{"ID":2829534,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12283","arxiv_id":"2512.12283","title":"Large Language Models have Chain-of-Affect","abstract":"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.","short_abstract":"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-l...","url_abs":"https://arxiv.org/abs/2512.12283","url_pdf":"https://arxiv.org/pdf/2512.12283v2","authors":"[\"Junjie Xu\",\"Xingjiao Wu\",\"Luwei Xiao\",\"Yuzhe Yang\",\"Jie Zhou\",\"Zihao Zhang\",\"Luhan Wang\",\"Yi Huang\",\"Nan Wu\",\"Yingbin Zheng\",\"Chao Yan\",\"Cheng Jin\",\"Honglin Li\",\"Liang He\"]","published":"2025-12-13T10:55:06Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}