NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?
We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move beyond reproduction toward discovery on real scientific problems. NatureBench is built on NatureGym, an automated pipeline that constructs a standardized, per-task containerized environment from a source paper, addressing the environment-fragmentation problem that has limited the credibility of prior agent-on-research benchmarks. Evaluating ten frontier agent configurations under a strict web-search-disabled protocol, we find that the strongest model surpasses SOTA on only 17.8% of tasks under the g>0.1 criterion...
| Authors | Yuru Wang, Lejun Cheng, Yuxin Zuo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 June 2026 |
| Last updated at source | 6 July 2026 |
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- FrontisAI/NatureBenchDataset
- FrontisAI/NatureBench-HarborDataset
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