arXiv:2606.03858v1 Announce Type: new Abstract: Despite the pivotal role of numerical reasoning as the cornerstone of mathematical capabilities in large language models (LLMs) across applications, few benchmarks evaluate LLMs by integrating numerical processing and mathematical reasoning, hindering the interpretability of failures in math tasks. We introduce PyraMathBench, a comprehensive hierarchical benchmark with 32,505 questions derived from 7,404 math word problems, spanning 4 key cognitive aspects, 14 subcategories, and 2 modalities. Experiments reveal that LLMs' performance is severely
The proliferation of LLMs across various applications necessitates robust mathematical capabilities, making the development of comprehensive evaluation benchmarks critical at this stage of AI development.
A nuanced understanding of LLM mathematical reasoning failures is crucial for developing more reliable and capable AI, impacting scientific discovery, financial modeling, and engineering applications.
The introduction of PyraMathBench provides a more granular and diagnostic tool for assessing LLM mathematical performance, moving beyond simple accuracy to identify specific areas of weakness.
- · AI researchers
- · LLM developers
- · Quantitative fields
- · Overly simplistic LLM benchmarks
LLMs will be developed with more targeted improvements on numerical reasoning and mathematical problem-solving through better diagnostic tools.
Enhanced mathematical capabilities in LLMs could accelerate progress in scientific research and complex engineering design by providing more reliable AI assistants.
As LLMs become more mathematically robust, they could automate increasingly sophisticated tasks in finance and R&D, potentially leading to new economic efficiencies and job reconfigurations.
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Read at arXiv cs.AI