[目的/意义]大语言模型（Large Language Mode, LLM）技术在同行评议中展现出巨大潜力的同时，也引发了关于学术伦理与规范的广泛争议。文章旨在从审稿人这一实践主体出发，探究其在真实工作流中与LLM协作的行为过程机制。[方法/过程]对20位具有LLM辅助审稿经历的审稿人进行深度访谈，并采用主题分析法进行数据分析。[结果/结论]构建了同行评议中的人智协作行为模型，包括协作行为条件、协作行为动机、协作行为策略、协作反馈评估、协作行为调适五要素。审稿人的行为决策受到主观规范和能力基础两类条件的调节，并在工具动机和逃避动机的驱动下进行。基于认知外包层次和对最终评审意见的影响程度，审稿人与LLM的任务分配策略可以被构建为“辅助—增强—共创—代理”的连续谱系，并伴随着风险管控策略，以对数据安全和信息质量进行控制。审稿人与LLM的协作行为是一个持续学习和调整的过程。在协作反馈评估的激励下，审稿人会做出相应的行为调适，包括LLM使用技能提升和协作策略调整。该行为模型为理解同行评议任务下人智协同的要素、路径和边界提供了新的视角和启示。

[Purpose/significance] While Large Language Model (LLM) demonstrates substantial potential in academic peer review, its application has also sparked widespread controversy regarding academic ethics and norms. Focusing on reviewers, this study investigates the behavioral processes and mechanisms underlying their collaboration with LLMs in real-world workflows. [Method/process] This study conducted in-depth interviews with 20 reviewers experienced in LLM-assisted peer review and employed thematic analysis for data analysis. [Result/conclusion] This study develops a human-AI collaborative behavior model in the context of peer review, consisting of five elements: collaborative conditions, collaborative motivations, collaborative strategies, collaborative feedback evaluation, and collaborative adaptation. Reviewers' behavioral decisions are moderated by subjective norms and capability foundations, and are driv⁃en by both instrumental and avoidance motivations. Based on the level of cognitive offloading and the degree of impact on the final review opinion, task allocation strategies between reviewers and LLMs can be structured as a continuum of "assistance–augmentation–co-creation agent." This continuum is accompanied by risk management strategies to ensureda⁃tasecurity and information quality. The collaboration between reviewers and LLMs is a continuous process of learning and adaptation. Driven by collaborative feedback evaluation, reviewers make corresponding behavioral adaptations, including the enhancement of LLM usage skills and the adjustment of collaborative strategies. This behavioral model provides new insights into the elements, pathways, and boundaries of human-AI collaboration in the context of peer review tasks.