Too long; didn't solve
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 analy...
| Authors | Lucía M. Cabrera, Isaac Saxton-Knight, Jocelyn D'Arcy |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 17 June 2026 |
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