@inproceedings{fu-etal-2026-sentipolis,
title = "Sentipolis: Emotion-Aware Agents for Social Simulations",
author = "Fu, Chiyuan and
Chen, Lyuhao and
Xiao, Yunze and
Xuan, Weihao and
Busso, Carlos and
Diab, Mona T.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.368/",
pages = "7465--7494",
ISBN = "979-8-89176-395-1",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Sentipolis: Emotion-Aware Agents for Social Simulations
%A Fu, Chiyuan
%A Chen, Lyuhao
%A Xiao, Yunze
%A Xuan, Weihao
%A Busso, Carlos
%A Diab, Mona T.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F fu-etal-2026-sentipolis
%X 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.
%U https://aclanthology.org/2026.findings-acl.368/
%P 7465-7494
Markdown (Informal)
[Sentipolis: Emotion-Aware Agents for Social Simulations](https://aclanthology.org/2026.findings-acl.368/) (Fu et al., Findings 2026)
ACL
- Chiyuan Fu, Lyuhao Chen, Yunze Xiao, Weihao Xuan, Carlos Busso, and Mona T. Diab. 2026. Sentipolis: Emotion-Aware Agents for Social Simulations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 7465–7494, San Diego, California, United States. Association for Computational Linguistics.