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Google DeepMind and several top universities have published a paper testing AI agents' ability to solve unpublished, research-level theoretical physics problems. According to the paper, the best-performing AI agent solved 49 of 50 unpublished research-level theoretical physics problems, each roughly equivalent to a day's work for a domain expert, at a cost of under $3 per problem. The problems were contributed by experts from their own research, and the agents answered independently, split into two groups—with and without internet access—and were graded solely on the final numerical answer. GPT-6-Astra, with internet access in Codex, solved 49; it and Opus 5.5, without internet access in Claude Code, each solved 46, with accuracy changing very little when internet access was removed, leading the authors to suggest that the models may have already memorized a large body of physics literature.
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Oct 25
Rohan Paul
Paper from Google DeepMind and other institutions: the best AI agent solved 49 of 50 unpublished research-level physics problems
A new paper from Google DeepMind and several top universities shows that the best-performing AI agent in the test solved 49 of 50 unpublished research-level theoretical physics problems, each roughly equivalent to a day's work for a domain expert, at a cost of under $3 per problem. The problems were contributed by experts from their own research, and the agents answered independently, split into two groups—with and without internet access—and were scored only on the final numerical answer. GPT-6-Astra, with internet access in Codex, solved 49; it and Opus 5.5, without internet access in Claude Code, each solved 46, and removing internet access changed accuracy very little, leading the authors to suggest that the models may have memorized a large amount of physics literature.