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REVIEW 4 major objections 5 minor 18 references

Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read LLMs show human-like mental rigidity on math problems, study finds

desk verdict The paper's new measurements on DeCaro-style math equivalence problems are real, but the central mental-set claim rests on an undefined and confounded 'Steps' metric, so the conclusion does not follow from the data. read the letter →

arxiv 2501.11833 v1 pith:5KRR2YJ3 submitted 2025-01-21 cs.CL cs.AI

Saiful Haq , Niyati Chhaya , Piyush Pandey , Pushpak Bhattacharya This is my paper · ORCID
classification cs.CLcs.AI
keywords mentalsetcognitiverigiditychain-of-thoughtlargelanguagemodelsmathematicalreasoningproceduralflexibilityin-contextlearningevaluationmetrics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper attempts to show that large language models, like humans, can fall into a cognitive "mental set": once a multi-step solution method works, they keep using it even when a single-step shortcut would do. To test this, the authors adapt a mathematics-equivalence experiment from cognitive psychology, presenting models with complex problems before shortcut problems or vice versa, and measuring both exact accuracy and the number of reasoning steps. They report that few-shot prompting with chain-of-thought improves accuracy but also raises step counts, concluding that LLMs need more than one step to reach higher accuracy, which they read as evidence of mental set. If right, this would mean standard benchmarks miss an important dimension of reasoning flexibility in LLMs.

What carries the argument

The central object is the "Steps" metric: the number of reasoning steps the model uses before producing the correct answer, averaged over successful problems. The experimental machinery is the DeCaro equivalence-task design, with six complex and six shortcut equations of identical numbers; ordering problems (complex-first versus shortcut-first) is meant to induce or reveal entrenched strategies. The claim hinges on interpreting an increase in steps under chain-of-thought prompting as evidence of cognitive rigidity rather than as an artifact of the prompt's instruction to reason step by step.

What would settle it

Record the actual generated chains for each problem and count the number of distinct arithmetic operations; if shortcut-first and complex-first conditions produce the same operation counts, the order manipulation does not induce mental set.

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Extended reading notes

Core claim

The paper claims that LLMs exhibit mental sets in mathematical reasoning: when asked to reason via chain-of-thought, all three tested models take three steps to solve problems that humans can solve in one, and this step inflation is interpreted as the models persisting with entrenched multi-step strategies rather than adopting efficient shortcuts. The authors also claim that in-context examples improve exact-match accuracy but do not change the number of steps, and that this is the first study to bring the mental-set concept from cognitive psychology into LLM evaluation for complex reasoning tasks.

Load-bearing premise

The conclusion depends on the assumption that the "Steps" metric measures strategy persistence, not simply the model following the prompt's instruction to show step-by-step reasoning.

Editorial extensions

If this is right

  • If chain-of-thought induces mental sets, then accuracy gains from chain-of-thought may come with an efficiency cost that current benchmarks, which score only final answers, ignore.
  • Evaluation protocols should include problem-order manipulations and step-efficiency metrics to capture adaptability, not just exact match.
  • In-context examples can raise accuracy but do not by themselves break entrenched strategies, since step counts stayed at three under chain-of-thought.
  • Models that solve shortcut problems in one step under few-shot prompting show that the capability exists, so the rigidity is context-dependent rather than absolute.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The step-count evidence could be strengthened by analyzing the content of the generated chains to verify that extra steps are redundant arithmetic rather than genuine alternative strategies.
  • A direct test of mental set would compare shortcut-first versus complex-first order under identical prompts; the paper's tables do not clearly isolate this order effect on steps, so a reanalysis could sharpen the claim.
  • If mental sets are real in LLMs, then curriculum design for in-context learning could deliberately reorder examples to prevent strategy entrenchment.
  • The same experiment could be run with vision-language models on visually presented equations, which the authors propose as future work, to see whether mental-set effects transfer across modalities.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript adapts DeCaro's (2016) mathematical-equivalence problems to test whether large language models (Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, GPT-4o) exhibit mental sets, defined as persistence with previously successful strategies. Three models are tested under few-shot (FS) and few-shot plus zero-shot chain-of-thought (FS+CoT) prompting across four conditions: shortcut problems alone (SP), shortcut problems after complex problems (SP+CPF), complex problems alone (CP), and complex problems after shortcut problems (CP+SPF). The reported metrics are Exact Match (EM) and a 'Steps' count. The paper concludes that LLMs require more than one step to achieve higher accuracy under FS+CoT, which it interprets as evidence of mental sets, and claims to be the first to integrate cognitive psychology concepts into LLM evaluation.

Significance. If the central claim were sound, the paper would introduce a genuinely new evaluation dimension for LLM reasoning, and the adapted equivalence-problem paradigm would be a useful test bed for cognitive rigidity in models. The matching of numerical values across complex and shortcut problems is a good control, and the authors report low scores honestly rather than selecting only favorable cases. However, the core evidence is not credible: the 'Steps' metric is undefined and appears to measure prompt-induced verbosity rather than strategy persistence; the order manipulation yields no interference on shortcut problems, which is the defining prediction of a mental set; and the sample sizes (six items per condition) are too small for the strong claims made. The paper also provides no prompts or code, so the results are not reproducible. The framing of the study is interesting, but the current manuscript does not establish its conclusion.

major comments (4)
  1. [§4, Table 2] The central conclusion in §6 ('LLMs require more than one step to achieve higher accuracy, reflecting presence of mental sets') rests entirely on the 'Steps' metric, yet the paper never defines what counts as a step. Section 4 only says that Steps 'tracks the number of steps taken to arrive at the correct solution.' Table 2 shows exactly 3 steps for every model, every condition, and every success under FS+CoT. This uniform value is most plausibly an artifact of the zero-shot chain-of-thought instruction to reason step by step, not a measure of an entrenched cognitive strategy. Without a definition, example outputs, or a manipulation check showing that the model persisted with a previously successful method, the step count cannot support the mental-set interpretation.
  2. [§5, Tables 1 and 2] A mental set produces an order effect: shortcut problems should be solved less efficiently (or at least not better) after complex problems than when presented alone. The data show the opposite. In Table 1, GPT-4o achieves EM 0.66 in SP+CPF versus 0.33 in SP; in Table 2, Llama-3.1-8B and Llama-3.1-70B both achieve EM 0.66 in SP+CPF versus 0.50 in SP. Step counts are 1 in all successful FS conditions and 3 in all successful FS+CoT conditions, with no difference between SP and SP+CPF. These results contradict the predicted interference and thus fail to provide any evidence of mental-set rigidity.
  3. [§5] The sentence 'Incorporating in-context examples, whether under the CPF or SPG conditions, boosts EM scores, except for the SP+CPF condition with the Llama-3.1-8b-instruct model using FS prompting' is not supported by the tables. Under FS+CoT, SP+CPF is never worse than SP for any model; under FS, only Llama-3.1-8B shows a decrease (0.16 to 0), the opposite of a boost. More importantly, the accompanying claim that in-context examples 'do not influence the number of steps' depends on the undefined Steps metric, so the negative result carries no weight.
  4. [§5, Tables 1 and 2] Each condition is based on only six problems, with no repeated runs, no confidence intervals, and no significance testing. Differences such as Llama-3.1-70B moving from EM 0 in CP to 0.66 in CP+SPF under FS prompting, or GPT-4o moving from 0.33 to 0.83, are reported as substantive findings. With n=6 and no stochastic reporting, these differences cannot be distinguished from sampling noise, and the paper should state this limitation explicitly rather than drawing conclusions from point estimates alone.
minor comments (5)
  1. [§5] The text refers to 'CPF or SPG conditions,' but 'SPG' is not defined anywhere; the intended abbreviation appears to be 'SPF'.
  2. [Tables 1 and 2] The captions mention 'shortcut problems (Sh),' but the table columns use 'SP'; please standardize the abbreviations.
  3. [Abstract and §1] The claim to be 'the first study to integrate cognitive psychology concepts into the evaluation of LLMs' is not established by the related-work section, which does not discuss prior work on cognitive biases or rigidity in LLM evaluation. This novelty claim should be softened or supported by an appropriate literature review.
  4. [§4] The exact prompts, temperature settings, decoding parameters, and the procedure for parsing the final answer to compute Exact Match are not reported, which limits reproducibility. The CoT prompt is particularly important because the step count may be directly dictated by its instructions.
  5. [Table 3] The column labeled 'Input' and 'Output' is clear, but the Problem Type entries and the ordering of the two problem types are easy to confuse with the experimental conditions; consider adding a separate column for 'Shortcut type' or a clearer label.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the mental-set claim is an empirical interpretation, not a construction that reduces to its inputs.

full rationale

The paper's chain is observational rather than derivational: it builds a math-equivalence dataset from DeCaro (2016), runs three LLMs under few-shot and few-shot-plus-chain-of-thought prompting, and interprets the reported Exact Match and step counts as evidence of mental sets. No parameter is fitted and then renamed as a prediction, no metric is defined in terms of the conclusion, and no load-bearing premise is justified only by the authors' own prior work. The central inference that more-than-one-step solutions 'reflect presence of mental sets' is an interpretive claim about the step-count data; its weakness is construct validity, since the step count may simply track the CoT instruction to reason step by step, which belongs to soundness rather than circularity. Accordingly, no step in the paper reduces by definition or by self-citation to the conclusion it is supposed to support.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no fitted parameters and no new entities. It depends on uncritically applying a human cognitive psychology paradigm to LLMs, on an undefined step-counting metric, and on a very small problem set. These are domain assumptions rather than free parameters.

assumptions (4)
  • domain assumption The DeCaro (2016) human mental set paradigm transfers to LLMs without modification.
    The experimental design in Section 4 is imported from human psychology with no check that order-induced rigidity manifests in LLMs.
  • domain assumption The number of steps an LLM takes is a valid measure of strategy efficiency and cognitive rigidity.
    Section 4 defines Steps as 'the number of steps taken to arrive at the correct solution' but never specifies the counting rule or validates it as a proxy for mental set.
  • domain assumption Six problems per condition are sufficient to detect order effects.
    Sections 3 and 4 use only six problems per condition and no statistical tests or power analysis are reported.
  • domain assumption Exact match on the final numeric answer is an appropriate measure of success for math equivalence problems.
    Section 4 uses EM; equivalent algebraic answers are not considered, though this is minor relative to the other assumptions.

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Cite this review

Pith. "Pith review of Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs." pith.science (2026). https://pith.science/paper/5KRR2YJ3

@misc{pith2026250111833,
  author       = {Pith},
  title        = {Pith review of: Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5KRR2YJ3}},
  note         = {Machine review of arXiv:2501.11833}
}
read the original abstract

In this paper, we present an investigative study on how Mental Sets influence the reasoning capabilities of LLMs. LLMs have excelled in diverse natural language processing (NLP) tasks, driven by advancements in parameter-efficient fine-tuning (PEFT) and emergent capabilities like in-context learning (ICL). For complex reasoning tasks, selecting the right model for PEFT or ICL is critical, often relying on scores on benchmarks such as MMLU, MATH, and GSM8K. However, current evaluation methods, based on metrics like F1 Score or reasoning chain assessments by larger models, overlook a key dimension: adaptability to unfamiliar situations and overcoming entrenched thinking patterns. In cognitive psychology, Mental Set refers to the tendency to persist with previously successful strategies, even when they become inefficient - a challenge for problem solving and reasoning. We compare the performance of LLM models like Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct and GPT-4o in the presence of mental sets. To the best of our knowledge, this is the first study to integrate cognitive psychology concepts into the evaluation of LLMs for complex reasoning tasks, providing deeper insights into their adaptability and problem-solving efficacy.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 10, 2026 · model on record in the stance chip above.