Investigating LLM's Problem Solving Capability -- a Study on Statics Questions
A new study evaluates LLM performance on statics problems using a model distillation approach. LLMs perform well on text-only problems but accuracy drops when diagrams and multi-step reasoning are introduced. The decline is primarily due to difficulties in multi-step reasoning, not image recognition limits.
[2606.26103] Investigating LLM's Problem Solving Capability -- a Study on Statics Questions
[Submitted on 30 Apr 2026]
Title:Investigating LLM's Problem Solving Capability -- a Study on Statics Questions
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Abstract:Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education, due to their demonstrated ability to complete assignments and examinations across a wide range of subjects. Although prior studies have examined the educational impact of LLMs, much of the existing work relies on public or open problem datasets and lacks topic-specific analysis. In engineering education, especially within mechanical engineering, systematic investigations of LLM performance on specific problem types remain limited. Instead of using traditional methods that directly ask textbook questions to an LLM tool, our study adopts a model distillation process to evaluate LLM capabilities in solving statics problems. By distilling ChatGPT, we extracted 25 text-only statics questions and further constructed two additional datasets by adding diagrams and modifying their numerical values. Experimental results show that while LLMs perform well on text-only statics problems, their accuracy decreases when diagrams are introduced and the problems require multi-step reasoning. Further analysis suggests that this performance drop is not primarily caused by limitations in image recognition, but rather by difficulties in multi-step reasoning and in consistently applying extracted visual information across successive solution stages.
Comments: 9 pages, Engineering and Technology Symposium 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26103 [cs.CL]
(or arXiv:2606.26103v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.26103
arXiv-issued DOI via DataCite
Submission history
From: Hung-Fu Chang [view email] [v1] Thu, 30 Apr 2026 20:17:09 UTC (791 KB)
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