Google DeepMind 与多所高校发布 TheoPhysEval,用 50 道未公开的研究级理论物理题测试 AI 智能体,每题约需领域专家一天完成。GPT-6-Astra 在 Codex 中联网求解 49/50,单题成本低于 3 美元;它与 Claude Code 中的 Opus 5.5 在不联网时均解出 46/50,准确率变化很小,作者据此认为模型可能已记住大量物理文献。
论文用50道未公开研究级物理题测试智能体,并给出成本与联网与否的对比数据。
New paper from Google Deep Mind, and many top Universities finds the best AI agent tested solved 49 of 50 unpublished research-level theoretical physics problems, each roughly a day's work for a world expert.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Experts contributed 50 unpublished problems from their own research, each about a day's work for a specialist.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.
来源:Rohan Paul · x.com