Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook

2026-05-19

AI Summary

This study addresses the lack of systematic understanding regarding affective interaction mechanisms and behavioral stability of large-scale AI agents on social platforms such as Moltbook. The work proposes the first emotion-aware modeling framework for multi-agent interactions, mapping textual exchanges to fine-grained emotional categories through a Persona-Stimulus-Reaction (PSR) paradigm that quantifies the consistency of agents’ emotional responses under similar contextual stimuli. By integrating emotion classification, natural language processing, and context alignment analysis, the research demonstrates that distinct AI agents exhibit unique affective profiles, and their behavioral stability is significantly modulated by interaction context. These findings establish a theoretical foundation for designing trustworthy multi-agent systems with emotionally coherent and contextually adaptive behaviors.

Abstract

Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, large-scale agentic AIs can post, comment, and engage in activities generated at scale by AI-driven text. Yet these agent behavioral characteristics remain insufficiently understood, particularly in complex, multi-agent interaction. In this study, we analyze the emotional dynamics of agent interactions within Moltbook. We construct an emotion-aware framework that maps textual interactions to a predefined set of fine-grained emotional categories, enabling the extraction of structured emotion profiles across agents and interaction contexts. To further evaluate behavioral reliability, we introduce an emotion-based domain called Persona-Stimulus-Reaction (PSR) that captures the alignment of emotional responses across similar contexts. Our analysis shows distinct emotional patterns and varying levels of behavioral stability across agents. Our analysis reveals that agents exhibit distinct emotional signatures with varying levels of behavioral stability influenced by interaction context.

Problem

• Emotional Dynamics
• Agent-to-Agent Interactions
• Behavioral Stability
• Generative AI
• Multi-agent Systems

Innovation

• emotion-aware framework
• multi-agent interactions
• Persona-Stimulus-Reaction
• behavioral stability
• emotional dynamics