Sentipolis: Emotion-Aware Agents for Social Simulations

Fuente: arXiv
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Main Authors: Fu, Chiyuan, Chen, Lyuhao, Xiao, Yunze, Xuan, Weihao, Busso, Carlos, Diab, Mona
Format: Preprint
Published: 2026
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author Fu, Chiyuan
Chen, Lyuhao
Xiao, Yunze
Xuan, Weihao
Busso, Carlos
Diab, Mona
author_facet Fu, Chiyuan
Chen, Lyuhao
Xiao, Yunze
Xuan, Weihao
Busso, Carlos
Diab, Mona
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_18027
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Sentipolis: Emotion-Aware Agents for Social Simulations
Fu, Chiyuan
Chen, Lyuhao
Xiao, Yunze
Xuan, Weihao
Busso, Carlos
Diab, Mona
Artificial Intelligence
Computation and Language
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.
title Sentipolis: Emotion-Aware Agents for Social Simulations
topic Artificial Intelligence
Computation and Language
url https://arxiv.org/abs/2601.18027