@inproceedings{hou-etal-2026-smart,
    title = "{SMART}: Evaluating {LLM}s' Mathematical Reasoning via a Human Cognitive Process-Inspired Benchmark",
    author = "Hou, Yujie  and
      Wang, Mei  and
      Zhong, Yaoyao  and
      Zhang, Ting  and
      Ma, Xuetao  and
      Huang, Hua",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1638/",
    pages = "35426--35452",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) have achieved remarkable performance across a wide range of mathematical benchmarks. However, concerns remain as to whether these successes reflect genuine reasoning or superficial pattern recognition. Existing evaluation methods, which typically focus either on the final answer or on the intermediate reasoning steps, reduce mathematical reasoning to a shallow input{--}output mapping, overlooking its inherently multi-stage and multi-dimensional cognitive nature. Inspired by P{'}olya{'}s problem-solving theory, we propose SMART, a benchmark that decomposes mathematical problem-solving into four cognitive dimensions: **S**emantic Understanding, **M**athematical Reasoning, **A**rithmetic Computation, and **R**eflection Refinemen**T**, and introduces dimension-specific tasks to measure the corresponding cognitive processes of LLMs. We apply SMART to 22 state-of-the-art open- and closed-source LLMs and uncover substantial discrepancies in their capabilities across dimensions. Our findings reveal genuine weaknesses in current models and motivate a new metric, the All-Pass Score, designed to better capture true problem-solving capability."
}