AI Reasoning Models for Problem Solving in Physics
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915468458065920 |
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| author | Bralin, Amir Rebello, N. Sanjay |
| author_facet | Bralin, Amir Rebello, N. Sanjay |
| contents | Reasoning models are the new generation of Large Language Models (LLMs) capable of complex problem solving. Their reliability in solving introductory physics problems was tested by evaluating a sample of n = 5 solutions generated by one such model -- OpenAI's o3-mini -- per each problem from 20 chapters of a standard undergraduate textbook. In total, N = 408 problems were given to the model and N x n = 2,040 generated solutions examined. The model successfully solved 94% of the problems posed, excelling at the beginning topics in mechanics but struggling with the later ones such as waves and thermodynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Reasoning Models for Problem Solving in Physics Bralin, Amir Rebello, N. Sanjay Physics Education Reasoning models are the new generation of Large Language Models (LLMs) capable of complex problem solving. Their reliability in solving introductory physics problems was tested by evaluating a sample of n = 5 solutions generated by one such model -- OpenAI's o3-mini -- per each problem from 20 chapters of a standard undergraduate textbook. In total, N = 408 problems were given to the model and N x n = 2,040 generated solutions examined. The model successfully solved 94% of the problems posed, excelling at the beginning topics in mechanics but struggling with the later ones such as waves and thermodynamics. |
| title | AI Reasoning Models for Problem Solving in Physics |
| topic | Physics Education |
| url | https://arxiv.org/abs/2508.20941 |