用动态情绪状态让大模型对话更连贯稳定
Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics
- 引入外部情绪状态系统，用连续情绪值控制对话行为
- 二阶动态使情绪变化更稳定，但响应变慢
- 适合需要长期一致性的人机对话场景
大型语言模型代理在长时间交互中常出现语气和人设突变，缺乏显式的时序状态结构。现有研究多关注单轮情感或静态分类，而对显式情绪动力学如何影响长期行为仍研究不足。本文探究在外部施加情绪状态动力学是否能提升多轮对话的时序连贯性与可控恢复能力。提出一种代理级情绪子系统，维护外部连续的唤醒-效价-支配（VAD）状态，由一阶和二阶更新规则驱动。瞬时情绪信号通过固定、无记忆估计器提取，并通过指数平滑或动量机制整合。情绪状态注入生成过程，不修改模型参数。在固定25轮对话协议下对比无状态、一阶和二阶情绪动力学。无状态代理无法维持连贯轨迹或恢复；状态持久化支持延迟响应与可靠恢复；二阶动态引入情绪惯性和滞后效应，随动量增加而增强，揭示了稳定性与响应速度间的权衡。
原文摘要 · Abstract (English)
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.
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