Paper from Google DeepMind and other institutions: the best AI agent solved 49 of 50 unpublished research-level physics problems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from Google DeepMind and several top universities shows that the best-performing AI agent in the test solved 49 of 50 unpublished research-level theoretical physics problems, each roughly equivalent to a day's work for a domain expert, at a cost of under $3 per problem.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.
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The paper tests agents on 50 research-level physics problems not previously released by experts, and provides a comparison of per-problem cost and with/without internet access.
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New paper from Google Deep Mind, and many top Universities finds the best AI agent tested solved 49 of 50 unpublished research-level theoretical physics problems, each roughly a day's work for a world expert.
at under $3 each.
Benchmarks for AI in physics often use textbook or contest problems, or block the tools real researchers use. So it was unclear how agents handle the calculations that fill daily research.
Experts contributed 50 unpublished problems from their own research, each about a day's work for a specialist. Agents worked alone, with and without web search, and were graded only on final numerical answers.
GPT-6-Astra in Codex solved 49 of 50 with web search, and both it and Opus 5.5 in Claude Code solved 46 of 50 without it. Accuracy barely changed without search, which suggests the models have memorized much of the physics literature.