -\end{table} - -The strict common cohort across all 26 runs was 59 in this frozen snapshot. -Setting-specific paired cohorts are reported in the earlier status discussion; -therefore 97.2\% slot completion must not be described as a 97-model -complete-case cohort. - -\subsection{Per-setting exact classification} - -\begin{longtable}{ccrrrr} -\caption{Available held-out exact-model Top-1. Starred entries are exploratory -84-model results, not final validated-100 estimates.}\label{tab:per-setting}\\ -\toprule -$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ -\midrule -\endfirsthead -\toprule -$T$ & Top-$p$ & Cosine & Linear SVM & RBF SVM & Proposed \\ -\midrule -\endhead -0.0 & N/A & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.2 & 0.8 & \demovalue{0.643} & \demovalue{0.821} & \demovalue{0.702} & \todoresult \\ -0.2 & 0.9 & \demovalue{0.726} & \demovalue{0.845} & \demovalue{0.762} & \todoresult \\ -0.2 & 1.0 & \demovalue{0.595} & \demovalue{0.643} & \demovalue{0.643} & \todoresult \\ -0.3 & 0.8 & \demovalue{0.774} & \demovalue{0.869} & \demovalue{0.857} & \todoresult \\ -0.3 & 0.9 & \demovalue{0.690} & \demovalue{0.786} & \demovalue{0.762} & \todoresult \\ -0.3 & 1.0 & \demovalue{0.298} & \demovalue{0.369} & \demovalue{0.405} & \todoresult \\ -0.5 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.5 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.5 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.7 & 0.8 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.7 & 0.9 & \todoresult & \todoresult & \todoresult & \todoresult \\ -0.7 & 1.0 & \todoresult & \todoresult & \todoresult & \todoresult \\ -\bottomrule -\end{longtable} - -\noindent\textsuperscript{*}Repeat 1 was used for pooled training/reference -construction and repeat 2 for testing on the same 84 availability-selected -models. The Proposed column remains pending: a same-query $K=1$ RBF-MMD check -is feasible, but a multi-sample claim requires additional matched responses. - -\subsection{Bounded unknown-temperature deployment} - -\begin{table}[ht] -\centering -\caption{Temperature-agnostic deployment results. The classifier is not given -temperature or top-$p$.} -\begin{tabular}{clrrr} -\toprule -$\tau$ & Method & Mixed accuracy & Worst setting & MRR \\ -\midrule -0.2 & Original cosine & \demovalue{0.596} & \demovalue{0.562} & \demovalue{0.672} \\ - & Linear SVM & \demovalue{0.682} & \demovalue{0.652} & \demovalue{0.744} \\ - & RBF SVM & \demovalue{0.629} & \demovalue{0.584} & \demovalue{0.710} \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\addlinespace -0.3 & Original cosine & \demovalue{0.621} & \demovalue{0.298} & \demovalue{0.698} \\ - & Linear SVM & \demovalue{0.722} & \demovalue{0.369} & \demovalue{0.777} \\ - & RBF SVM & \demovalue{0.688} & \demovalue{0.405} & \demovalue{0.754} \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\addlinespace -0.5 & Original cosine & \todoresult & \todoresult & \todoresult \\ - & Linear SVM & \todoresult & \todoresult & \todoresult \\ - & RBF SVM & \todoresult & \todoresult & \todoresult \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\addlinespace -0.7 & Original cosine & \todoresult & \todoresult & \todoresult \\ - & Linear SVM & \todoresult & \todoresult & \todoresult \\ - & RBF SVM & \todoresult & \todoresult & \todoresult \\ - & Proposed & \todoresult & \todoresult & \todoresult \\ -\bottomrule -\end{tabular} -\end{table} - -\subsection{Planned figures} - -The final report will contain: - -\begin{enumerate} - \item exact Top-1 versus temperature, faceted by top-$p$, with one curve per - representation/classifier and 95\% confidence bands; - \item accuracy loss relative to $T=0$ and estimated degradation slopes; - \item mixed and worst-setting accuracy versus temperature bound $\tau$; - \item stacked tree-aware Top-1 error severity versus temperature; and - \item accuracy versus response-sample/token budget for the mean and - distributional representations. -\end{enumerate} - -\section{Planned interpretation} - -The final conclusions will be limited to what the completed protocol supports: - -\begin{itemize} - \item the effect of query-side decoding conditions relative to explicit, - independently generated galleries; - \item classifier mitigation on the same exact-model task; - \item representation gains under matched budgets; and - \item expected and worst observed performance when decoding parameters are - hidden but temperature is bounded. -\end{itemize} - -No lineage-robustness conclusion will be based on aggregate positives dominated -by same-model pairs. No method will be declared superior from a point estimate -alone, and no causal temperature conclusion will use the historical -unknown-setting gallery. - -\appendix - -\section{Execution commands} - -Build or verify the fixed cohort and refresh the live grid: - -\begin{lstlisting}[language=bash] -python3 scripts/build_validated100_config.py \ - --manifest out/top_p_control_t05_validated100_20260719/\ -top_p_control_t05_t05_p06_r2/manifest.json \ - --output configs/rand_chinese_validated100_latest.jsonl -python3 scripts/build_primary_run_grid.py -\end{lstlisting} - -The completed missing/failed backfill sequence was launched with: - -\begin{lstlisting}[language=bash] -env GPUS=0,1,2,4 MAX_CONCURRENT_GPUS=4 \ - bash scripts/resume_primary_robustness_grid.sh -\end{lstlisting} - -After targeted recovery and the independence audit, run the exact-model and -tree-aware analyses: - -\begin{lstlisting}[language=bash] -bash scripts/run_primary_analysis.sh -\end{lstlisting} - -Run the implemented formal $K=1$ RFFTrace diagnostic after the common cohort -reaches the minimum gate: - -\begin{lstlisting}[language=bash] -env ENCODER_DEVICE=cuda:0 bash scripts/run_primary_rfftrace_k1.sh -\end{lstlisting} - -The same Python entry point supports a future valid $K=2$ experiment by passing -\method{--reference-repeats 1,3 --query-repeats 2,4 ---samples-per-side 2} after independent rounds 3 and 4 exist. It must not be -run by reusing the same response twice. - -The primary analysis outputs are written beneath -\path{out/primary_robustness_100_analysis}. The exact-model evaluator produces -per-setting metrics, per-query predictions, bounded-deployment tables, cohort -metadata, and JSON summaries. - -\section{Implementation map} - -\begin{longtable}{p{0.34\linewidth}p{0.58\linewidth}} -\toprule -File & Purpose \\ -\midrule -\endfirsthead -\toprule -File & Purpose \\ -\midrule -\endhead -\path{scripts/run_primary_robustness_experiment.sh} - & Launch the 100-model, 13-condition, two-round extraction sweep. \\ -\path{scripts/resume_primary_robustness_grid.sh} - & Resume missing and failed validated-100 grid cells without regenerating - successful artifacts. \\ -\path{scripts/build_primary_run_grid.py} - & Produce slot-completion and repeat-level cohort audits. \\ -\path{scripts/run_primary_analysis.sh} - & Construct the fixed train/test repeat split and run primary analyses. \\ -\path{scripts/evaluate_exact_dna_classification.py} - & Compare cosine centroids and multiclass SVMs on exact identity; produce - bounded unknown-temperature results and confidence intervals. \\ -\path{scripts/analyze_tree_aware_errors.py} - & Produce hierarchical retrieval and tree-distance error summaries. \\ -\path{scripts/evaluate_dna_probe_sweep.py} - & Run legacy and separated binary verification tasks using all test - negatives by default. \\ -\path{scripts/run_rbf_mmd_pilot.py} - & Legacy character-hashing pilot for single-sample, mean-cosine, and exact - RBF-MMD retrieval; not the formal DistDNA implementation. \\ -\path{src/llm_dna/distributional.py} - & Implement exact biased RBF-MMD, shared random Fourier features, prompt-wise - RFFTrace vectors, and optional compact Gaussian projection. \\ -\path{scripts/run_rfftrace_experiment.py} - & Run formal MPNet-based, calibration-separated, matched-budget exact-MMD, - RFFTrace, and compact-DistDNA retrieval and export reusable vectors. \\ -\path{scripts/run_primary_rfftrace_k1.sh} - & Freeze the primary-cohort $K=1$ same/cross-setting diagnostic command. \\ -\path{scripts/evaluate_rfftrace_svm.py} - & Evaluate exported RFFTrace or compact-DistDNA vectors with held-out linear - and RBF SVMs while enforcing disjoint vector keys. \\ -\path{scripts/plot_primary_status_and_distdna_design.py} - & Regenerate the timestamped live grid/seed snapshot and leakage-free - same-setting $K=2$ DistDNA pilot design figures. \\ -\path{tests/test_distributional.py} - & Check kernel geometry, MMD/RFF convergence, scaling, projection, seeds, and - package exports. \\ -\path{tests/test_rbf_mmd_pilot.py} - & Preserve legacy pilot MMD, repeat-split, and all-record prompt-intersection - regression checks. \\ -\path{tests/test_rfftrace_experiment.py} - & Check response discovery, actual model IDs, common prompts, required - repeats, and disjoint prompt splitting. \\ -\path{tests/test_rfftrace_svm.py} - & Check SVM split leakage rejection and held-out multiclass evaluation. \\ -\path{scripts/export_hf_model_tree.py} - & Export direct parent/base edges and connected components. \\ -\path{scripts/report_sweep_progress.py} - & Report completion, missing models, and failure-status counts. \\ -\bottomrule -\end{longtable} - -\end{document} diff --git a/scripts/plot_primary_status_and_distdna_design.py b/scripts/plot_primary_status_and_distdna_design.py index dc870f4..adfc11e 100644 --- a/scripts/plot_primary_status_and_distdna_design.py +++ b/scripts/plot_primary_status_and_distdna_design.py @@ -222,7 +222,7 @@ def draw_pilot_design(output: Path) -> None: image = Image.new("RGB", (1800, 1000), "white") draw = ImageDraw.Draw(image) draw.text((70, 40), "Same-setting K=2 DistDNA pilot: leakage-free, matched-budget comparison", font=font(39, True), fill="#0f172a") - draw.text((70, 92), "Preselect a difficult 20–30 model cohort; use identical prompts, setting, encoder, and token budget.", font=font(23), fill="#475569") + draw.text((70, 92), "Start with a 10-model debugging pilot; expand to 20–30 models for evidence after the pipeline is stable.", font=font(23), fill="#475569") rounded_panel(draw, (55, 155, 855, 565)) draw.text((90, 185), "A. Four independent response sets", font=font(29, True), fill="#0f172a") diff --git a/scripts/render_aug13_progress_report.sh b/scripts/render_aug13_progress_report.sh new file mode 100755 index 0000000..1e316fd --- /dev/null +++ b/scripts/render_aug13_progress_report.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +SOURCE="$ROOT/docs/primary_robustness_experiment_full_updated_2026-08-13.html" +OUTPUT="$ROOT/docs/primary_robustness_experiment_full_updated_2026-08-13.pdf" + +chromium \ + --headless \ + --no-sandbox \ + --disable-gpu \ + --allow-file-access-from-files \ + --no-pdf-header-footer \ + --print-to-pdf="$OUTPUT" \ + "file://$SOURCE" + +echo "Rendered $OUTPUT" From 2cc274bad85aa02d453c477ccf914894c94a09a7 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Thu, 13 Aug 2026 14:24:07 +0800 Subject: [PATCH 27/28] Add LaTeX report and decoding surface analysis --- .../bounded_decoding_relationship.png | Bin 0 -> 74049 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LLM-DNA · Primary robustness study
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Weekly Progress Update

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Recovery status, seed provenance, and the four-seed DistDNA validation plan
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Reporting period: 5–13 August 2026 · Snapshot: 13 August, 13:56 SGT
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- -

This note updates the 5 August report. It records only what changed during the week, what the current data can support, and the next experiment needed to test DistDNA fairly. The full experimental protocol and previously reported formulas remain in the earlier report and are not repeated here.

- -
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2,552
successful artifacts
of 2,600 planned
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+24
failed slots recovered
since 5 August
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68
models complete across
all 26 planned runs
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48
slots still lacking a
successful artifact
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- -

What changed this week

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  • A failure-specific recovery pass was launched on the current A100 host and increased grid coverage from 97.2% to 98.2%.
    • -
  • The strict all-run cohort increased from 59 to 68 models. Recovery is still active, so this is a timestamped interim count.
    • -
  • Seed provenance was separated from artifact coverage: 1,956 successful slots still rely only on historical runs with no recorded generation seed.
    • -
  • The repository now contains the formal RFFTrace/DistDNA implementation, exact MMD baseline, matched-sample evaluator, compact projection, and tests.
    • -
  • A leakage-free four-seed pilot is specified so that DistDNA and cosine baselines consume exactly the same generated responses.
    • -
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Recommended decision. First run a 10-model, one-setting, four-seed debugging pilot. Use it to validate the data split, implementation, and effect direction. Then start the 100-model extension at the same setting. Expand to all 12 stochastic settings only after the pilot is stable.

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- -
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Current job
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Recovery was running at report build time. The active cell had moved to T=0.5, top-p=0.9, repeat 1, using generation seed 42.
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Primary LLM-DNA robustness experiment · 13 August 20261
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1. Recovery progress since 5 August

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Measure5 Aug frozen snapshot13 Aug live snapshotChange
Successful artifacts2,528 / 2,6002,552 / 2,600+24
Artifact coverage97.2%98.2%+1.0 pp
Slots without success7248−24
Strict all-26-run cohort59 / 10068 / 100+9 models
Explicit-seed successes572596+24
Historical unknown-seed only1,9561,956unchanged
- - Live primary grid recovery status -

Figure 1. Live grid snapshot at 13:56 SGT on 13 August. Green recovery gains are real successful artifacts, but the seed-provenance panel shows why high coverage is not yet equivalent to a four-seed DistDNA dataset.

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Interpretation. The recovery pass repairs the preregistered two-round grid. It should finish before a final primary-grid analysis is frozen. It does not, by itself, create four independent samples per model and setting.

-
- -

Settings that remain most incomplete

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The largest remaining gap is T=0.5, top-p=1.0 with 176/200 successful artifacts. All other stochastic settings are between 195/200 and 200/200. Failures are distributed across models, which is why the strict common cohort remains lower than the overall coverage percentage.

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Weekly progress update · Recovery2
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- -
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2. Seed provenance: what is reusable

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The response-generation seed and the DNA projection seed are different variables. This report concerns the response-generation seed. The projection seed remains fixed and does not create an independent response sample.

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Artifact sourceRecorded generation seedUse in current analysisUse in four-seed DistDNA
Stochastic T=0.2, 0.3, 0.7 backfillsr1: 42001
r2: 42002		ValidReusable as independent seeds
T=0.5 control and recoveryr1: 42
r2: 42		Valid for coverageReplace for seed-clean study
Historical sweepNot recordedValid for matched method comparisons on the same responsesReplace; independence cannot be established
New four-seed extension42001, 42002, 42003, 42004ValidPrimary DistDNA evidence
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The current 98.2% grid supports

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    -
  • Two-round cosine, linear SVM, and RBF-SVM comparisons.
    • -
  • Temperature/top-p robustness analysis on a fixed matched cohort.
    • -
  • Exploratory representation comparisons when every method reads identical artifacts.
    • -
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It does not yet support

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  • A leakage-free same-setting K=2 reference and K=2 query split.
    • -
  • A claim that seed 42 duplicates or unknown-seed runs are independent samples.
    • -
  • A fair DistDNA advantage claim based only on the easier single-vector cosine baseline.
    • -
-
-
- -

Current seed-clean inventory

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Across the 12 stochastic settings, the live repository contains 320 unique successful model–setting artifacts with explicit seed 42001 or 42002. A further 176 successful stochastic artifacts use seed 42 and are not counted as seed-clean four-seed evidence. The 100 deterministic artifacts are also excluded because repeated sampling is inactive at T=0.

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-

Operational rule for the new queue: skip an artifact only when the same model, decoding setting, prompt set, token budget, and exact generation seed already have a successful output. Unknown-seed and seed-42 artifacts remain in the historical grid but do not satisfy a 42001–42004 slot.

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Weekly progress update · Seed provenance3
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- -
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3. The immediate DistDNA pilot

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The pilot asks one narrow question: when the generated-response budget is matched, does a distributional representation identify the model more reliably than averaging ordinary DNA vectors and using cosine retrieval?

- - Four-seed DistDNA pilot design -

Figure 2. Four independent response sets at one fixed decoding setting. Seeds 42001 and 42003 form the reference distribution; seeds 42002 and 42004 form the held-out query distribution. Every K=2 method reads the same four sets.

- -

Recommended first setting

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-

T=0.7, top-p=1.0. This is deliberately stochastic and therefore more likely to reveal whether distributional information helps. It also has broad explicit seed-42002 coverage in the current grid, reducing the amount of new generation needed for the later 100-model extension.

-
- -

Why four seeds are needed

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Two seeds are used to estimate the reference distribution and two different seeds estimate the query distribution. Reusing a response set on both sides would make a model artificially close to itself. Four seeds are therefore the minimum clean design for a K=2 versus K=2 same-setting comparison.

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What the 10-model result means

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The 10-model run is a debugging and effect-direction pilot. It can reveal implementation errors, leakage, saturation, or an obviously unhelpful representation. It is not a final statistical claim: with ten queries, one corrected model changes Top-1 by ten percentage points. If the pipeline behaves sensibly, expand to 20–30 deliberately confusable models before interpreting confidence intervals.

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Weekly progress update · DistDNA pilot design4
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- -
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4. Proposed 10-model small-scale cohort

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All candidates below are already members of the fixed 100-model cohort and are advertised below 0.8B parameters. Selection is based on scale and family structure, not on DistDNA outcomes. The repeated Pythia and Qwen families intentionally make the retrieval task less likely to saturate.

- - - - - - - - - - - - - - - -
#ModelScaleRole in pilot
1ibm-granite/granite-4.0-h-350m-base≈0.35BGranite baseline
2EleutherAI/pythia-410m-seed1≈0.41BConfusable same-family trio
3EleutherAI/pythia-410m-seed2≈0.41B
4EleutherAI/pythia-410m-seed3≈0.41B
5unsloth/Qwen2.5-0.5B≈0.50BRelated Qwen variants
6unsloth/Qwen2.5-0.5B-Instruct≈0.50B
7Ihor/Text2Graph-R1-Qwen2.5-0.5b≈0.50B
8robowaifudev/megatron-gpt2-345m≈0.35BGPT-2 architecture
9state-spaces/mamba-370m-hf≈0.37BState-space architecture
10unsloth/Qwen3-0.6B-Base≈0.60BNewer Qwen family
- -
-

Pre-launch check. Parameter labels above are model-name/model-card scale labels. The runner should still validate configuration compatibility, non-empty generation, tokenizer access, and cached model availability before the four-seed queue starts.

-
- -

Frozen generation contract

- - - - - - - - - -
Decoding settingT=0.7, top-p=1.0, sampling enabled
Seeds42001, 42002, 42003, 42004
PromptsThe same 100 classical-Chinese prompts already used by the primary grid
Response budgetThe same maximum generated-token limit as the primary grid
RepresentationThe same response encoder and projection configuration for every method
Output policyNever overwrite historical artifacts; write to a seed-explicit extension directory
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Weekly progress update · Pilot cohort5
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- -
-

5. Fair comparison and expected pilot time

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Methods evaluated on identical responses

- - - - - - - - - -
MethodSample budgetPurposeInterpretation
Single-response cosineK=1 vs K=1Original low-budget diagnosticUseful, but not the fairness gate
Mean-DNA cosineK=2 vs K=2Strong matched-budget baselinePrimary comparator
Exact RBF-MMDK=2 vs K=2Exact distribution distanceAccuracy reference for approximation
RFFTraceK=2 vs K=2Scalable distributional vectorPrimary DistDNA result
Compact DistDNAK=2 vs K=2Projected representationAccuracy–storage trade-off
- -

The decision rule is simple: for each held-out query distribution, rank the ten reference models by distance and check whether the true model is ranked first. Report Top-1, MRR, Top-3, runtime, and storage. The meaningful improvement is DistDNA minus mean-DNA cosine on the same query models.

- -

Time from an available four-GPU A100 allocation

-
-
0–0.5 h
Audit reusable seed-explicit artifacts, freeze the ten-model manifest, and dry-run the queue.
-
0.5–3.5 h
Generate the missing seed-specific responses. Historical small-model observations have a 2.4-minute median per artifact, with a long retry tail; 1–3 hours is the realistic wall-clock range on four GPUs.
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3.5–4.5 h
Build embeddings and run cosine, exact MMD, RFFTrace, and compact DistDNA on the identical split.
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4.5–8 h
Inspect nearest-neighbour errors, verify no leakage, adjust implementation if necessary, rerun evaluation, and produce the first result table.
-
- -
-
1–3 h
expected generation time
for the 10-model pilot
-
4–8 h
expected time to a checked
first DistDNA comparison
-
- -
-

Practical advantage. Changes to RFF dimension, bandwidth, projection, or the DistDNA vector do not require regenerating model responses. Once the four raw response sets are stored correctly, representation development can iterate quickly.

-
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Weekly progress update · Comparison and timing6
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- -
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6. Scaling from 10 models to the full cohort

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“Four seeds for 100 models” has two materially different interpretations. The operational plan should name the scope explicitly.

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ScopeTarget artifactsReusable nowStill requiredExpected wall time
One setting
T=0.7, top-p=1.0		100 × 4 = 400≈103 explicit 42001/42002 artifacts≈2974 A100s: 2–4 days
8 A100s: 1.5–3 days
All 12 stochastic settings100 × 12 × 4 = 4,800320 explicit 42001/42002 artifacts4,4804 A100s: 4–6 weeks
8 A100s: 2–3 weeks
- -

Theoretical compute for the all-setting extension is about 2,335 A100 GPU-hours, estimated from the 2,552 successful primary artifacts (31.3 GPU-minutes per artifact on average). Wall-clock ranges add allowance for retries, timeouts, incompatible models, scheduling gaps, and audit passes.

- -

Recommended staged execution

-
Stage A
Let the current 72-slot recovery pass finish and freeze its final status. This preserves a clean endpoint for the original two-round primary grid.
-
Stage B
Run the 10-model four-seed pilot at T=0.7, top-p=1.0. Obtain a checked DistDNA versus matched mean-cosine result within one working day.
-
Stage C
Start the 100-model extension at the same setting. Skip only successful artifacts with the exact required seed; regenerate unknown-seed and seed-42 slots.
-
Stage D
While Stage C runs, iterate on DistDNA using already generated raw responses. No new generation is needed for representation-only changes.
-
Stage E
Expand to all settings only if the pipeline passes leakage checks and DistDNA shows a useful effect or a clearly motivated diagnostic value.
- -
-

Why not immediately queue all 4,480 missing artifacts? It is safe to generate raw responses while representation code changes, but the full queue commits several weeks of A100 time. A same-day pilot can first catch split errors, saturated retrieval, an unsuitable bandwidth, or a DistDNA construction that needs revision.

-
- -

Seed replacement policy

-

Existing unknown-seed and seed-42 artifacts are retained for provenance and the original analysis. They are not deleted or overwritten. The four-seed extension writes new, seed-explicit artifacts alongside them and the audit chooses the correct artifact by exact seed.

-
Weekly progress update · Scale-out plan7
-
- -
-

7. Implementation status and next deliverables

- - - - - - - - - - -
StatusComponentRepository location
ImplementedExact MMD, shared RFF map, RFFTrace, compact projectionsrc/llm_dna/distributional.py
ImplementedMatched-sample retrieval evaluator and exportscripts/run_rfftrace_experiment.py
ImplementedOptional DistDNA multiclass SVM evaluationscripts/evaluate_rfftrace_svm.py
VerifiedDistributional and evaluator unit teststests/test_distributional.py
tests/test_rfftrace_experiment.py
NextSeed-exact audit and four-seed generation queueNew pilot manifest and resumable runner
NextPilot result table, paired errors, and saturation diagnosticGenerated after the four-seed pilot
- -

Acceptance checks before interpreting accuracy

-
    -
  1. Exact split: no response artifact appears in both reference and query distributions.
    2. -
  2. Matched budget: DistDNA and mean-DNA cosine use the same four response sets.
    3. -
  3. Shared map: every model uses the same response encoder, RFF parameters, and optional projection.
    4. -
  4. No outcome-based cohort changes: the ten models are frozen before DistDNA accuracy is inspected.
    5. -
  5. Failure transparency: failed/empty responses are reported and never silently replaced by a different model.
    6. -
  6. Saturation check: if all methods score 100%, move to a larger or more confusable cohort rather than claiming equivalence or improvement.
    7. -
- -
-

Deliverable sequence

-

1 Final recovery audit   2 10-model seed manifest   3 Four-seed generation   4 Matched comparison   5 Error analysis   6 100-model scale-out

-
- -

Bottom line

-

A first technically meaningful DistDNA comparison is a same-day task once GPUs are available. A full 100-model, one-setting four-seed dataset is a multi-day task. A four-seed replacement across the entire 12-setting grid is a separate multi-week compute commitment and should follow, not precede, the pilot gate.

- -
-

Provenance. Counts in this note were generated from the live repository on 13 August 2026 at 13:56 SGT. Recovery was still running. Final figures must be regenerated after that process exits. The 5 August report and PDF remain unchanged.

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-
Primary LLM-DNA robustness experiment · End of update8
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+$T\in\{0.2,0.3,0.7\}$ backfills & r1: 42001; r2: 42002 & Reusable when the exact model, setting, prompts, token budget, and seed match. \\ +$T=0.5$ control/recovery & r1: 42; r2: 42 & Retain for the original grid, but regenerate for seed-clean evidence. \\ +Historical sweep & unknown & Retain for matched comparisons on identical responses, but regenerate for seed-clean evidence. \\ +New extension & 42001--42004 & Primary four-seed DistDNA artifacts. \\ +\bottomrule +\end{tabularx} +\end{table} + +Across all 12 stochastic settings, 320 unique successful model--setting +artifacts currently have explicit seed 42001 or 42002. Another 176 stochastic +artifacts use seed 42. A complete four-seed extension would require 4,800 +artifacts, leaving approximately 4,480 to generate after exact-seed reuse. + +\section{Immediate DistDNA pilot} + +The first DistDNA result should be a debugging pilot rather than an immediate +full-grid commitment. The proposed design fixes one difficult stochastic +setting, $T=0.7,p=1.0$, and uses four independent seeds for ten models below +0.8B parameters. + +\begin{figure}[htbp] +\centering +\includegraphics[width=\textwidth]{distdna_k2_pilot_design.png} +\caption{Leakage-free, matched-budget DistDNA pilot. Seeds 42001 and 42003 +form the $K=2$ reference distribution; seeds 42002 and 42004 form the held-out +$K=2$ query distribution. Every $K=2$ method consumes the same responses.} +\label{fig:distdna-pilot} +\end{figure} + +The ten-model cohort is fixed before DistDNA accuracy is inspected: + +\begin{longtable}{r p{0.68\textwidth} r} +\caption{Proposed small-model pilot cohort.}\label{tab:pilot-cohort}\\ +\toprule +No. & Model & Approx. scale \\ +\midrule +\endfirsthead +\toprule +No. & Model & Approx. scale \\ +\midrule +\endhead +1 & \nolinkurl{ibm-granite/granite-4.0-h-350m-base} & 0.35B \\ +2 & \nolinkurl{EleutherAI/pythia-410m-seed1} & 0.41B \\ +3 & \nolinkurl{EleutherAI/pythia-410m-seed2} & 0.41B \\ +4 & \nolinkurl{EleutherAI/pythia-410m-seed3} & 0.41B \\ +5 & \nolinkurl{unsloth/Qwen2.5-0.5B} & 0.50B \\ +6 & \nolinkurl{unsloth/Qwen2.5-0.5B-Instruct} & 0.50B \\ +7 & \nolinkurl{Ihor/Text2Graph-R1-Qwen2.5-0.5b} & 0.50B \\ +8 & \nolinkurl{robowaifudev/megatron-gpt2-345m} & 0.35B \\ +9 & \nolinkurl{state-spaces/mamba-370m-hf} & 0.37B \\ +10 & \nolinkurl{unsloth/Qwen3-0.6B-Base} & 0.60B \\ +\bottomrule +\end{longtable} + +The repeated Pythia and Qwen families make the pilot less likely to saturate +than an arbitrary set of unrelated models. The parameter labels are advertised +model scales and must be checked against model metadata before launch. + +\subsection{Fair comparison} + +The pilot reports five retrieval methods: + +\begin{enumerate} + \item single-response cosine ($K=1$), as a lower-budget diagnostic; + \item mean-DNA cosine ($K=2$), as the primary matched-budget baseline; + \item exact RBF-MMD ($K=2$); + \item RFFTrace ($K=2$); and + \item compact projected DistDNA ($K=2$). +\end{enumerate} + +The principal contrast is DistDNA minus mean-DNA cosine on the same query +models. Beating only the $K=1$ cosine baseline would not establish a +distributional advantage. Top-1 is primary; MRR, Top-3/5, runtime, and storage +are secondary. With ten query models, one corrected model changes Top-1 by ten +percentage points, so this pilot is suitable for debugging and effect direction +rather than a final confidence claim. + +\section{Execution time and scale-out decision} + +Historical successful primary artifacts required a median of 9.0 GPU-minutes +and a mean of 31.3 GPU-minutes per artifact; recognizable sub-0.8B models had a +median of 2.4 minutes but a long retry tail. Table~\ref{tab:timing} therefore +uses operational ranges rather than the median alone. + +\begin{table}[htbp] +\centering +\caption{Estimated time after GPUs become available.} +\label{tab:timing} +\begin{tabularx}{\textwidth}{Xrr} +\toprule +Task & Four A100s & Eight A100s \\ +\midrule +Ten-model, one-setting four-seed generation & 1--3 hours & below 2 hours \\ +Checked first DistDNA comparison, including diagnostics & 4--8 hours & 4--8 hours \\ +100 models, one setting, four seeds & 2--4 days & 1.5--3 days \\ +100 models, all 12 stochastic settings, four seeds & 4--6 weeks & 2--3 weeks \\ +\bottomrule +\end{tabularx} +\end{table} + +At $T=0.7,p=1.0$, the four-seed target is 400 artifacts. Approximately 103 +explicit 42001/42002 artifacts are already reusable, leaving roughly 297 new +artifacts. This one-setting extension should follow immediately after the +ten-model pilot passes its leakage and saturation checks. The all-setting +extension is a separate multi-week compute commitment and should be gated by +the pilot result. + +Raw responses remain reusable when RFF dimension, kernel bandwidth, compact +projection, or the DistDNA vector is changed. Representation development can +therefore continue while the seed-explicit generation queue runs. + +\section{Implementation and reproducibility} + +The relevant implementation is: + +\begin{itemize} + \item distributional features and distances: + \nolinkurl{src/llm_dna/distributional.py}; + \item matched-sample evaluator: + \nolinkurl{scripts/run_rfftrace_experiment.py}; + \item optional SVM evaluator: + \nolinkurl{scripts/evaluate_rfftrace_svm.py}; + \item live recovery and DistDNA design figures: + \nolinkurl{scripts/plot_primary_status_and_distdna_design.py}; and + \item temperature/top-$p$ and bounded-surface analysis: + \nolinkurl{scripts/analyze_primary_decoding_surface.py}. +\end{itemize} + +The new surface data are saved next to the figures as +\nolinkurl{primary_decoding_surface.csv}, +\nolinkurl{primary_bounded_surface.csv}, and +\nolinkurl{primary_decoding_analysis.json}. Re-running the analysis while +recovery is active may change the strict cohort and reported values. The final +report must therefore regenerate all counts and accuracy figures after the +recovery process exits. + +\end{document} diff --git a/scripts/analyze_primary_decoding_surface.py b/scripts/analyze_primary_decoding_surface.py new file mode 100644 index 0000000..5ad1bc7 --- /dev/null +++ b/scripts/analyze_primary_decoding_surface.py @@ -0,0 +1,458 @@ +#!/usr/bin/env python3 +"""Analyze and plot the live temperature/top-p retrieval surface. + +The analysis uses one fixed strict cohort across all 26 planned run keys. It +reports (1) queries against a deterministic T=0 reference gallery, (2) +same-setting r1-to-r2 repeat stability, and (3) equal-cell mean and worst-cell +accuracy under joint bounds T <= tau and top-p <= pi. +""" + +from __future__ import annotations + +import csv +import importlib.util +import json +import re +import sys +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import numpy as np +from PIL import Image, ImageDraw, ImageFont + + +ROOT = Path(__file__).resolve().parents[1] +OUT_DIR = ROOT / "docs" / "figures" / "primary_robustness_20260813" +GRID_SCRIPT = ROOT / "scripts" / "build_primary_run_grid.py" +TEMPERATURES = (0.0, 0.2, 0.3, 0.5, 0.7) +STOCHASTIC_TEMPERATURES = (0.2, 0.3, 0.5, 0.7) +TOP_PS = (0.8, 0.9, 1.0) +REPEATS = (1, 2) +COLORS = {0.8: "#2563eb", 0.9: "#d97706", 1.0: "#059669"} + + +def load_grid_module(): + spec = importlib.util.spec_from_file_location("primary_grid_surface", GRID_SCRIPT) + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return module + + +def font(size: int, bold: bool = False) -> ImageFont.FreeTypeFont: + name = "DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf" + return ImageFont.truetype(f"/usr/share/fonts/truetype/dejavu/{name}", size) + + +def status_rows(path: Path) -> dict[str, dict[str, Any]]: + final: dict[str, dict[str, Any]] = {} + if not path.exists(): + return final + for line in path.read_text(encoding="utf-8", errors="ignore").splitlines(): + try: + row = json.loads(line) + except json.JSONDecodeError: + continue + model = row.get("model_id") + if model: + final[str(model)] = row + return final + + +def run_key(manifest_path: Path, manifest: dict[str, Any]) -> tuple[float, float, int] | None: + try: + temperature = float(manifest["temperature"]) + top_p = float(manifest["top_p"]) + except (KeyError, TypeError, ValueError): + return None + suffix = str(manifest.get("output_suffix", "")) + match = re.search(r"_r([12])$", suffix) + repeat = int(match.group(1)) if match else None + if ( + repeat is None + and manifest_path.parent.name == "rand_chinese_0p3b_7b_temp07" + and temperature == 0.7 + and top_p == 0.9 + ): + repeat = 1 + if ( + repeat not in REPEATS + or temperature not in TEMPERATURES + or top_p not in TOP_PS + or (temperature == 0.0 and top_p != 1.0) + ): + return None + return temperature, top_p, int(repeat) + + +def candidate_priority(manifest: dict[str, Any], row: dict[str, Any]) -> tuple[int, str]: + seed = manifest.get("generation_seed") + seed_priority = 2 if seed in (42001, 42002) else 1 if seed is not None else 0 + return seed_priority, str(row.get("recorded_at", "")) + + +def collect_artifacts(cohort: set[str]) -> dict[tuple[str, float, float, int], Path]: + selected: dict[tuple[str, float, float, int], tuple[tuple[int, str], Path]] = {} + for manifest_path in sorted((ROOT / "out").rglob("manifest.json")): + try: + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + continue + key = run_key(manifest_path, manifest) + if key is None: + continue + temperature, top_p, repeat = key + for model, row in status_rows(manifest_path.parent / "status.jsonl").items(): + if model not in cohort or row.get("status") != "success": + continue + raw_path = row.get("output_path") + if not raw_path: + continue + dna_path = Path(str(raw_path)) + if not dna_path.is_absolute(): + dna_path = ROOT / dna_path + if not dna_path.exists(): + continue + artifact_key = model, temperature, top_p, repeat + candidate = candidate_priority(manifest, row), dna_path + if artifact_key not in selected or candidate[0] > selected[artifact_key][0]: + selected[artifact_key] = candidate + return {key: value[1] for key, value in selected.items()} + + +def planned_run_keys() -> list[tuple[float, float, int]]: + return [ + (temperature, top_p, repeat) + for temperature in TEMPERATURES + for top_p in TOP_PS + if temperature > 0 or top_p == 1.0 + for repeat in REPEATS + ] + + +def load_vector(path: Path) -> np.ndarray: + payload = json.loads(path.read_text(encoding="utf-8")) + vector = np.asarray(payload["signature"], dtype=np.float64) + if vector.ndim != 1 or not np.all(np.isfinite(vector)): + raise ValueError(f"Invalid DNA vector: {path}") + return vector + + +def matrix( + artifacts: dict[tuple[str, float, float, int], Path], + cohort: list[str], + temperature: float, + top_p: float, + repeat: int, +) -> np.ndarray: + return np.stack( + [load_vector(artifacts[(model, temperature, top_p, repeat)]) for model in cohort] + ) + + +def normalize(values: np.ndarray) -> np.ndarray: + return values / np.maximum(np.linalg.norm(values, axis=1, keepdims=True), 1e-12) + + +def retrieval_metrics(gallery: np.ndarray, query: np.ndarray) -> dict[str, float]: + scores = normalize(query) @ normalize(gallery).T + order = np.argsort(-scores, axis=1, kind="stable") + truth = np.arange(len(query)) + ranks = np.asarray([int(np.flatnonzero(order[i] == i)[0]) + 1 for i in truth]) + return { + "top1": float(np.mean(ranks == 1)), + "top3": float(np.mean(ranks <= 3)), + "top5": float(np.mean(ranks <= 5)), + "mrr": float(np.mean(1.0 / ranks)), + } + + +def setting_rows( + artifacts: dict[tuple[str, float, float, int], Path], cohort: list[str] +) -> list[dict[str, Any]]: + fixed_gallery = matrix(artifacts, cohort, 0.0, 1.0, 1) + rows: list[dict[str, Any]] = [] + for temperature in TEMPERATURES: + for top_p in TOP_PS: + if temperature == 0.0 and top_p != 1.0: + continue + query = matrix(artifacts, cohort, temperature, top_p, 2) + same_gallery = matrix(artifacts, cohort, temperature, top_p, 1) + for comparison, gallery in ( + ("fixed_t0_gallery", fixed_gallery), + ("same_setting_repeat", same_gallery), + ): + rows.append( + { + "comparison": comparison, + "temperature": temperature, + "top_p": top_p, + "model_count": len(cohort), + **retrieval_metrics(gallery, query), + } + ) + return rows + + +def bounded_rows( + artifacts: dict[tuple[str, float, float, int], Path], cohort: list[str] +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for tau in STOCHASTIC_TEMPERATURES: + for p_bound in TOP_PS: + settings = [(0.0, 1.0)] + [ + (temperature, top_p) + for temperature in STOCHASTIC_TEMPERATURES + for top_p in TOP_PS + if temperature <= tau and top_p <= p_bound + ] + galleries = [matrix(artifacts, cohort, t, p, 1) for t, p in settings] + centroid = np.mean(np.stack(galleries), axis=0) + cell_rows = [] + for temperature, top_p in settings: + query = matrix(artifacts, cohort, temperature, top_p, 2) + cell_rows.append(retrieval_metrics(centroid, query)) + for metric in ("top1", "mrr"): + values = [row[metric] for row in cell_rows] + rows.append( + { + "temperature_bound": tau, + "top_p_bound": p_bound, + "metric": metric, + "cell_count": len(settings), + "model_count": len(cohort), + "equal_cell_mean": float(np.mean(values)), + "worst_setting": float(np.min(values)), + "best_setting": float(np.max(values)), + } + ) + return rows + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + fields = list(rows[0]) + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fields, lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + + +def panel(draw: ImageDraw.ImageDraw, box: tuple[int, int, int, int], title: str) -> None: + draw.rounded_rectangle(box, radius=18, fill="#f8fafc", outline="#cbd5e1", width=3) + draw.text((box[0] + 25, box[1] + 20), title, font=font(27, True), fill="#0f172a") + + +def axes( + draw: ImageDraw.ImageDraw, + box: tuple[int, int, int, int], + xs: list[float], + xlabel: str, + ylabel: str, +) -> tuple[callable, callable]: + left, top, right, bottom = box + draw.line((left, bottom, right, bottom), fill="#475569", width=3) + draw.line((left, top, left, bottom), fill="#475569", width=3) + for value in np.linspace(0, 1, 6): + y = int(bottom - value * (bottom - top)) + draw.line((left, y, right, y), fill="#e2e8f0", width=2) + draw.text((left - 62, y - 11), f"{value:.1f}", font=font(18), fill="#475569") + for index, value in enumerate(xs): + x = int(left + index * (right - left) / max(1, len(xs) - 1)) + draw.line((x, bottom, x, bottom + 8), fill="#475569", width=2) + draw.text((x - 17, bottom + 12), f"{value:g}", font=font(18), fill="#475569") + draw.text(((left + right) // 2 - 85, bottom + 48), xlabel, font=font(20, True), fill="#334155") + draw.text((left - 78, top - 30), ylabel, font=font(18, True), fill="#334155") + return ( + lambda index: int(left + index * (right - left) / max(1, len(xs) - 1)), + lambda value: int(bottom - value * (bottom - top)), + ) + + +def draw_line_panel( + draw: ImageDraw.ImageDraw, + box: tuple[int, int, int, int], + rows: list[dict[str, Any]], +) -> None: + panel(draw, box, "A. Fixed top-p: exact Top-1 vs temperature") + plot_box = (box[0] + 95, box[1] + 105, box[2] - 35, box[3] - 85) + xmap, ymap = axes(draw, plot_box, list(TEMPERATURES), "Temperature", "Top-1") + lookup = { + (float(row["temperature"]), float(row["top_p"])): float(row["top1"]) + for row in rows + if row["comparison"] == "fixed_t0_gallery" + } + for top_p in TOP_PS: + points = [] + for index, temperature in enumerate(TEMPERATURES): + key = (temperature, top_p) + if key in lookup: + points.append((xmap(index), ymap(lookup[key]), lookup[key])) + if len(points) > 1: + draw.line([(x, y) for x, y, _ in points], fill=COLORS[top_p], width=6) + for x, y, value in points: + draw.ellipse((x - 7, y - 7, x + 7, y + 7), fill=COLORS[top_p], outline="white", width=2) + draw.text((x - 23, y - 31), f"{value:.2f}", font=font(16, True), fill=COLORS[top_p]) + legend_x = box[0] + 125 + for top_p in TOP_PS: + draw.line((legend_x, box[1] + 68, legend_x + 38, box[1] + 68), fill=COLORS[top_p], width=6) + draw.text((legend_x + 46, box[1] + 55), f"top-p={top_p:g}", font=font(18), fill="#334155") + legend_x += 180 + + +def heat_color(value: float) -> str: + low = (254, 226, 226) + high = (22, 163, 74) + ratio = min(1.0, max(0.0, value)) + rgb = [round(a + (b - a) * ratio) for a, b in zip(low, high)] + return "#" + "".join(f"{part:02x}" for part in rgb) + + +def draw_heatmap( + draw: ImageDraw.ImageDraw, + box: tuple[int, int, int, int], + title: str, + rows: list[dict[str, Any]], + comparison: str, +) -> None: + panel(draw, box, title) + lookup = { + (float(row["temperature"]), float(row["top_p"])): float(row["top1"]) + for row in rows + if row["comparison"] == comparison + } + left, top = box[0] + 105, box[1] + 115 + cell_w, cell_h = 145, 80 + for column, top_p in enumerate(TOP_PS): + draw.text((left + column * cell_w + 35, top - 42), f"p={top_p:g}", font=font(19, True), fill="#334155") + for row_index, temperature in enumerate(TEMPERATURES): + draw.text((box[0] + 28, top + row_index * cell_h + 25), f"T={temperature:g}", font=font(19, True), fill="#334155") + for column, top_p in enumerate(TOP_PS): + coords = ( + left + column * cell_w, + top + row_index * cell_h, + left + (column + 1) * cell_w - 10, + top + (row_index + 1) * cell_h - 10, + ) + key = (temperature, top_p) + if key not in lookup: + draw.rounded_rectangle(coords, radius=9, fill="#e2e8f0") + draw.text((coords[0] + 48, coords[1] + 20), "N/A", font=font(18, True), fill="#64748b") + continue + value = lookup[key] + draw.rounded_rectangle(coords, radius=9, fill=heat_color(value), outline="#94a3b8", width=2) + draw.text((coords[0] + 35, coords[1] + 13), f"{value:.3f}", font=font(21, True), fill="#0f172a") + draw.text((coords[0] + 43, coords[1] + 42), "Top-1", font=font(15), fill="#334155") + + +def plot_surface(rows: list[dict[str, Any]], cohort_size: int, generated_at: str, output: Path) -> None: + image = Image.new("RGB", (2000, 1120), "white") + draw = ImageDraw.Draw(image) + draw.text((65, 38), "Temperature and top-p relationship — live primary cohort", font=font(40, True), fill="#0f172a") + draw.text((65, 92), f"Fixed strict cohort N={cohort_size}; r1 gallery vs held-out r2 query; generated {generated_at}", font=font(22), fill="#475569") + draw_line_panel(draw, (45, 145, 860, 1050), rows) + draw_heatmap(draw, (895, 145, 1430, 1050), "B. Fixed T=0 gallery", rows, "fixed_t0_gallery") + draw_heatmap(draw, (1465, 145, 1990, 1050), "C. Same-setting repeat", rows, "same_setting_repeat") + output.parent.mkdir(parents=True, exist_ok=True) + image.save(output, optimize=True) + + +def plot_bounded(rows: list[dict[str, Any]], cohort_size: int, generated_at: str, output: Path) -> None: + image = Image.new("RGB", (1900, 1050), "white") + draw = ImageDraw.Draw(image) + draw.text((65, 38), "Joint decoding bounds — cosine centroid retrieval", font=font(40, True), fill="#0f172a") + draw.text((65, 92), f"T≤tau and top-p≤pi; deterministic control always included; strict cohort N={cohort_size}; {generated_at}", font=font(22), fill="#475569") + selected = [row for row in rows if row["metric"] == "top1"] + for panel_index, (value_key, title) in enumerate( + (("equal_cell_mean", "A. Equal-cell mean Top-1"), ("worst_setting", "B. Worst-setting Top-1")) + ): + x0 = 45 + panel_index * 735 + box = (x0, 145, x0 + 700, 980) + panel(draw, box, title) + plot_box = (box[0] + 95, box[1] + 110, box[2] - 35, box[3] - 85) + xmap, ymap = axes(draw, plot_box, list(STOCHASTIC_TEMPERATURES), "Temperature bound tau", "Top-1") + lookup = { + (float(row["temperature_bound"]), float(row["top_p_bound"])): float(row[value_key]) + for row in selected + } + legend_x = box[0] + 110 + for p_bound in TOP_PS: + points = [(xmap(i), ymap(lookup[(tau, p_bound)]), lookup[(tau, p_bound)]) for i, tau in enumerate(STOCHASTIC_TEMPERATURES)] + draw.line([(x, y) for x, y, _ in points], fill=COLORS[p_bound], width=6) + for x, y, _ in points: + draw.ellipse((x - 7, y - 7, x + 7, y + 7), fill=COLORS[p_bound], outline="white", width=2) + draw.line((legend_x, box[1] + 72, legend_x + 32, box[1] + 72), fill=COLORS[p_bound], width=6) + draw.text((legend_x + 38, box[1] + 59), f"p≤{p_bound:g}", font=font(17), fill="#334155") + legend_x += 145 + + box = (1510, 145, 1885, 980) + panel(draw, box, "C. Mean Top-1 grid") + lookup = { + (float(row["temperature_bound"]), float(row["top_p_bound"])): float(row["equal_cell_mean"]) + for row in selected + } + left, top, cell_w, cell_h = box[0] + 90, box[1] + 145, 90, 135 + for column, p_bound in enumerate(TOP_PS): + draw.text((left + column * cell_w + 8, top - 48), f"p≤{p_bound:g}", font=font(16, True), fill="#334155") + for row_index, tau in enumerate(STOCHASTIC_TEMPERATURES): + draw.text((box[0] + 18, top + row_index * cell_h + 43), f"T≤{tau:g}", font=font(17, True), fill="#334155") + for column, p_bound in enumerate(TOP_PS): + value = lookup[(tau, p_bound)] + coords = (left + column * cell_w, top + row_index * cell_h, left + (column + 1) * cell_w - 8, top + (row_index + 1) * cell_h - 10) + draw.rounded_rectangle(coords, radius=9, fill=heat_color(value), outline="#94a3b8", width=2) + draw.text((coords[0] + 10, coords[1] + 42), f"{value:.3f}", font=font(17, True), fill="#0f172a") + output.parent.mkdir(parents=True, exist_ok=True) + image.save(output, optimize=True) + + +def main() -> int: + grid = load_grid_module() + fixed_cohort = grid.load_cohort() + artifacts = collect_artifacts(fixed_cohort) + run_keys = planned_run_keys() + strict = sorted( + model + for model in fixed_cohort + if all((model, temperature, top_p, repeat) in artifacts for temperature, top_p, repeat in run_keys) + ) + if not strict: + raise SystemExit("No strict common cohort is available.") + generated_at = datetime.now().astimezone().isoformat(timespec="seconds") + per_setting = setting_rows(artifacts, strict) + bounded = bounded_rows(artifacts, strict) + OUT_DIR.mkdir(parents=True, exist_ok=True) + write_csv(OUT_DIR / "primary_decoding_surface.csv", per_setting) + write_csv(OUT_DIR / "primary_bounded_surface.csv", bounded) + payload = { + "generated_at": generated_at, + "strict_cohort_size": len(strict), + "strict_cohort": strict, + "artifact_count": len(artifacts), + "selection_priority": "explicit 42001/42002 success, then other explicit seed, then historical unknown seed", + "per_setting": per_setting, + "bounded": bounded, + } + (OUT_DIR / "primary_decoding_analysis.json").write_text( + json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8" + ) + stamp = generated_at.replace("T", " ") + plot_surface(per_setting, len(strict), stamp, OUT_DIR / "temperature_top_p_relationship.png") + plot_bounded(bounded, len(strict), stamp, OUT_DIR / "bounded_decoding_relationship.png") + print(json.dumps({ + "generated_at": generated_at, + "artifact_count": len(artifacts), + "strict_cohort_size": len(strict), + "outputs": [ + str(OUT_DIR / "temperature_top_p_relationship.png"), + str(OUT_DIR / "bounded_decoding_relationship.png"), + str(OUT_DIR / "primary_decoding_surface.csv"), + str(OUT_DIR / "primary_bounded_surface.csv"), + ], + }, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/render_aug13_progress_report.sh b/scripts/render_aug13_progress_report.sh index 1e316fd..d3b434d 100755 --- a/scripts/render_aug13_progress_report.sh +++ b/scripts/render_aug13_progress_report.sh @@ -2,16 +2,27 @@ set -euo pipefail ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -SOURCE="$ROOT/docs/primary_robustness_experiment_full_updated_2026-08-13.html" -OUTPUT="$ROOT/docs/primary_robustness_experiment_full_updated_2026-08-13.pdf" +SOURCE="$ROOT/docs/primary_robustness_experiment_full_updated_2026-08-13.tex" +OUTPUT_DIR="${OUTPUT_DIR:-$ROOT/docs}" +TECTONIC_BIN="${TECTONIC_BIN:-$(command -v tectonic || true)}" -chromium \ - --headless \ - --no-sandbox \ - --disable-gpu \ - --allow-file-access-from-files \ - --no-pdf-header-footer \ - --print-to-pdf="$OUTPUT" \ - "file://$SOURCE" +if [[ -z "$TECTONIC_BIN" || ! -x "$TECTONIC_BIN" ]]; then + echo "tectonic is required; set TECTONIC_BIN to the executable path" >&2 + exit 1 +fi -echo "Rendered $OUTPUT" +if [[ -z "${BUILD_DIR:-}" ]]; then + BUILD_DIR="$(mktemp -d)" + CREATED_BUILD_DIR=1 +else + mkdir -p "$BUILD_DIR" + CREATED_BUILD_DIR=0 +fi + +cd "$ROOT" +trap 'if [[ "$CREATED_BUILD_DIR" == "1" ]]; then rm -rf "$BUILD_DIR"; fi' EXIT +"$TECTONIC_BIN" --outdir "$BUILD_DIR" "$SOURCE" +mkdir -p "$OUTPUT_DIR" +cp "$BUILD_DIR/primary_robustness_experiment_full_updated_2026-08-13.pdf" "$OUTPUT_DIR/" + +echo "Rendered $OUTPUT_DIR/primary_robustness_experiment_full_updated_2026-08-13.pdf" From 0634bf64e8338bb10626e8a341372db617feb231 Mon Sep 17 00:00:00 2001 From: yuqiannemo Date: Thu, 13 Aug 2026 16:02:00 +0800 Subject: [PATCH 28/28] Add resumable four-seed DistDNA pilot --- configs/distdna_pilot_10models.jsonl | 10 ++ scripts/build_distdna_pilot_seed_queue.py | 141 ++++++++++++++++++++++ scripts/run_distdna_four_seed_pilot.sh | 96 +++++++++++++++ 3 files changed, 247 insertions(+) create mode 100644 configs/distdna_pilot_10models.jsonl create mode 100755 scripts/build_distdna_pilot_seed_queue.py create mode 100755 scripts/run_distdna_four_seed_pilot.sh diff --git a/configs/distdna_pilot_10models.jsonl b/configs/distdna_pilot_10models.jsonl new file mode 100644 index 0000000..4dc0bf1 --- /dev/null +++ b/configs/distdna_pilot_10models.jsonl @@ -0,0 +1,10 @@ +{"model_id": "ibm-granite/granite-4.0-h-350m-base", "provider": "huggingface"} +{"model_id": "EleutherAI/pythia-410m-seed1", "provider": "huggingface"} +{"model_id": "EleutherAI/pythia-410m-seed2", "provider": "huggingface"} +{"model_id": "EleutherAI/pythia-410m-seed3", "provider": "huggingface"} +{"model_id": "unsloth/Qwen2.5-0.5B", "provider": "huggingface"} +{"model_id": "unsloth/Qwen2.5-0.5B-Instruct", "provider": "huggingface"} +{"model_id": "Ihor/Text2Graph-R1-Qwen2.5-0.5b", "provider": "huggingface"} +{"model_id": "robowaifudev/megatron-gpt2-345m", "provider": "huggingface"} +{"model_id": "state-spaces/mamba-370m-hf", "provider": "huggingface"} +{"model_id": "unsloth/Qwen3-0.6B-Base", "provider": "huggingface"} diff --git a/scripts/build_distdna_pilot_seed_queue.py b/scripts/build_distdna_pilot_seed_queue.py new file mode 100755 index 0000000..a893cd8 --- /dev/null +++ b/scripts/build_distdna_pilot_seed_queue.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Build the missing-model queue for one explicit DistDNA pilot seed. + +An artifact is reusable only when its run manifest explicitly records the +requested generation seed and decoding setting, its latest per-model status is +successful, and both the DNA and summary files still exist. Historical runs +whose seed is absent from the manifest are deliberately ignored. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] + + +def read_json(path: Path) -> Any: + return json.loads(path.read_text(encoding="utf-8")) + + +def resolved_artifact(path_value: object) -> Path | None: + if not isinstance(path_value, str) or not path_value.strip(): + return None + path = Path(path_value) + return path if path.is_absolute() else ROOT / path + + +def explicit_successes( + output_root: Path, + model_ids: set[str], + seed: int, + temperature: float, + top_p: float, +) -> dict[str, dict[str, str]]: + successes: dict[str, dict[str, str]] = {} + for manifest_path in output_root.glob("**/manifest.json"): + try: + manifest = read_json(manifest_path) + if int(manifest.get("generation_seed")) != seed: + continue + if float(manifest.get("temperature")) != temperature: + continue + if float(manifest.get("top_p")) != top_p: + continue + except (OSError, ValueError, TypeError, json.JSONDecodeError): + continue + + status_path = manifest_path.with_name("status.jsonl") + if not status_path.is_file(): + continue + + latest: dict[str, dict[str, Any]] = {} + try: + for line in status_path.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + record = json.loads(line) + model_id = record.get("model_id") + if model_id in model_ids: + latest[model_id] = record + except (OSError, json.JSONDecodeError): + continue + + for model_id, record in latest.items(): + if record.get("status") != "success": + continue + dna_path = resolved_artifact(record.get("output_path")) + summary_path = resolved_artifact(record.get("summary_path")) + if not dna_path or not summary_path: + continue + if not dna_path.is_file() or dna_path.stat().st_size == 0: + continue + if not summary_path.is_file() or summary_path.stat().st_size == 0: + continue + successes[model_id] = { + "manifest": str(manifest_path.relative_to(ROOT)), + "status": str(status_path.relative_to(ROOT)), + "dna": str(dna_path.relative_to(ROOT)), + "summary": str(summary_path.relative_to(ROOT)), + } + return successes + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--cohort", type=Path, required=True) + parser.add_argument("--seed", type=int, required=True) + parser.add_argument("--temperature", type=float, default=0.7) + parser.add_argument("--top-p", type=float, default=1.0) + parser.add_argument("--search-root", type=Path, default=ROOT / "out") + parser.add_argument("--missing-jsonl", type=Path, required=True) + parser.add_argument("--inventory-json", type=Path, required=True) + args = parser.parse_args() + + cohort_path = args.cohort if args.cohort.is_absolute() else ROOT / args.cohort + rows = [json.loads(line) for line in cohort_path.read_text(encoding="utf-8").splitlines() if line.strip()] + model_ids = {str(row["model_id"]) for row in rows} + if len(rows) != 10 or len(model_ids) != 10: + raise SystemExit(f"pilot cohort must contain exactly 10 unique models: {cohort_path}") + + search_root = args.search_root if args.search_root.is_absolute() else ROOT / args.search_root + successes = explicit_successes(search_root, model_ids, args.seed, args.temperature, args.top_p) + missing = [row for row in rows if row["model_id"] not in successes] + + args.missing_jsonl.parent.mkdir(parents=True, exist_ok=True) + args.inventory_json.parent.mkdir(parents=True, exist_ok=True) + args.missing_jsonl.write_text( + "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in missing), + encoding="utf-8", + ) + args.inventory_json.write_text( + json.dumps( + { + "temperature": args.temperature, + "top_p": args.top_p, + "generation_seed": args.seed, + "cohort_size": len(rows), + "reusable_explicit_success_count": len(successes), + "missing_count": len(missing), + "reusable_explicit_successes": successes, + "missing_models": [row["model_id"] for row in missing], + }, + ensure_ascii=False, + indent=2, + ) + + "\n", + encoding="utf-8", + ) + print( + f"seed={args.seed} reusable={len(successes)}/10 " + f"missing={len(missing)}/10 queue={args.missing_jsonl}" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_distdna_four_seed_pilot.sh b/scripts/run_distdna_four_seed_pilot.sh new file mode 100755 index 0000000..02c5508 --- /dev/null +++ b/scripts/run_distdna_four_seed_pilot.sh @@ -0,0 +1,96 @@ +#!/usr/bin/env bash +# Generate only missing explicit-seed artifacts for the 10-model DistDNA pilot. +# Historical unknown-seed outputs are never used as evidence and never touched. + +set -euo pipefail + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$ROOT" + +PYTHON_BIN="${PYTHON_BIN:-$ROOT/venv/bin/python}" +COHORT="${COHORT:-configs/distdna_pilot_10models.jsonl}" +OUTPUT_DIR="${OUTPUT_DIR:-out/distdna_four_seed_pilot_t07_p10_20260813}" +LOG_DIR="${LOG_DIR:-logs/distdna_four_seed_pilot_t07_p10_20260813}" +GPUS="${GPUS:-6,7}" +MAX_CONCURRENT_GPUS="${MAX_CONCURRENT_GPUS:-2}" +SEEDS="${SEEDS:-42001 42002 42003 42004}" +TEMPERATURE="${TEMPERATURE:-0.7}" +TOP_P="${TOP_P:-1.0}" + +mkdir -p "$OUTPUT_DIR" "$LOG_DIR/generated_configs" "$LOG_DIR/inventory" + +LOCK_PATH="$LOG_DIR/four_seed_pilot.lock" +exec 9>"$LOCK_PATH" +if ! flock -n 9; then + echo "Another DistDNA pilot process already holds $LOCK_PATH" >&2 + exit 1 +fi + +if [[ ! -x "$PYTHON_BIN" ]]; then + echo "Missing Python environment: $PYTHON_BIN" >&2 + exit 1 +fi + +run_seed() { + local seed="$1" + local queue="$LOG_DIR/generated_configs/seed_${seed}_missing.jsonl" + local inventory="$LOG_DIR/inventory/seed_${seed}.json" + local suffix="_t07_p10_seed${seed}" + local run_name="distdna_pilot_t07_p10_seed${seed}" + local run_log="$LOG_DIR/${run_name}.log" + + "$PYTHON_BIN" scripts/build_distdna_pilot_seed_queue.py \ + --cohort "$COHORT" \ + --seed "$seed" \ + --temperature "$TEMPERATURE" \ + --top-p "$TOP_P" \ + --missing-jsonl "$queue" \ + --inventory-json "$inventory" + + local missing_count + missing_count="$(wc -l < "$queue")" + if [[ "$missing_count" -eq 0 ]]; then + echo "[$(date '+%Y-%m-%d %H:%M:%S')] seed=$seed already complete; skipped" + return 0 + fi + + echo "[$(date '+%Y-%m-%d %H:%M:%S')] seed=$seed starting $missing_count missing models on GPUs $GPUS" + "$PYTHON_BIN" scripts/run_hf_dna_pipeline.py \ + --providers huggingface \ + --hf-jsonl "$queue" \ + --dataset rand_chinese \ + --max-samples 100 \ + --output-dir "$OUTPUT_DIR" \ + --output-suffix "$suffix" \ + --run-name "$run_name" \ + --global-status-journal "$OUTPUT_DIR/dna_global_status.jsonl" \ + --global-state "$OUTPUT_DIR/dna_global_state.json" \ + --temperature "$TEMPERATURE" \ + --top-p "$TOP_P" \ + --generation-seed "$seed" \ + --ignore-response-cache \ + --try-vllm \ + --gpus "$GPUS" \ + --max-concurrent-gpus "$MAX_CONCURRENT_GPUS" \ + --min-gpu-free-gb 4 \ + --gpu-poll-seconds 15 \ + --model-timeout-seconds 43200 \ + --max-retries 3 \ + --oom-retries 3 \ + --retry-delay-seconds 300 \ + --gpu-memory-per-billion-gb 2.2 \ + --gpu-memory-headroom-gb 2 \ + --cache-evict-finished success \ + --resume-mode all \ + --do-sample 2>&1 | tee "$run_log" +} + +echo "DistDNA four-seed pilot: T=$TEMPERATURE top_p=$TOP_P cohort=$COHORT" +echo "New explicit-seed output root: $OUTPUT_DIR" +echo "Historical output roots are read only for explicit-seed reuse." + +for seed in $SEEDS; do + run_seed "$seed" +done + +echo "[$(date '+%Y-%m-%d %H:%M:%S')] DistDNA four-seed pilot generation complete"

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