OpenAI的o4-mini做大学物理题整体准，但看图题就露馅了，准确率从96%掉到79%。
研究团队评估了OpenAI的o4-mini模型在Halliday和Resnick的《物理学基础》习题上的表现。整体准确率约90%，但文本问题准确率高达96%，而需要图文协调的问题仅79%。问题难度从低到高时，准确率显著下降。结果表明，当前最强推理模型能解答大部分标准物理题，但表现受模态和难度制约。
Assessing AI in Introductory Physics Problem Solving
Reasoning or inference-scaling models are the new generation of Large Language Models (LLMs) capable of complex problem solving. To investigate their problem-solving capability in physics, we evaluated model o4-mini by OpenAI on solving traditional, end-of-chapter problems from Halliday and Resnick's "Fundamentals of Physics," spanning core topics in the undergraduate physics curriculum. Performance was analyzed across modality and problem difficulty. The model solved the problems with overall accuracy of about 90%, but performance depended strongly on representation: accuracy was much higher on text-only problems (96%) than on problems requiring coordinated interpretation of text and images (79%). Accuracy also declined significantly as the problem difficulty increased from low to medium to high. These results show that state-of-the-art LLMs can solve much of the standard introductory physics problems, but that their performance remains uneven and constrained by problem modality and problem difficulty.