Add power Doppler seed energy schedule (--schedule doppler)

CHANGELOG.md

@@ -21,6 +21,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- **Power Doppler schedule** (`--schedule doppler`, `core/power_doppler.py`): per-seed ensembles of mutant hit counts; mean + SVD wall filter, CFAR χ² flow detection; flow power scales seed energy to `[1, max_mult]`.
 - **OS / network scheduler ports**: seed arms `mlfq`, `stride`, `eevdf`, `bfq`, `sfq`, `codel`, `aimd`, `p2c` (`--seed-<name>-scheduler`) and op arms `op_stride`, `op_p2c` (`--op-stride`, `--op-p2c`); `core/fair_queue.py` gains `Stride` and `EEVDF`. Elo arms, in `--hail-mary`; seed arms also run without `--elo`. Falsification and adversarial tests. No paired benchmark yet.
 - **Ten op mutators** for in-tree targets and text decoders: `json_mutate` (fuzzgoat), `sql_mutate` (sqlite SQL path), `ecdsa_field_mutate` (secp256k1), `recompress_lz4`, `recompress_png_idat` (format band, sniffer-gated); `encoding_wrap`, `escape_mutate`, `ascii_float` (structural); `utf16_transcode`, `nest_bomb` (radamsa). Tests: `tests/test_{json_mutate,sql_mutate,ecdsa_field_mutate,recompress_roundtrip,text_codec,nest_bomb}.py`. Discovery effect on fuzzgoat unmeasured.
 - **`covering_array_webp`, `covering_array_isobmff`, `covering_array_zip`** operators (`core/mutations/covering_array_container.py`): pairwise sweep of WebP `VP8X` (riff_size, chunk_size, flags, reserved, width, height; 49 rows), ISO-BMFF `ftyp` (size, major brand, minor; 30 rows) and the first ZIP local file header (version, flags, method, sizes, name/extra length; 50 rows). Each gated on its own magic. Selection share unmeasured; ISO-BMFF `tkhd` not covered (variable depth).

README.md

@@ -143,7 +143,7 @@ per-sub-operator reward instead of uniformly (`--no-adaptive-havoc` restores uni
 | Metropolis admission | `--metropolis` | Accept non-improving inputs with P = exp(−ΔE/T) |
 | Secretary stopping | `--secretary` | Secretary-problem stopping ranks (display only) |
 | honggfuzz power factors | `--honggfuzz` | Novelty decay, freshness, fertility, density, entropy and timeout penalties |
-| AFL++ power schedules | `--schedule` | FAST/COE/RARE/MMOPT/LIN/QUAD/GO/AFLGO/ENTROPIC seed-level energy |
+| AFL++ power schedules | `--schedule` | FAST/COE/RARE/MMOPT/LIN/QUAD/GO/AFLGO/ENTROPIC/DOPPLER seed-level energy |
 | AFLGo directed annealing | `--schedule aflgo` | Exact AFLGo power factor — symmetric 32×/1/32× energy by distance-to-target with time-based cooling (`--t-x`, `--aflgo-cooling`) |
 | Entropic power schedule | `--schedule entropic` | libFuzzer `-entropic`: energy ∝ log(1 + rare-feature count) from already-tracked rare-edge ownership |
 | **K-Scheduler Katz centrality** | auto | On trace-pc targets: whole-program ICFG → horizon graph (contracted visited deletion, DAG) → out-degree Katz with β from node-hit counts; Elo-rated `katz` seed arm plus a clamped `--schedule katz` energy |

docs/DEEP_DIVE.md

@@ -84,7 +84,8 @@ For production and sensitive binaries using AFL family fuzzers is the best cours
 - **AFLGo power schedule** (`--schedule aflgo`, `--aflgo-cooling exp|log|lin|quad`, `--t-x MINUTES`): the exact `calculate_score()` distance section from AFLGo's afl-fuzz.c. Temperature `T` follows the chosen cooling over t_x minutes to exploitation (`exp`: 1/20^progress; `log`: 1/(1+2·ln(1+progress·13358.7268297)); `lin`: 1/(1+19·progress); `quad`: 1/(1+19·progress²)); `p = (1−nd)(1−T) + 0.5T` with `nd = (d−min)/(max−min)` normalized over the observed queue (min/max tracked per run, excluding the no-data sentinel); `factor = 2^(2·log2(32)·(p−0.5))` — symmetric around 1.0: early campaigns treat every seed equally, late campaigns give near-target seeds up to 32× energy and far seeds as little as 1/32×.
 - **AFLGo SHM-tail distance channel**: compiled into EVERY shim-linked target since `__AFL_DISTANCE_MODE` defaulted to 1 (2026-08-24; `-D__AFL_DISTANCE_MODE=0` opts out). Inert unless directed mode uploads a distance table (`DistanceTableShm`/`__AFL_DIST_SHM_ID`): without one, sum/count stay 0 and every reader takes the Python-side path. `build_targets.sh --distance` additionally builds the trace-pc-instrumented `*_dist.so`/`*_dist_asan.so` variants, where the shim accumulates per-block distances in `__sanitizer_cov_trace_pc()` — the PC (relative to the dladdr-derived object base) probes an open-addressing table of `{key, dist}` entries (packed 12-byte layout; the 4-byte header holds the slot *capacity*, a power of two ≥ 2×entries so empty slots exist, and the builder hash-inserts at `key % capacity` with linear probing to mirror the shim's probe — uploaded by the fuzzer at startup via `DistanceTableShm`/`__AFL_DIST_SHM_ID`), accumulating sum/count into the 16-byte SHM **tail** (after the edge table: `u64 dist_sum`, `u64 dist_count`), written at reset, at process exit (subprocess runs never call reset), and per-iteration in in-process modes via `__afl_dist_flush` (direct_lite has no process boundary, so the runner flushes the tail after each `run_one`). Per-execution `avg_distance = sum/count/100` is read straight from the tail and preferred over Python-side computation; blocks without a table entry don't count (AFLGo semantics). The table's PC keys are recovered by scanning text for `call __sanitizer_cov_trace_pc` sites (modern clang emits no `__sancov_pcs` for trace-pc) mapped to valued blocks via the CFGs — `TargetDistance.pc_distance_table()`. The shim's sanitizer-coverage callbacks are hidden-visibility so a libasan LD_PRELOAD cannot interpose over them in PIE builds. `tools/gen_distance_table.py` emits the table as C or text for inspection. Without the table (count==0) everything degrades to the Python-side path. Works in subprocess, direct_lite, and persistent modes (ASAN direct_lite requires libasan preloaded at fuzzer-process start — the `use_direct_lite` gate). The periodic stats line shows live distance when directed mode is active: `dist: avg:<tail avg> min:<observed min> max:<observed max>` (or `no-data`). `build_targets.sh --distance` builds both `*_dist.so` (no-ASAN) and `*_dist_asan.so` (ASAN) variants with the cmplog shim linked in, so `--cmplog` keeps them in direct_lite mode. Startup reports `[*] Distance instrumentation: detected` when the target carries the channel (the shim's `__afl_dist_flush` or a defined `__sanitizer_cov_trace_pc`), mirroring the AFL-instrumentation check. With `--elo` in directed mode, `aflgo` joins the Elo-arbitrated seed-strategy pool — a distance-pure arm picking `P(seed) ∝ exp(-2·norm_dist)` (distinct from the generic `weighted` arm, which blends distance with speed/size/entropy).
 - **AFLGo distance-annealed schedule** (`--schedule go`, requires `--target-functions`): wires the precomputed `avg_distance` (per-seed distance to directed targets) and `_anneal_progress` (exploration/exploitation annealing variable) into `SeedScorer.score()` for mutation budget scaling. During exploration phase (`anneal_progress` ≈ 0): uniform energy. During exploitation phase (`anneal_progress` → 1): `energy *= exp(β · (1 - norm_dist))` where `β = anneal_progress * 5`, capping at 100x. Seeds near the target get exponentially more mutations as the campaign matures. Previously these metrics only influenced seed selection but not mutation intensity.
-- **Power schedules** (`--schedule base|fast|coe|rare|mopt|lin|quad|go|aflgo|entropic`): AFL++ power schedules ported to control mutation budget per seed via `SeedScorer`. Each schedule modifies a base score (100) by frequency-based factors. Honggfuzz-style novelty decay, density, fertility, freshness, and entropy factors are applied multiplicatively on top. `entropic` (libFuzzer `-entropic`) scales energy by `1 + log2(1 + rare)`, where `rare` is the larger of `rare_edge_count`/`tc_ref` already collected for RARE/honggfuzz scoring — an approximation of libFuzzer's feature-frequency Shannon entropy using signal the fuzzer already tracks.
+- **Power schedules** (`--schedule base|fast|coe|rare|mopt|lin|quad|go|aflgo|entropic|doppler`): AFL++ power schedules ported to control mutation budget per seed via `SeedScorer`. Each schedule modifies a base score (100) by frequency-based factors. Honggfuzz-style novelty decay, density, fertility, freshness, and entropy factors are applied multiplicatively on top. `entropic` (libFuzzer `-entropic`) scales energy by `1 + log2(1 + rare)`, where `rare` is the larger of `rare_edge_count`/`tc_ref` already collected for RARE/honggfuzz scoring — an approximation of libFuzzer's feature-frequency Shannon entropy using signal the fuzzer already tracks.
+- **Power Doppler schedule** (`--schedule doppler`, `core/power_doppler.py`): ultrasound power Doppler on coverage. Slow time = successive mutants of one seed (32 per ensemble); pixel = edge; sample = `log2(1 + hits)` (raw SHM counts, not buckets). Wall filter: mean removal (static path), then SVD components whose participation ratio spans ≥ half the seed's edges (and ≥ 4) are dropped as clutter — an early reject moving the whole path at once ("flash"). CFAR: residual power per edge vs `σ² · χ²_{dof}(1 − 10⁻³)`, `σ²` = median residual variance (floor 10⁻³). Seed power = summed flow power / dof; energy = `log1p(p)/log1p(max p)` ∈ [0, 1], scaled to `[1, max_mult]` like `katz`. Unscored or static seeds stay 1×. Gram eigendecomposition (n×n) replaces the full SVD (~6× faster). Bounded: 64 open ensembles × 2048 edges (float32, ≤16 MiB), 4096 scores (LRU). SHM coverage only; cost ~100 µs/exec at 2k live edges (dict→array conversion dominates), zero when off. Unmeasured — see `docs/TODO.md`.
 - **Favored set / cull_queue** (`core/schedules.py`, `services/fuzzer.py`): AFL-style `top_rated` minimal-set-cover selection. For each edge, the cheapest seed covering it is selected, then a greedy cover builds the favored set. FAST and COE schedules apply energy bonuses to favored seeds (`_fast_factor`, `_coe_factor`, `coe_skip`). `_cull_queue()` runs periodically during fuzzing and updates `self._favored`; the score call site passes `favored=(seed_key in self._favored)` so the scheduler actually uses it.
 - **Bayesian seed quality** (`--bayesian`): `BayesianSeedQuality` (`core/seed_quality.py`) maintains a Beta-Bernoulli posterior per seed over `P(outcome = new_coverage)`. Thompson sampling naturally balances explore/exploit without a manual temperature knob — unexplored seeds have high posterior variance and get sampled. The `record_outcome()` feedback loop is now wired in `fuzz_one()` (was previously a dead code path with all posteriors stuck at Beta(1,1)). State is persisted to `seed_quality.json` and restored on resume.
 
@@ -587,7 +588,7 @@ fuzzer-tool rank ./target -d corpus -n 10 --dump top_seeds
 | `--wfc` | Wave Function Collapse structural generation (chunk reordering, pixel generation) |
 | `--enable-smt-z3` | Z3-based SMT solving for arithmetic constraint solving on cmplog pairs |
 | `--hw-perf` | Hardware performance counters via perf_event_open (instructions, branches, misses) |
-| `--schedule base\|fast\|coe\|rare\|mopt\|lin\|quad\|go\|aflgo\|entropic` | AFL++ power schedule (`aflgo` = exact AFLGo distance annealing, `entropic` = libFuzzer `-entropic`, log-scaled rare-feature energy) |
+| `--schedule base\|fast\|coe\|rare\|mopt\|lin\|quad\|go\|aflgo\|entropic\|doppler` | AFL++ power schedule (`aflgo` = exact AFLGo distance annealing, `entropic` = libFuzzer `-entropic`, log-scaled rare-feature energy, `doppler` = power Doppler flow energy) |
 | `--aflgo-cooling exp\|log\|lin\|quad` | Cooling schedule for the `aflgo` power factor (default exp) |
 | `--t-x MINUTES` | AFLGo time-to-exploitation in minutes; temperature cools to 1/20 over this window |
 | `--markov-order N` | Markov chain order(s), comma-separated (e.g. '0,1,2' for ensemble) |

docs/TODO.md

@@ -25,6 +25,7 @@
 - [ ] **ptrace breakpoints only on dominator-tree leaves** (2026-09-26) — a hit block implies its dominators ran, so `ptrace_coverage.py` could place int3 on leaves only and infer the rest. Edges `(prev, curr)` are not implied the same way; measure breakpoint count and edge-set loss on fuzzgoat first. See `docs/learnings/2026-09-26-dominators-chk-worst-case.md`.
 
 ## Scheduling
+- [ ] **A/B `--schedule doppler`** (2026-10-01) — power Doppler seed energy (`core/power_doppler.py`) is unit-tested and wired; never measured. Paired `bench_paired.py` vs `fast` on clang-built fuzzgoat (Hard Rule 52). Open: (a) ~100 µs/exec at 2k edges, mostly `get_edge_counts()` dict → numpy; a numpy accessor on `ShmCoverage` (exposes `_active_columns`, Hard Rule 34 — needs approval) removes it; (b) ensembles span picks, so a seed needs 32 picks to score — tune `ensemble` vs pick rate; (c) unused: `flow_edges()` (input-sensitive edge set) could feed position/operator targeting; (d) scores not persisted across `--resume`.
 - [ ] **A/B the OS / network scheduler ports** (2026-09-30) — `mlfq`, `stride`, `eevdf`, `bfq`, `sfq`, `codel`, `aimd`, `p2c` seed arms and `op_stride`, `op_p2c` are unit- and wiring-tested only. Run `tools/lib/bench_paired.py` per arm vs `seed_round_robin` / `drr` on fuzzgoat (clang, ASAN). Untuned: MLFQ allotment/boost, BFQ budget range, CoDel target/interval, AIMD alpha/beta/loss run. Open: (a) `sfq` groups only direct siblings (`parent_key`), and only under `--lineage`; a lineage-root flow would group whole families; (b) no arm persists state; (c) `seed_round_robin.select_seed` is O(n^2) per pick (`s in seed_ids` on a list): ~100 ms at 5000 seeds.
 - [ ] **A/B the effector/token/chunk/changed/rare_mask position arms** (2026-09-30) — wired and unit-tested; a 3k-exec fuzzgoat run confirms live signal (`changed`: 673 moved / 96 unmoved / 2231 unmeasured rounds; `rare_mask`: target on 328/3000 rounds) but fuzzgoat cannot rank position arms (see `pos_fibonacci` entry). Run `tools/lib/bench_paired.py` `pos-arena-{token,chunk,changed,rare-mask}` vs `pos-arena-uniform` on png_read (`chunk` needs a container corpus). Open: (a) `effector` had no drained map in 3k execs (SkipDet skipped 111/112 seeds) -- measure its reach on long runs before A/B, it is not subset-testable (needs the det stage); (b) `changed` credits only ~25% of rounds: parents without a recorded path hash (spliced/generated inputs) -- record one at admission or accept; (c) neither `changed` nor `rare_mask` persists state.
 - [ ] **Pre-existing, found 2026-09-30:** `test_regression_hail_mary_gates.py` fails on base (`cuckoo_seed_filter`, `swap_walk` missing from `_HAIL_MARY_FLAGS` or an exclusion); `tools/build_targets.sh` ASAN variants fail in the cloud container (non-ASAN builds fine).

docs/architecture.dot

@@ -56,7 +56,7 @@ digraph fuzzer_tool {
     subgraph cluster_sched {
         label="1 · Scheduling   —   what to fuzz next";
         color="#bcd0c4"; fillcolor="#f2f8f4"; fontcolor="#33604a";
-        picker [label="services/seed_picker.py  +  core/schedules.py\lweights · Pareto front · crowding · saturation gate\lFAST COE RARE MMOPT LIN QUAD GO AFLGO ENTROPIC KATZ\lKRUSKAL-COUNT (coupling walkers · recombination)\l",
+        picker [label="services/seed_picker.py  +  core/schedules.py\lweights · Pareto front · crowding · saturation gate\lFAST COE RARE MMOPT LIN QUAD GO AFLGO ENTROPIC KATZ DOPPLER\lKRUSKAL-COUNT (coupling walkers · recombination)\l",
                 fillcolor="#d8eade", fontcolor="#1f4433"];
         bandit [label="core/schedulers/ (16)  +  elo · shapley\lmonte_carlo mopt exp3 gp_ucb cmaes contextual\lducb swucb kl_ducb kl_swucb cucb hierarchical replicator epsilon_greedy mcts katz\l",
                 fillcolor="#d8eade", fontcolor="#1f4433"];