arXiv:2505.23851v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization with genuine reasoning. To address this gap, we present \textbf{ASyMOB}, a high-resolution dataset of \textit{35,368} validated symbolic math problems spanning integration, limits, differential equations, series, and hypergeometrics. Unlike prior benchmarks, \textbf{ASyMOB} systematically perturbs each seed problem using symbolic, numeric, and equivalence-preserving transformations, enabling a fine-graine
The increasing application of large language models (LLMs) to symbolic mathematics necessitates more robust and nuanced evaluation benchmarks to differentiate genuine reasoning from pattern memorization.
This new benchmark provides a higher resolution tool for assessing LLM capabilities in complex symbolic mathematics, which is crucial for advancing AI in scientific discovery and engineering.
The ability to accurately evaluate and compare LLMs on their symbolic reasoning will improve model development and highlight the true progress in AI's understanding of mathematical principles.
- · AI researchers
- · LLM developers
- · AI ethics and safety organizations
- · LLMs with superficial mathematical abilities
- · Benchmarks conflating memorization with reasoning
ASyMOB will become a standard benchmark for evaluating LLMs' symbolic math capabilities.
Improved LLM evaluation will accelerate the development of more capable AI for scientific and engineering problem-solving.
Advanced mathematical reasoning in AI could lead to breakthroughs in areas currently limited by human cognitive capacity.
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