让大模型代理具备持续情绪状态，提升社交模拟真实感。
Sentipolis: Emotion-Aware Agents for Social Simulations
- 用PAD情绪空间与双速动态建模长期情绪变化
- 多轮交互中情绪连贯性显著提升，高容量模型效果更优
- 适合研究情感驱动的社会关系演化，如联盟形成
大型语言模型代理被广泛用于社会模拟，但情绪常被视为短暂线索，导致情绪遗忘和长时程连续性不足。我们提出Sentipolis框架，通过融合连续的愉悦-唤醒-支配（PAD）表示、双速情绪动力学及情绪-记忆耦合机制，实现有情绪状态的代理。在多个基础模型和评估者上进行数千次交互测试表明，该框架显著提升了行为的情感根基性，增强沟通与情绪连续性。增益具有模型依赖性：高容量模型的可信度上升，而小模型可能下降；情绪感知能力轻微降低对社会规范的遵守，反映人类在情绪驱动与规则服从间的张力。网络级诊断显示关系结构具有互惠性、中等聚类性和时间稳定性，支持对联盟形成和关系渐变等累积性社会动态的研究。
原文摘要 · Abstract (English)
LLM agents are increasingly used for social simulation, yet emotion is often treated as a transient cue, causing emotional amnesia and weak long-horizon continuity. We present Sentipolis, a framework for emotionally stateful agents that integrates continuous Pleasure-Arousal-Dominance (PAD) representation, dual-speed emotion dynamics, and emotion--memory coupling. Across thousands of interactions over multiple base models and evaluators, Sentipolis improves emotionally grounded behavior, boosting communication, and emotional continuity. Gains are model-dependent: believability increases for higher-capacity models but can drop for smaller ones, and emotion-awareness can mildly reduce adherence to social norms, reflecting a human-like tension between emotion-driven behavior and rule compliance in social simulation. Network-level diagnostics show reciprocal, moderately clustered, and temporally stable relationship structures, supporting the study of cumulative social dynamics such as alliance formation and gradual relationship change.
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