@inproceedings{kuang-etal-2026-atomic,
title = "From Atomic to Agentic: Towards Interpretable Evaluation of {LLM}s' Agentic Mathematical Capabilities",
author = "Kuang, Jiayi and
Li, Yinghui and
Song, Yunze and
Chen, Keyu and
Shen, Zhifeng and
Li, Yangning and
Wang, Yidong and
Yin, di and
Qiao, Ruizhi and
Sun, Xing and
Jin, Kai and
Shen, Ying and
Lin, Liang and
Yu, Philip S.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.194/",
pages = "4437--4465",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate only final answers. This outcome-oriented evaluation provides limited diagnostic value for identifying process-level failures or rigorous logic, failing to guide the transformation of LLMs into robust agents. To bridge this gap, we present a process-level benchmark designed to evaluate the inherent agentic mathematical reasoning abilities of LLMs. Our framework aligns problem-solving agentic behaviors with a structured taxonomy of reusable mathematical atomic capabilities. We design a comprehensive suite of planning, action, and feedback tasks across both textual and multimodal contexts, supported by an automated pipeline that synthesizes high-quality trajectories and produces fine-grained annotations via controlled LLM rewriting. Experiments reveal that models with stronger end-to-end accuracy can exhibit markedly different agentic capability profiles. This demonstrates that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents."
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<abstract>Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate only final answers. This outcome-oriented evaluation provides limited diagnostic value for identifying process-level failures or rigorous logic, failing to guide the transformation of LLMs into robust agents. To bridge this gap, we present a process-level benchmark designed to evaluate the inherent agentic mathematical reasoning abilities of LLMs. Our framework aligns problem-solving agentic behaviors with a structured taxonomy of reusable mathematical atomic capabilities. We design a comprehensive suite of planning, action, and feedback tasks across both textual and multimodal contexts, supported by an automated pipeline that synthesizes high-quality trajectories and produces fine-grained annotations via controlled LLM rewriting. Experiments reveal that models with stronger end-to-end accuracy can exhibit markedly different agentic capability profiles. This demonstrates that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.</abstract>
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%0 Conference Proceedings
%T From Atomic to Agentic: Towards Interpretable Evaluation of LLMs’ Agentic Mathematical Capabilities
%A Kuang, Jiayi
%A Li, Yinghui
%A Song, Yunze
%A Chen, Keyu
%A Shen, Zhifeng
%A Li, Yangning
%A Wang, Yidong
%A Yin, di
%A Qiao, Ruizhi
%A Sun, Xing
%A Jin, Kai
%A Shen, Ying
%A Lin, Liang
%A Yu, Philip S.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kuang-etal-2026-atomic
%X Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate only final answers. This outcome-oriented evaluation provides limited diagnostic value for identifying process-level failures or rigorous logic, failing to guide the transformation of LLMs into robust agents. To bridge this gap, we present a process-level benchmark designed to evaluate the inherent agentic mathematical reasoning abilities of LLMs. Our framework aligns problem-solving agentic behaviors with a structured taxonomy of reusable mathematical atomic capabilities. We design a comprehensive suite of planning, action, and feedback tasks across both textual and multimodal contexts, supported by an automated pipeline that synthesizes high-quality trajectories and produces fine-grained annotations via controlled LLM rewriting. Experiments reveal that models with stronger end-to-end accuracy can exhibit markedly different agentic capability profiles. This demonstrates that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
%U https://aclanthology.org/2026.emnlp-main.194/
%P 4437-4465
Markdown (Informal)
[From Atomic to Agentic: Towards Interpretable Evaluation of LLMs’ Agentic Mathematical Capabilities](https://aclanthology.org/2026.emnlp-main.194/) (Kuang et al., EMNLP 2026)
ACL
- Jiayi Kuang, Yinghui Li, Yunze Song, Keyu Chen, Zhifeng Shen, Yangning Li, Yidong Wang, di Yin, Ruizhi Qiao, Xing Sun, Kai Jin, Ying Shen, Liang Lin, and Philip S. Yu. 2026. From Atomic to Agentic: Towards Interpretable Evaluation of LLMs’ Agentic Mathematical Capabilities. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4437–4465, Budapest, Hungary. Association for Computational Linguistics.