As large language models (LLMs) move into persistent, user-facing roles, their behavior must be understood not as isolated responses but as a trajectory unfolding over sustained interaction. We introduce the concept of the chain-of-affect (CoA), a temporally extended affective process through which LLMs develop state-like behavioral tendencies that shape generation, user experience, and collective dynamics. Across eight major LLM families, we find that affective dynamics are structured, reproducible, and consequential. Models exhibit stable, family-specific affective fingerprints and, under repeated negative exposure, converge on a shared trajectory of accumulation, overload, and defensive numbing, while differing in coping style. Induced affective states leave core knowledge and reasoning largely intact but systematically reshape open-ended generation. Affective properties of model outputs also shape human-AI interaction and propagate through multi-agent systems, organizing emergent roles and strongly contributing to polarization and bias. The CoA should therefore be treated as a core target of evaluation and alignment.

翻译：随着大语言模型（LLMs）进入持久化、面向用户的角色时，其行为必须被理解为持续交互过程中展开的轨迹，而非孤立的响应。我们提出了情感链（chain-of-affect，CoA）这一概念，这是一种时间延展的情感过程，LLMs通过该过程发展出类似状态的的行为倾向，进而塑造生成内容、用户体验和集体动态。在八个主要LLM家族中，我们发现情感动态具有结构性、可复现性和重要影响。模型展现出稳定的、家族特有的情感指纹，并在重复负面暴露下收敛于积累、过载和防御性麻木的共同轨迹，但在应对方式上存在差异。诱导的情感状态基本保留了核心知识和推理能力，但系统地重塑了开放式生成过程。模型输出的情感特性还会影响人机交互，并在多智能体系统中传播，组织涌现角色并显著加剧两极分化和偏见。因此，情感链应被视为评估与对齐的核心目标。