arXiv:2604.07593v2 Announce Type: replace 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 mod
The proliferation of advanced large language models necessitates a deeper understanding of their limitations and biases, especially concerning reasoning tasks.
This research provides critical insights into the real-world performance constraints of large language models, guiding both development and deployment strategies for AI applications requiring robust reasoning.
The understanding of how structural properties like prompt and solution length significantly influence LLM performance on complex tasks is enhanced, moving beyond simple accuracy metrics.
- · AI researchers
- · LLM developers focused on reasoning
- · Companies building robust AI agents
- · Developers neglecting LLM reasoning limitations
- · Benchmarks solely focused on short-form problems
Further research will focus on developing LLMs and techniques robust to varying prompt and solution lengths in complex problem-solving.
This understanding could lead to more specialized LLMs or pre-processing techniques designed to handle specific problem structures, improving reliability in critical applications.
Improved LLM reasoning could accelerate the development and deployment of truly autonomous AI agents capable of complex decision-making in diverse environments.
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Read at arXiv cs.AI