ASyMOB: New Benchmark Reveals LLMs Struggle With Symbolic Math Generalization
Researchers introduced ASyMOB, a dataset of 35,368 symbolic math problems designed to test whether large language models genuinely understand mathematics or merely memorize patterns. The benchmark uses systematic perturbations to evaluate how well models generalize across variations of the same problem. The findings show most LLMs fail under minor changes, but top systems show robustness improvements when paired with computer algebra tools.
A new preprint from arXiv presents ASyMOB, a high-resolution benchmark for evaluating large language models on symbolic mathematics tasks including integration, limits, differential equations, series, and hypergeometric functions. The dataset contains 35,368 validated problems and uses symbolic, numeric, and equivalence-preserving transformations to systematically perturb seed problems, enabling fine-grained assessment of generalization rather than pattern memorization. The evaluation reveals that most models' performance collapses under minor perturbations, though top-performing systems exhibit a regime shift in robustness. The research also finds that integrated code tools stabilize performance, particularly for weaker models, and identifies cases where LLMs outperform traditional Computer Algebra Systems or where hybrid LLM-CAS approaches are necessary. The authors position ASyMOB as a diagnostic tool for advancing trustworthy AI in scientific discovery.
What's missing
The paper does not specify which specific LLM architectures or models were evaluated, making it difficult to assess which contemporary systems showed the regime shift in robustness. Additionally, the study does not discuss computational costs or inference time comparisons between LLM-only, CAS-only, and hybrid approaches.
What different sources said
- arXiv cs.AICenter
ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark
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