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arXiv · 2605.28700

The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic

Dominika Agnieszka Długosz·Arlindo Oliveira·Natalia Díaz-Rodríguez
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Abstract

The GSM-Symbolic benchmark (Mirzadeh et al., 2025) reported consistent performance drops across 25 Large Language Models (LLMs) when tested on template-generated variants of GSM8K problems, concluding that the models lack genuine reasoning capabilities. We argue that this conclusion rests on shaky statistical ground. Re-evaluating 20 open-weight models using bootstrapped Generalised Linear Mixed Models with per-question random effects, we find that only 8 exhibit statistically significant performance changes under the original prompt format. Moreover, we identify a previously unacknowledged factor: the distribution of integers in problem texts of the main GSM-Symbolic dataset is systematically shifted towards larger values relative to the original GSM8K (K-S statistic = 0.12, p < 0.001), contradicting the original authors' claims. Controlling for this large-number effect accounts for significance in half of the remaining cases. Among models with statistically significant performance deltas, we identify distinct, model-specific behavioural failure profiles -- including fragility of variable binding, arithmetic limitations, and dual-task interference -- underscoring that blanket claims about LLM reasoning risk being both statistically premature and mechanistically misleading.

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Dominika Agnieszka Długosz, Arlindo Oliveira, Natalia Díaz-Rodríguez. 2026-05-27. The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic. https://arxiv.org/abs/2605.28700

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