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LLMs solve text-only statics problems accurately but lose ground when diagrams appear because multi-step reasoning and consistent visual application become harder.

2026-07-01 08:15 UTC pith:HSEUBJDJ

load-bearing objection The question set is generated by distilling from the same model family under test, so the reported drop when diagrams are added likely reflects construction bias more than a clean test of reasoning limits. the 2 major comments →

arxiv 2606.26103 v1 pith:HSEUBJDJ submitted 2026-04-30 cs.CL cs.AI

Investigating LLM's Problem Solving Capability -- a Study on Statics Questions

Tanner Culleton , Hung-Fu Chang This is my paper
classification cs.CL cs.AI
keywords large language modelsstatics problemsproblem solvingengineering educationmulti-step reasoningdiagramsmodel distillationaccuracy evaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The study extracts 25 statics questions through model distillation from ChatGPT and builds two variant sets by adding diagrams and changing numbers. LLMs handle the original text versions well yet show lower accuracy on the diagram versions, especially when multiple reasoning steps are required. The authors conclude the drop stems from trouble maintaining consistency across solution stages and applying visual details repeatedly, not from failures to interpret the images. This points to a targeted limitation when LLMs face engineering problems that mix visuals with sequential calculations. The approach of controlled dataset variants helps separate the contributions of visual input from reasoning demands.

Core claim

Using 25 distilled statics questions and their diagram and numerical variants, the work shows higher LLM accuracy on text-only versions than on versions that combine diagrams with multi-step reasoning. The performance decline is traced to difficulties in multi-step reasoning and in consistently applying extracted visual information across successive solution stages rather than to limitations in image recognition.

What carries the argument

Model distillation process that generates 25 text-only statics questions, followed by controlled construction of diagram-added and numerically modified versions to isolate visual and reasoning effects.

Load-bearing premise

The 25 distilled questions represent typical statics problems and the addition of diagrams plus numerical changes isolates the effects of visuals and multi-step reasoning without introducing other biases.

What would settle it

Running the same multi-step problems with diagrams replaced by equivalent text descriptions and checking whether accuracy returns to the level of the original text-only set.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • LLMs can serve text-based statics tasks but need better support for sequential visual integration in engineering problems.
  • The core limitation lies in reasoning consistency rather than basic image interpretation.
  • Engineering education applications of LLMs should prioritize methods that enforce consistent reuse of visual data across steps.
  • Numerical modification of questions helps verify that models are not relying on memorized answers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pattern of drop with diagrams may appear in other mechanical engineering areas that combine visuals and sequential calculations.
  • Testing whether explicit step-by-step prompts for visual extraction reduce the accuracy gap would directly follow from the reported cause.
  • Extending the variant construction method to dynamics or strength of materials problems could reveal whether the limitation is statics-specific.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript investigates LLM performance on statics problems via model distillation from ChatGPT to extract 25 text-only questions, then constructs variants by adding diagrams and modifying numerical values. It claims LLMs perform well on text-only versions but accuracy drops with diagrams and multi-step reasoning, attributing the drop primarily to difficulties in multi-step reasoning and consistent application of visual information rather than image recognition limitations.

Significance. If the central claim holds after methodological fixes, the work offers topic-specific empirical evidence on LLM limitations for visually grounded, sequential engineering problems, which could inform educational applications. The controlled construction of three datasets from the same base questions is a strength for isolating factors, though the absence of standard textbook benchmarks or statistical reporting limits broader impact. No reproducible code or falsifiable predictions are described.

major comments (2)
  1. [Abstract and dataset construction] Dataset construction (abstract): The 25 questions extracted via model distillation from ChatGPT risk selection bias, as the text-only subset is likely enriched for problems the tested model class can already solve or articulate. Adding diagrams and changing numbers therefore does not cleanly isolate visual information or multi-step reasoning effects; performance differences may partly reflect the artificial benchmark construction rather than intrinsic limitations on typical statics problems. This directly undermines the attribution of the accuracy drop to reasoning rather than image recognition.
  2. [Abstract and experimental results] Experimental results (abstract): The directional claim of accuracy decrease with diagrams and multi-step reasoning is stated without any measurement details, statistical tests, error analysis, controls, or description of the full experimental protocol. This absence makes it impossible to evaluate whether the evidence supports the claimed cause of the performance drop.
minor comments (1)
  1. [Abstract] The abstract does not specify which LLMs were tested, how accuracy was scored, or the exact modification rules for diagrams and numbers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point by point below and describe the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract and dataset construction] Dataset construction (abstract): The 25 questions extracted via model distillation from ChatGPT risk selection bias, as the text-only subset is likely enriched for problems the tested model class can already solve or articulate. Adding diagrams and changing numbers therefore does not cleanly isolate visual information or multi-step reasoning effects; performance differences may partly reflect the artificial benchmark construction rather than intrinsic limitations on typical statics problems. This directly undermines the attribution of the accuracy drop to reasoning rather than image recognition.

    Authors: The distillation process was deliberately used to obtain a controlled set of text-only questions that are solvable by the LLMs under test, so that subsequent performance drops could be attributed to the addition of diagrams or multi-step requirements rather than to an inability to solve the base problems. We agree that this introduces a risk of selection bias and that the resulting benchmark may not reflect the full distribution of typical statics problems. In revision we will expand the methods section with a fuller account of the distillation procedure and add an explicit limitations paragraph discussing selection bias and its implications for the claimed attribution. revision: partial

  2. Referee: [Abstract and experimental results] Experimental results (abstract): The directional claim of accuracy decrease with diagrams and multi-step reasoning is stated without any measurement details, statistical tests, error analysis, controls, or description of the full experimental protocol. This absence makes it impossible to evaluate whether the evidence supports the claimed cause of the performance drop.

    Authors: The abstract is intentionally brief; the body of the manuscript contains the experimental protocol and accuracy figures. To improve transparency we will (i) augment the abstract with the key quantitative accuracy numbers and (ii) add statistical tests, error analysis, and an explicit protocol description to the methods and results sections. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical measurement on constructed datasets

full rationale

The paper reports direct experimental accuracy measurements on three datasets (text-only, diagram-added, numerically modified) generated via model distillation from ChatGPT. No equations, fitted parameters, predictions derived from inputs, uniqueness theorems, or self-citation chains appear in the central claims. The performance differences are presented as observed outcomes rather than reductions by construction, satisfying the criteria for an independent empirical study.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on the representativeness of the 25 distilled questions and the assumption that performance differences isolate reasoning from vision; no independent verification of dataset quality is described.

free parameters (2)
  • Question count and selection = 25
    25 questions extracted via distillation; selection criteria unspecified.
  • Diagram and numerical modification rules
    Rules for adding diagrams and altering values not detailed.
axioms (1)
  • domain assumption Model distillation from ChatGPT yields unbiased, representative statics questions
    Invoked to justify dataset construction in the abstract.

pith-pipeline@v0.9.1-grok · 5717 in / 1271 out tokens · 46673 ms · 2026-07-01T08:15:16.610915+00:00 · methodology

0 comments
read the original 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.

Figures

Figures reproduced from arXiv: 2606.26103 by Hung-Fu Chang, Tanner Culleton.

Figure 4
Figure 4. Figure 4: Example results given by asking GPT for 50 statics questions to solve [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

discussion (0)

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Reference graph

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