NeurIPS 2026 Explorer

A 2D semantic map of the accepted NeurIPS 2026 papers, built from SPECTER2 embeddings of titles and abstracts. Nearby points are semantically similar papers, so a cluster is a topic.

Live site (after setup, see below): https://flecomet.github.io/neurips-explorer/

Derived from flecomet/cvpr-explorer, itself a fork of dataplayer12/cvpr-explorer. Original idea and design credit to @dataplayer12. Same license as upstream, see LICENSE.

Status

The code is unit-tested; the paper data has not been generated yet.

• OpenReview has not released the 2026 papers (the venue group has public_submissions = false, and every 2026 query returns 0 notes while 2025 returns papers). It also answers scripts running on GitHub's servers with a human-verification challenge. scrape.py (OpenReview) is kept for when the papers are published.
• neurips.cc already lists the accepted papers per location (Sydney, Atlanta, Paris). Its listing pages are rendered by JavaScript from two JSON files (/static/virtual/data/neurips-2026-orals-posters.json and ...-abstracts.json), which scrape_site.py downloads. The abstract extraction from a paper page was checked against the real site. The layout of the two JSON files was not known when the parser was written, so field names are looked up from lists of likely candidates, and every run prints the structure it found. python scrape_site.py --probe prints that structure without writing anything.

Features

• Map of all papers with topic names drawn on it; more names appear as you zoom in.
• Color by topic, location (Sydney / Atlanta / Paris), presentation type (oral / spotlight / poster, also encoded by marker size) or OpenReview primary area. Palette for presentation type is colorblind-safe.
• Topics list: click a topic to zoom to it and list its papers.
• Search over titles, authors, keywords, TL;DR and abstracts. Matches stay in place, the rest dim.
• Paper panel: abstract, TL;DR, keywords (click to search), PDF and OpenReview links, and the six most similar papers.
• Save papers to a list kept in the browser (localStorage), export as CSV or Markdown.
• Shareable URLs: ?p=<paper id>, ?q=<search>, ?c=<topic>, ?color=<mode>.
• Light and dark themes, keyboard shortcuts (/ search, Esc clear), usable on a phone.
• Fast start: the map (data.json, a few hundred KB gzipped) loads first, abstracts (details.json) load afterwards.

How it works

The pipeline is offline and the site is static (no backend, no API keys at serve time).

Step	Script	Output
Fetch accepted papers from neurips.cc (or OpenReview)	scrape_site.py (scrape.py)	data/neurips_2026_papers.json
Embed title + abstract with SPECTER2	embed.py	data/neurips_2026_specter2.npy (float16)
UMAP to 2D, HDBSCAN clusters, TF-IDF topic names, nearest neighbours	layout.py	data/neurips_2026_layout.json
Merge into the site payload	build_site.py	site/data.json, site/details.json

site/index.html renders the payload client-side with plotly.js.

Setup

1. Create the GitHub repository (public: GitHub Pages is free only for public repositories), push this code to main.
2. Settings, Pages, Source: GitHub Actions.
3. Actions tab, Refresh data, Run workflow with source neurips.cc. It runs the whole pipeline on a GitHub runner (embedding on CPU takes tens of minutes), commits data/, and starts the Pages deployment. The Probe neurips.cc step prints what the site returned; if the scrape step then fails, that output shows what the parser needs to change.
4. Other sources: committed uses data/neurips_2026_papers.json as it is in the repository. openreview does not work from GitHub runners (human-verification challenge). Once OpenReview publishes the papers, scrape from your own machine with python scrape.py, or from a browser: open https://openreview.net, paste tools/browser_download.js into the developer console, then run python scrape.py --raw neurips2026_raw.json. Commit the data and run the workflow with source committed. The venue ids are in config.py.

Run locally

pip install -r requirements-pipeline.txt
python scrape_site.py   # neurips.cc; for OpenReview: python scrape.py
python embed.py         # GPU recommended, CPU works
python layout.py        # --min-cluster-size 25 --n-neighbors 15
python build_site.py
python -m http.server -d site 8000   # http://localhost:8000

Tests: pip install -r requirements-dev.txt && python -m pytest.

Notes

• layout.py is deterministic for fixed inputs (UMAP random_state=42) so reruns keep the map stable. Changing the embeddings or paper set changes the map.
• Roughly a third of papers can end up "unclustered" at --min-cluster-size 25. Lower it for more, smaller topics.
• Saved papers never leave the browser.