Google DeepMind 联合多所高校发布论文：GPT-6-Astra 解出 50 道未发表物理题中的 49 道
GPT-6-Astra 在新基准里解出 49/50 道研究级物理题，每道不到 3 美元，还能对比联网和不联网的差距。
Google DeepMind 与多所高校的研究者收集了 50 道未发表的研究级理论物理题，每道题约需世界级专家一天完成。GPT-6-Astra 在 Codex 中配合网络搜索解出 49 道，单题成本不到 3 美元。GPT-6-Astra 与 Opus 5.5 在 Claude Code 中不使用搜索也各解出 46 道。是否联网对准确率影响很小，论文据此推测模型已记住大量物理文献。评分只看最终数值答案，区别于以往依赖教科书或竞赛题的基准。
New paper from Google Deep Mind, and many top Universities finds the best AI agent tested solved 49 of 50 unpublished research-level theoretical physics problems, each roughly a day's work for a world expert.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Experts contributed 50 unpublished problems from their own research, each about a day's work for a specialist.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.
