LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening

Ming Zhang; Qiyuan Peng; Yinxi Wei; Yujiong Shen; Kexin Tan; Yuhui Wang; Zhenghao Xiang; Junjie Ye; Zhangyue Yin; Zhiheng Xi; Shihan Dou; Tao Gui; Maxm Pan; Ruizhi Yang; Qi Zhang; Xuanjing Huang

arXiv · 2605.19597

LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening

Ming Zhang·Qiyuan Peng·Yinxi Wei·Yujiong Shen·Kexin Tan·Yuhui Wang·Zhenghao Xiang·Junjie Ye·Zhangyue Yin·Zhiheng Xi·Shihan Dou·Tao Gui·Maxm Pan·Ruizhi Yang·Qi Zhang·Xuanjing Huang

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Abstract

Evaluating large language models (LLMs) on natural-language logical reasoning is essential because rule-governed tasks require conclusions to follow strictly from stated premises. Many existing logical-reasoning benchmarks are generated by templating natural-language items from sampled formulas, provide only coarse or unaudited formal annotations, and are now quickly saturated by frontier reasoning models. We present LLMEval-Logic, a Chinese logical reasoning benchmark built from realistic situational scenarios. Its pipeline forward-authors and expert-audits natural-language items together with their reference formalizations, verifies annotated answers with Z3, constructs expert rubrics for natural-to-formal grading, and hardens selected items through a closed-loop adversarial workflow. The benchmark is released in two paired subsets: a 246-item Base subset shipped with 1,400 expert-developed rubric atoms, and a 190-item Hard subset with 938 multi-step sub-questions over closed model spaces. Evaluating 14 frontier LLMs on LLMEval-Logic reveals substantial gaps in current models: the best model reaches only 37.5% Hard Item Accuracy, and even with reference symbols the highest joint Z3+Rubric formalization score among evaluated models reaches only 60.16%. Our benchmark is publicly available at https://github.com/llmeval/LLMEval-Logic.

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Ming Zhang, Qiyuan Peng, Yinxi Wei, Yujiong Shen, Kexin Tan, Yuhui Wang, Zhenghao Xiang, Junjie Ye, Zhangyue Yin, Zhiheng Xi, Shihan Dou, Tao Gui, Maxm Pan, Ruizhi Yang, Qi Zhang, Xuanjing Huang. 2026-05-19. LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening. https://arxiv.org/abs/2605.19597

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