AI Reasoning Models for Problem Solving in Physics

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 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.

Authors

Institutions

Publication Details

Published
2025-10-28
DOI
https://doi.org/10.1119/perc.2025.pr.bralin
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Reasoning Models for Problem Solving in Physics

Amir Bralin, N. Sanjay Rebello
Machine Learning in Materials Science
article

AI Reasoning Models for Problem Solving in Physics

Amir Bralin, N. Sanjay Rebello
article en

Abstract

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 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.

Purdue University West Lafayette (US), Astronomy and Space (AU)
National Science Foundation
Openalex Percentile: Top 75%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.