Large language model (LLM) agents often exhibit abrupt shifts in tone and persona during extended interaction, reflecting the absence of explicit temporal structure governing agent-level state. While prior work emphasizes turn-local sentiment or static emotion classification, the role of explicit affective dynamics in shaping long-horizon agent behavior remains underexplored. This work investigates whether imposing dynamical structure on an external affective state can induce temporal coherence and controlled recovery in multi-turn dialogue. We introduce an agent-level affective subsystem that maintains a continuous Valence-Arousal-Dominance (VAD) state external to the language model and governed by first- and second-order update rules. Instantaneous affective signals are extracted using a fixed, memoryless estimator and integrated over time via exponential smoothing or momentum-based dynamics. The resulting affective state is injected back into generation without modifying model parameters. Using a fixed 25-turn dialogue protocol, we compare stateless, first-order, and second-order affective dynamics. Stateless agents fail to exhibit coherent trajectories or recovery, while state persistence enables delayed responses and reliable recovery. Second-order dynamics introduce affective inertia and hysteresis that increase with momentum, revealing a trade-off between stability and responsiveness.

翻译：大型语言模型（LLM）智能体在长时间交互中常表现出语气与人格特征的突变，这反映了缺乏支配智能体层面状态的显式时间结构。尽管先前研究侧重于轮次局部情感分析或静态情绪分类，但显式情感动力学在塑造智能体长时程行为中的作用仍未得到充分探索。本研究探讨在外部情感状态上施加动力学结构是否能在多轮对话中诱导时间连贯性与受控恢复。我们引入一个智能体层面的情感子系统，该系统在语言模型外部维持连续的效价-唤醒-支配（VAD）状态，并受一阶和二阶更新规则支配。瞬时情感信号通过固定的无记忆估计器提取，并通过指数平滑或基于动量的动力学进行时间积分。生成的情感状态在不修改模型参数的情况下被注入回生成过程。采用固定的25轮对话协议，我们比较了无状态、一阶和二阶情感动力学。无状态智能体无法展现连贯轨迹或恢复行为，而状态持续性则支持延迟响应与可靠恢复。二阶动力学引入的情感惯性与滞后效应随动量增强而增大，揭示了稳定性与响应性之间的权衡关系。