Google DeepMind 与多所高校的新论文用专家提供的 50 道未公开研究级理论物理题测试 AI 智能体,每题约相当于专家一天的工作量。GPT-6-Astra 在 Codex 中联网解出 49 道,单题成本低于 3 美元;它和 Opus 5.5 在 Claude Code 中不联网均解出 46 道,去掉联网后准确率变化很小,作者据此认为模型可能已记住大量物理文献。
该研究用专家未公开的物理题测试智能体,并给出成本与联网与否的对比,可观察当前智能体在真实科研计算上的边界。
New paper from Google Deep Mind, and many top Universities finds the best AI agent tested solved 49 of 50 unpublished research-level theoretical physics problems, each roughly a day's work for a world expert.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Experts contributed 50 unpublished problems from their own research, each about a day's work for a specialist.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