PRBench: End-to-end Paper Reproduction in Physics Research
AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. However, whether these agents can reliably perform end-to-end reproduction from real scientific papers remains an open question. We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics. Each task requires an agent to comprehend the methodology of a published paper, implement the corresponding algorithms from scratch, and produce quantitative results matching the original publication. Agents are provided only with the task instruction and paper content, an...
| Authors | Shi Qiu, Junyi Deng, Yiwei Deng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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