arXiv:2606.07591v1 Announce Type: new 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
The proliferation of AI coding agents necessitates robust methodologies for evaluating their autonomous research capabilities in scientific domains.
This benchmark is crucial for validating the efficacy and trustworthiness of AI in scientific discovery, accelerating research, and potentially automating significant portions of the scientific method.
The ability to systematically and objectively evaluate end-to-end autonomous scientific research agents moves from theoretical discussion to practical, quantifiable assessment, enabling faster development and deployment.
- · AI agent developers
- · Scientific research institutions
- · Drug discovery
- · Materials science
- · Manual data processing roles
- · Research validation service providers
The ResearchClawBench allows for direct comparison and improvement of AI agents designed for scientific research.
Accelerated scientific discovery across multiple domains due to more effective AI research assistants and autonomous systems.
A potential shift in the scientific funding landscape, prioritizing projects that leverage highly validated autonomous AI research platforms.
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Read at arXiv cs.LG