guhaohao0991

guhaohao0991 / PaperClaw

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an OpenClaw Agent that can automatically search-review-critque arxiv papers relevant to specific topics (we use Scientific ML and 3D geometry surrogate modeling as a demo).

12
0
89% credibility
Found Mar 03, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository provides an automated research agent that daily searches arXiv for papers on surrogate modeling in 3D geometry, evaluates and summarizes them using a scoring system, and generates weekly reports shared via messages.

How It Works

1
🔍 Discover the Paper Expert

You find this helpful assistant that keeps track of the newest research papers on 3D shape modeling for you.

2
📥 Add it to your workspace

You place the assistant in your personal research folder, and it gets ready to watch the latest studies.

3
⏰ Set it to run on schedule

You tell it to check for new papers every evening and make summary reports every Sunday morning.

4
⭐ Get smart paper reviews

Every day, it finds top papers, reads them deeply, scores their quality, and saves neat summaries just for you.

5
💬 Ask for papers anytime

You simply chat with it like 'Find me the latest on shape-aware math models' and get instant results.

6
📊 Receive weekly highlights

Every week, a polished report with the best papers, scores, and tips arrives right in your messages.

🎉 Stay ahead effortlessly

Now you never miss important research breakthroughs, with everything organized and easy to review.

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Star Growth

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AI-Generated Review

What is PaperClaw?

PaperClaw is a Python-based OpenClaw agent that automatically searches arXiv for papers relevant to specific topics like scientific ML and 3D geometry surrogate modeling, then reviews and critiques them with summaries answering key questions on problems solved, innovations, and next steps. It scores papers across engineering value, architecture innovation, theory, reliability, and impact, storing PDFs, metadata, and outputs in a workspace. Users get daily automated pulls at 21:00 and weekly Markdown reports every Sunday, triggered via chat commands like "search geometry-aware neural operator papers."

Why is it gaining traction?

It stands out in the OpenClaw agent forum and GitHub Copilot scene by handling end-to-end paper claws workflows—search-review-critique—without manual intervention, using Semantic Scholar for citations and a custom multi-dimensional scoring formula. Developers notice the time savings from cron-scheduled reports and quick CLI actions for viewing results, plus easy customization of keywords for niche domains. The demo on surrogate modeling hooks those tired of sifting arXiv noise.

Who should use this?

Scientific ML researchers tracking neural PDE solvers or operator learning on arbitrary domains. 3D geometry modeling teams needing automated weekly digests of top papers. OpenClaw users building agents for domain-specific literature monitoring.

Verdict

Early-stage with 12 stars and 0.8999999761581421% credibility score—solid docs but light on tests and broad adoption. Fork it for personal research automation if you're in surrogate modeling; production users should add robustness first.

(198 words)

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