How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation

• URL: http://arxiv.org/abs/2607.25140v1
• Date: Mon, 27 Jul 2026 23:22:17 GMT
• Title: How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation
• Abstract summary: We study how affect propagates among agents that perceive and appraise one another in a multi-agent crowd simulation.<n>The architecture contains no hand-authored mechanism for directly transferring affective state between agents.<n>We show that the system produces emotional contagion dynamics with spatial, temporal, and personality-dependent structure in sparse, small crowds.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and appraise one another. Each agent perceives its neighbors through visual, auditory, and tactile channels, then appraises these perceptions in light of its prompted personality profile, memory, current affective state, and situational context. Appraisal is carried out by an LLM, which updates the agent's internal affective state and selects its outward expression. The architecture contains no hand-authored mechanism for directly transferring affective state between agents; instead, inter-agent influence arises through the perception-appraisal-expression loop. The agent representation draws on the Big Five personality model and Russell's circumplex model of affect. To limit latency, low-level steering and navigation are handled by a conventional crowd simulator operating independently of the LLM-based cognitive layer. We evaluate the architecture across five scenario environments spanning alarming, joyful, and neutral situations in different spatial layouts. The results show that the system produces emotional contagion dynamics with spatial, temporal, and personality-dependent structure in sparse, small crowds. Alarm spreads from seeded agents as a traveling front, the mean alarmed fraction settles at a nonzero plateau, and the distribution of prompted personality profiles determines whether an ambiguous alarm ignites panic and whether a provocation is interpreted as anger or fear. We further evaluate the appraisal step through controlled experiments across prompt variants, sampling temperatures, and four model backends, showing that the dynamics are backend-dependent.

Related papers

• Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events [34.88005801095875]
  Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing.<n>We study event-induced personality change after 11 major life events using the Big Five traits as a psychometric anchor.<n>Our results suggest that current PC-Agents simulate the mean of human personality dynamics, but not its shape.
  arXiv  Detail & Related papers  (2026-08-06T18:25:58Z)
• Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments [68.02342920827135]
  We propose NoisyAgent, an agentic training framework that explicitly incorporates environmental imperfections into the agent learning process.<n>We identify two major sources of interaction noise in real-world scenarios: user noise, which captures ambiguity and variability in user interaction, and tool noise, which reflects failures and anomalies in tool execution.<n>Extensive experiments demonstrate that our approach consistently improves agent robustness under noisy and dynamic environments.
  arXiv  Detail & Related papers  (2026-05-26T16:02:00Z)
• ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop [55.468404995694975]
  We develop a benchmark for embodied spatial intelligence spanning 10 task categories and 29 subcategories built on OmniGibson.<n>We conduct experiments on state-of-the-art MLLMs and find that active exploration substantially outperforms passive counterparts.<n>Unlike humans who seek falsifying viewpoints and revise beliefs under contradiction, models commit prematurely with high confidence regardless of evidence quality.
  arXiv  Detail & Related papers  (2026-05-18T17:59:02Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics [0.0]
  Large language model (LLM) agents exhibit abrupt shifts in tone and persona during extended interaction.<n>The role of explicit affective dynamics in shaping long-horizon agent behavior remains underexplored.<n>We introduce an agent-level affective subsystem that maintains a continuous Valence-Arousal-Dominance (VAD) state external to the language model.
  arXiv  Detail & Related papers  (2026-01-22T16:34:05Z)
• DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios [57.327907850766785]
  characterization of deception across realistic real-world scenarios remains underexplored.<n>We establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different domains.<n>On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement.<n>We incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics.
  arXiv  Detail & Related papers  (2025-10-17T10:14:26Z)
• AgentZero++: Modeling Fear-Based Behavior [4.783433971864009]
  We present AgentZero++, an agent-based model that integrates cognitive, emotional, and social mechanisms to simulate collective violence.<n>Building on Epstein's Agent_Zero framework, we extend the original model with eight behavioral enhancements.<n>These additions allow agents to adapt based on internal states, previous experiences, and social feedback.
  arXiv  Detail & Related papers  (2025-10-05T22:33:56Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• Inference of Affordances and Active Motor Control in Simulated Agents [0.5161531917413706]
  We introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture. We show that our architecture develops latent states that can be interpreted as affordance maps. In combination with active inference, we show that flexible, goal-directed behavior can be invoked.
  arXiv  Detail & Related papers  (2022-02-23T14:13:04Z)
• Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making [6.704848594973921]
  We study Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. As a main contribution, we establish Pavlovian signalling as a natural bridge between fixed signalling paradigms and fully adaptive communication learning between two agents.
  arXiv  Detail & Related papers  (2022-01-11T00:14:04Z)
• Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
  We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
  arXiv  Detail & Related papers  (2021-12-07T18:50:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.