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openreview-explorer

Search papers on OpenReview and retrieve peer reviews and rebuttals from major ML conferences (ICLR, NeurIPS, ICML). Core skill for FrontierPilot's Knowledge Exploration engine.

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skyllwt/FrontierPilot
最近来源活动
2026年3月17日 06:47
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SKILL.md
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name
openreview-explorer
description
Search papers on OpenReview and retrieve peer reviews and rebuttals from major ML conferences (ICLR, NeurIPS, ICML). Core skill for FrontierPilot's Knowledge Exploration engine.
version
1.0.0
# OpenReview Explorer This skill fetches papers, peer reviews, and author rebuttals from OpenReview — treating the review process as structured learning material for newcomers entering a research field. ## Why Reviews Matter Peer reviews reveal what the field actually cares about: common weaknesses, evaluation standards, and how top authors respond to criticism. This is knowledge that takes years to accumulate informally — FrontierPilot surfaces it directly. ## Python Interpreter Always use: `python3` (available in the OpenClaw container environment) ## Capabilities ### 1. Search papers by keyword + venue ```bash python3 /home/node/.openclaw/workspace/skills/openreview-explorer/scripts/search_papers.py \ --query "automl" \ --venue "ICLR" \ --year 2024 \ --limit 5 ``` ### 2. Get reviews + rebuttal for a paper ```bash python3 /home/node/.openclaw/workspace/skills/openreview-explorer/scripts/get_reviews.py \ --forum-id <FORUM_ID> \ --venue "ICLR" \ --year 2024 ``` ### 3. Get conference papers (all from a venue/year) ```bash python3 /home/node/.openclaw/workspace/skills/openreview-explorer/scripts/search_papers.py \ --venue "ICLR" \ --year 2024 \ --limit 10 ``` ## Workflow for Knowledge Exploration 1. User provides a topic (e.g., "diffusion models for image generation") 2. Run `search_papers.py` to find top relevant papers from recent ICLR/NeurIPS/ICML 3. For each promising paper, run `get_reviews.py` to retrieve: - Reviewer ratings and summaries - Detailed strengths/weaknesses - Author rebuttal (how the authors defended their work) 4. Synthesize: What do reviewers consistently praise? What weaknesses appear? What does the rebuttal reveal about the authors' intent? 5. Save synthesis to `memory/RESEARCH_LOG.md` ## Output Format Papers are returned as JSON. Present them as: - Title, authors, venue, year - Abstract excerpt - Forum ID (needed for get_reviews.py) Reviews are returned with: - Rating (e.g., "8: accept, good paper") - Summary, strengths, weaknesses - Confidence score - Rebuttal excerpt ## Environment Variables The scripts read credentials from environment variables. Set them before use: - `OPENREVIEW_USERNAME`: your OpenReview account email - `OPENREVIEW_PASSWORD`: your OpenReview account password - `OPENREVIEW_BASE_URL`: https://api2.openreview.net (default, usually no need to set) ## Supported Venues | Venue | Invitation Pattern | |-------|-------------------| | ICLR 2024 | ICLR.cc/2024/Conference/-/Submission | | ICLR 2023 | ICLR.cc/2023/Conference/-/Submission | | NeurIPS 2024 | NeurIPS.cc/2024/Conference/-/Submission | | ICML 2024 | ICML.cc/2024/Conference/-/Submission | ## Error Handling - If a paper has no reviews yet (e.g., under review), the script returns an empty list — note this to the user - Rate limit: space out requests for large batches (>20 papers) - If login fails, fall back to anonymous mode (public papers only, no reviews)
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