ResearchClawBench

Name: ResearchClawBench
Author: InternScience

Verified

🦞 ResearchClawBench: Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery

258stars

22forks

Jupyter Notebook

Installation

# Add to your Claude Code skills
git clone https://github.com/InternScience/ResearchClawBench

Getting Started

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Security ReportVerified

Last scanned: 5/30/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-30T16:25:40.966Z",
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

Frequently Asked Questions

What is ResearchClawBench?

ResearchClawBench is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by InternScience. 🦞 ResearchClawBench: Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery. It has 258 GitHub stars.

Is ResearchClawBench safe to use?

Yes. ResearchClawBench passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install ResearchClawBench?

Clone the repository with "git clone https://github.com/InternScience/ResearchClawBench" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is ResearchClawBench written in?

ResearchClawBench is primarily written in Jupyter Notebook. It is open-source under InternScience on GitHub, so you can review or fork the full source.

Are there alternatives to ResearchClawBench?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh ResearchClawBench against similar tools.

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⚠️ Third-Party Software Notice

This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.

Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.

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Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery

ResearchClawBench is a benchmark that measures whether AI coding agents can independently conduct scientific research — from reading raw data to producing publication-quality reports — and then rigorously evaluates the results against real human-authored papers.

Unlike benchmarks that test coding ability or factual recall, ResearchClawBench asks: given a curated scientific workspace and the same research goal, can an AI agent arrive at the same (or better) scientific conclusions?

Overview

✨ Highlights

🎬 Demo

https://github.com/user-attachments/assets/94829265-80a8-4d61-a744-3800603de6d9

💡 Why ResearchClawBench?

Most AI benchmarks evaluate what models know. We evaluate what agents can do.

Real science, not toy problems. 40 tasks sourced from published papers across 10 disciplines, each with curated experimental datasets.
Two-stage pipeline. Autonomous research first, rigorous evaluation second — just like peer review.
Fine-grained, multimodal scoring. A weighted rubric (checklist) with text and image criteria, judged by an LLM acting as a strict peer reviewer.
Agent-agnostic. Ships with built-in support for Claude Code, Codex CLI, ARIS Codex, OpenClaw, Nanobot, EvoScientist, ResearchClaw, and a lightweight ResearchHarness baseline. Bring your own agent in one line.
From Re-Discovery to New-Discovery. Scoring above 50 means matching the original paper; above 70 means surpassing it. The frontier is wide open.

📢 News

2026-09-09 📊 Updated Qiushi Engine with a new complete set of 40 task results using DeepSeek-V4-Pro-Preview, refreshing leaderboard scores, run times, and cost estimates. Results are available on the Leaderboard.
2026-09-02 📊 Received a new AutoSciRub submission and refreshed its leaderboard scores, run times, and cost estimates. Results are available on the Leaderboard.
2026-09-02 📊 Evaluated Ligase with GPT-5.6-Sol as an additional community-provided autonomous research agent result. Results are available on the Leaderboard.
2026-08-20 📊 Received an updated AutoSciRub submission and refreshed its leaderboard scores. Results are available on the Leaderboard.
2026-08-19 📊 Evaluated EvoLab with GPT-5.5 as an additional community-provided autonomous research agent result. Results are available on the Leaderboard.
2026-08-17 📊 Updated InnoClaw with a newly submitted complete set of 40 task results using GPT-5.5. Results are available on the Leaderboard.
2026-08-12 📊 Evaluated AutoSciRub with Codex GPT-5.6-Terra as an additional community-provided autonomous research agent result. Results are available on the Leaderboard.

2026-07-28 📊 Evaluated InnoClaw with GPT-5.5 as an additional community-provided autonomous research agent result. Results are available on the Leaderboard.
2026-07-15 📊 Evaluated Qiushi with GPT-5.5 as an additional autonomous research agent. Results are available on the Leaderboard.
2026-07-09 📊 Evaluated Open Science with Claude-Opus-4.8 as an additional autonomous research agent. Results are available on the Leaderboard.
2026-07-08 📊 Evaluated Hy3-Preview, a preview model rather than a final release, as an additional standalone LLM with ResearchHarness. Results are available on the Leaderboard.
2026-07-01 📈 Added Pass@5 leaderboard results, including stability-oriented statistics across repeated runs. Results are available on the Leaderboard.
2026-06-24 📊 Evaluated GLM-5.2 as an additional standalone LLM with ResearchHarness. Results are available on the Leaderboard.
2026-06-09 📄 ResearchClawBench paper preprint is available on arXiv.
2026-06-09 📊 Evaluated MiniMax-M3 as an additional standalone LLM with ResearchHarness. Results are available on the Leaderboard.
2026-06-05 🧪 Added rcb-eval, a YAML-configured command-line evaluation workflow powered by ResearchHarness, supporting concurrent runs, repeated trials, automatic scoring, and Markdown evaluation reports with per-run and per-task statistics.
2026-06-03 🧬 Added leaderboard results for EvoScientist v0.1.1 while retaining EvoScientist v0.0.4 as a separate versioned baseline. Results are available on the Leaderboard.
2026-06-02 📊 Evaluated Claude-Opus-4.8 as an additional standalone LLM with ResearchHarness. Results are available on the Leaderboard.
2026-05-28 📊 Evaluated two additional standalone LLMs with ResearchHarness: Gemini-3.5-Flash and Qwen3.7-Max. Results are available on the Leaderboard.
2026-05-21 📊 Evaluated four additional standalone LLMs with ResearchHarness: Grok-4.3, Kimi-K2.6, MiMo-V2.5, and DeepSeek-V4-Pro. Results are available on the Leaderboard.
2026-04-30 📊 Evaluated standalone LLMs with ResearchHarness: Claude-Opus-4.7, Claude-Opus-4.6, GLM-5.1, Qwen3.6-Plus, Qwen3.5-397B-A17B, GPT-5.5, GPT-5.4, MiMo-V2-Pro, Kimi-K2.5, Grok-4.1, and Gemini-3.1-Pro. Results are available on the Leaderboard.
2026-04-13 🧭 Added built-in ARIS Codex UI support and documentation. Imported benchmark runs are supported in the leaderboard and run browser; one-click lau