arxiv: 2604.07593 · v1 · submitted 2026-04-08 · 💻 cs.AI

Recognition: unknown

Too long; didn't solve

Luc\'ia M. Cabrera , Isaac Saxton-Knight

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:25 UTC · model grok-4.3

classification 💻 cs.AI

keywords large language modelsmathematical benchmarksprompt lengthsolution lengthmodel performanceadversarial datasetreasoning evaluation

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The pith

Longer prompts and solutions in math problems increase large language model failure rates.

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

The paper builds a new adversarial dataset of expert-authored mathematics problems to examine how two structural features affect model behaviour. It reports that both longer prompts and longer solutions are associated with higher rates of model failure across multiple large language models. A secondary normalized analysis finds that these length variables retain weak negative links to the degree of disagreement between models. The central result is that structural length tracks empirical difficulty on this collection of problems.

Core claim

Both prompt length and solution length correlate positively with increased model failure across models on the newly constructed adversarial dataset of expert-authored mathematics problems; under a difficulty-adjusted analysis, both variables retain weak negative associations with realised model separation, slightly stronger for prompt length.

What carries the argument

The adversarial dataset of expert-authored mathematics problems, together with the measurement of prompt length and solution length as predictors of model failure.

If this is right

Models exhibit higher failure rates on mathematics problems that have longer prompts.
Problems whose solutions are longer also produce higher failure rates across the tested models.
After difficulty adjustment, length still shows a weak negative relation to how much models disagree with one another.

Where Pith is reading between the lines

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

Benchmark creators could reduce length-related confounds by constructing problems of matched lengths when the goal is to isolate pure reasoning ability.
Apparent gains in model reasoning performance might partly reflect improved handling of longer text rather than deeper mathematical insight.
Future evaluations could test whether length effects persist when models are given explicit length-normalized prompts or chain-of-thought scaffolds.

Load-bearing premise

The new dataset's difficulty adjustment fully isolates length effects from other factors such as topic complexity or authoring style.

What would settle it

A controlled experiment in which prompt and solution lengths are varied independently while holding topic, style, and other features fixed, showing no corresponding rise in model failure rates.

Figures

Figures reproduced from arXiv: 2604.07593 by Isaac Saxton-Knight, Luc\'ia M. Cabrera.

**Figure 2.** Figure 2: Failure fraction xi,m as a function of structural length variables. Error bars show 95% bootstrap confidence intervals. the dependence of cross-model variance on mean failure fraction, and instead focuses on the variance achieved at a given difficulty level: Var gi = Vari µi(1 − µi) . (4) This metric measures the fraction of the theoretical maximum variance achieved by problem i at its observed difficulty… view at source ↗

**Figure 3.** Figure 3: Predicted failure fraction xi,m as a function of Li = log(1 + word count) under the fitted mixed-effects model. Solid lines show model-specific fits; the dashed line shows the global fixed effect. 4 Discussion Our work studies how two objectively measurable structural variables, prompt length and referencesolution length, relate to model failure and model separation on an adversarially constructed mathem… view at source ↗

read the original abstract

Mathematical benchmarks consisting of a range of mathematics problems are widely used to evaluate the reasoning abilities of large language models, yet little is known about how their structural properties influence model behaviour. In this work, we investigate two structural length variables, prompt length and solution length, and analyse how they relate to model performance on a newly constructed adversarial dataset of expert-authored mathematics problems. We find that both prompt and solution lengths correlate positively with increased model failure across models. We also include a secondary, exploratory analysis of cross-model disagreement. Under a difficulty-adjusted normalised analysis, both variables retain weak negative associations with realised model separation, slightly stronger for prompt length. Overall, our main robust finding is that structural length is linked to empirical difficulty in this dataset.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces a new expert-authored adversarial math dataset and reports positive correlations between prompt/solution length and model failure rates, but the difficulty normalization leaves open the possibility of confounding by topic or style.

read the letter

The key point for you is that this work puts together a fresh adversarial collection of math problems written by experts and then shows that longer prompts and longer solutions tend to line up with higher failure rates across several models. The association stays weakly present after they try to adjust for difficulty, at least in their normalized view of model separation.

Referee Report

2 major / 1 minor

Summary. The paper constructs a new adversarial dataset of expert-authored mathematics problems and examines correlations between two structural length variables (prompt length and solution length) and LLM failure rates. It reports positive correlations between both lengths and increased model failure across models. A secondary difficulty-adjusted normalized analysis finds that both variables retain weak negative associations with realized model separation (slightly stronger for prompt length). The main claim is that structural length is linked to empirical difficulty in this dataset. The work also includes an exploratory analysis of cross-model disagreement.

Significance. If the reported correlations hold after proper controls, the result would provide evidence that problem length is a structural factor influencing LLM reasoning failures on math tasks, potentially informing benchmark design and model evaluation. The use of an expert-authored adversarial dataset and the attempt at difficulty normalization are positive features, but the absence of key methodological details prevents assessment of whether the central empirical claim is robust.

major comments (2)

[Abstract] Abstract: The central claims of positive correlations with model failure and persistence after difficulty-adjusted normalization are reported without sample sizes, error bars, exact statistical methods (e.g., Pearson vs. Spearman, p-values), or exclusion criteria. These omissions are load-bearing because the soundness of the correlation analysis cannot be evaluated from the given text.
[Analysis description] The difficulty adjustment procedure is not described (e.g., source of difficulty scores, inter-rater reliability if expert-rated, normalization formula, or how it decouples length from topic complexity and authoring style). This is load-bearing for the claim that structural length is linked to empirical difficulty, as the skeptic concern about residual confounding cannot be assessed.

minor comments (1)

[Abstract] The abstract refers to 'realised model separation' without defining the term or linking it to the cross-model disagreement analysis; clarify the metric and its relation to the primary failure-rate results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment below and will revise the manuscript to improve the clarity and completeness of the statistical reporting and methodological descriptions.

read point-by-point responses

Referee: [Abstract] Abstract: The central claims of positive correlations with model failure and persistence after difficulty-adjusted normalization are reported without sample sizes, error bars, exact statistical methods (e.g., Pearson vs. Spearman, p-values), or exclusion criteria. These omissions are load-bearing because the soundness of the correlation analysis cannot be evaluated from the given text.

Authors: We agree that these details are essential for evaluating the reported correlations and should be included in the abstract. We will revise the abstract to state the dataset sample size, specify the correlation method (Spearman rank correlation), include p-values and confidence intervals or error bars, and note any exclusion criteria. This will make the central claims directly assessable without requiring the reader to consult the full text. revision: yes
Referee: [Analysis description] The difficulty adjustment procedure is not described (e.g., source of difficulty scores, inter-rater reliability if expert-rated, normalization formula, or how it decouples length from topic complexity and authoring style). This is load-bearing for the claim that structural length is linked to empirical difficulty, as the skeptic concern about residual confounding cannot be assessed.

Authors: We acknowledge that the current description of the difficulty adjustment is insufficient for assessing robustness against residual confounding. We will add a dedicated subsection detailing the source of difficulty scores (expert author ratings), the normalization formula, any inter-rater considerations, and how the procedure controls for topic complexity and authoring style (e.g., via category-based stratification). This addition will directly address the concern and strengthen the interpretation of the adjusted analysis. revision: yes

Circularity Check

0 steps flagged

Empirical correlation study with no self-referential derivations or fitted predictions

full rationale

The paper conducts an empirical analysis of correlations between structural lengths (prompt and solution) and model failure rates on a newly constructed dataset, including a difficulty-adjusted normalization. No equations, derivations, or predictions are presented that reduce the reported results to inputs by construction. The central findings rely on standard statistical correlations and exploratory analysis rather than any self-definitional, fitted-input, or self-citation load-bearing steps. The difficulty adjustment procedure is described at a high level but does not involve any circular reduction of the length-difficulty link to itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard statistical correlation methods applied to a new dataset; no free parameters, invented entities, or non-standard axioms are evident from the abstract.

axioms (1)

domain assumption Difficulty adjustment via normalisation isolates length effects from other problem features.
Invoked in the secondary analysis of model separation.

pith-pipeline@v0.9.0 · 5410 in / 1132 out tokens · 46956 ms · 2026-05-10T17:25:04.709335+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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