ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research

• URL: http://arxiv.org/abs/2606.07591v2
• Date: Wed, 10 Jun 2026 02:02:36 GMT
• Title: ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research
• Abstract summary: We present ResearchClawBench, a benchmark for evaluating autonomous scientific research.<n>Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation.
• Score: 142.29356526274387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.

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