Retrieve accepted paper metadata from ML/DL/NLP/CV/Robotics/Security/SE conferences. Uses the OpenReview API, web scraping, CVF Open Access, and the DBLP API.
uv is a fast Python package manager used to manage dependencies.
# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"After installing, restart your terminal so the uv command is available.
git clone https://github.com/brightjade/paper-explorer.git
cd paper-explorer
uv syncThis creates a virtual environment and installs all required packages automatically.
The paper data is hosted as a GitHub Release asset. Run the setup script to download and extract it:
./setup.shThis downloads the latest data snapshot (~60 MB) and extracts it to data/.
Note: Requires either the GitHub CLI (
gh) orcurl. If you don't havegh, the script falls back tocurlautomatically.
Only needed if you want to crawl OpenReview conferences (ICLR, NeurIPS, ICML, COLM, CoRL). Other conferences are scraped from public websites and don't require authentication.
- Create a free account at https://openreview.net/signup
- Copy the example env file and fill in your credentials:
cp .env.example .env- Edit
.envwith your OpenReview username and password.
# Crawl one or more conferences
uv run ppr crawl iclr_2025
uv run ppr crawl iclr_2025 neurips_2025 icml_2025
# Enrich with citation counts and abstracts
uv run ppr enrich iclr_2025
uv run ppr enrich iclr_2025 neurips_2025 icml_2025
# Validate paper counts against DBLP
uv run ppr validate iclr_2025
uv run ppr validate iclr_2025 neurips_2025 --tolerance 0.15
# Build static JSON for web app
./build.shConference ID format: <venue>_<year> (e.g., iclr_2025). Selections indicate available paper tracks.
| Conference | 2026 | 2025 | 2024 | 2023 |
|---|---|---|---|---|
| ICLR | oral, poster | oral, spotlight, poster | oral, spotlight, poster | oral, spotlight, poster |
| NeurIPS | oral, spotlight, poster | oral, spotlight, poster | oral, spotlight, poster | |
| ICML | oral, spotlight, poster | oral, spotlight, poster | oral, poster | |
| AAAI | main | main | main | |
| IJCAI | main | main | main |
NeurIPS also includes datasets_oral, datasets_spotlight, datasets_poster tracks.
| Conference | 2025 | 2024 | 2023 |
|---|---|---|---|
| ACL | main, findings, industry | main, findings | main, findings, industry |
| EMNLP | main, findings, industry | main, findings, industry | main, findings, industry |
| NAACL | main, findings, industry | main, findings, industry | |
| COLM | main | main | |
| EACL | main, findings | main, findings | |
| COLING | main | main |
| Conference | 2026 | 2025 | 2024 | 2023 |
|---|---|---|---|---|
| CVPR | main | main | main | |
| ICCV | main | main | ||
| ECCV | main | |||
| WACV | main | main | main | main |
| Conference | 2025 | 2024 | 2023 |
|---|---|---|---|
| CoRL | oral, poster | main | oral, poster |
| ICRA | main | main | main |
| IROS | main | main | main |
| RSS | main | main | main |
| Conference | 2025 | 2024 | 2023 |
|---|---|---|---|
| USENIX Security | main | main | main |
| Conference | 2025 | 2024 | 2023 |
|---|---|---|---|
| ICSE | main | main | main |
| FSE | main | main | main |
| ASE | main | main | main |
| ISSTA | main | main | main |
Use the /survey command in Claude Code to generate a grounded literature survey from the accepted papers in this repository. Requires enriched data (papers_enriched.jsonl) for the target conferences.
# Specify conferences and year range
/survey I'm exploring efficient inference methods for large language models,
like speculative decoding and early exit strategies. Search NLP and ML
conferences from 2023-2025.
# Let it ask you for scope
/survey What papers exist on 3D object detection from point clouds?
# Target specific venues
/survey Find related work on code generation with LLMs. Search ICSE, FSE,
ASE, ACL, and EMNLP from 2023-2025.
The survey searches through real accepted papers, ranks by citation count, identifies datasets and benchmarks, and highlights research gaps. Output is saved to outputs/<topic>_<timestamp>.md.