Background Understanding Classical Chinese remains a major challenge in Chinese education, especially in the National College Entrance Examination (NCEE). Although large language models (LLMs) exhibit strong reasoning capabilities, their performance on exam‐style Classical Chinese questions still suffers from instability and limited accuracy. Methods We propose a multiagent reasoning framework based on LLMs for Classical Chinese question answering. For each question type, standardized reasoning procedures are defined, and specialized agents are trained for subtasks including word interpretation, grammatical analysis, translation, and semantic summarization. A two‐round reasoning mechanism, consisting of an initial response followed by refinement using standard answers, is introduced to enhance consistency and robustness. Results Experiments on Gaokao‐style Classical Chinese questions demonstrate that the proposed framework achieves higher accuracy and greater reasoning stability than single‐agent systems and general‐purpose LLMs. In objective tasks, it outperforms strong Chinese‐oriented models such as Qwen‐Max and Baichuan‐4 by up to 6.8%. Conclusions The proposed multiagent framework improves both the interpretability and reliability of LLM‐based Classical Chinese understanding. It shows strong potential for applications in intelligent tutoring systems, curriculum support, and cognitive modeling of human‐like reasoning in educational contexts.
Liu et al. (Thu,) studied this question.