From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue

2026-07-08

AI Summary

This work addresses the limitation of existing character dialogue systems, which typically treat emotion as a static trait and thus fail to model dynamic emotional evolution triggered by external events. To overcome this, the study introduces the Component Process Model (CPM) from psychology into the field for the first time and proposes CPM-MultiAgent, a multi-agent framework that enables continuous emotional evolution across multi-turn dialogues. The framework integrates emotion trigger recognition, CPM-based collaborative appraisal, and a latent-variable mechanism for updating emotional states. Experimental results demonstrate that the proposed approach significantly enhances both emotional consistency and dynamic expressiveness, as evidenced by automatic metrics, ablation studies, human evaluations, and case analyses, making it particularly suitable for emotion-sensitive applications such as healthcare and education.

Abstract

Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling. However, two related lines of work leave a key gap. Persona-based dialogue systems often encode emotions as static traits or surface-level stylistic cues, and affective dialogue research has largely focused on empathetic response generation toward users rather than modeling the agent persona's own evolving emotional state. As a result, trigger-driven emotional evolution within a character remains underexplored. To address this limitation, we draw inspiration from the Component Process Model (CPM), a psychological theory that views emotion as a dynamic process shaped by the appraisal of external events. We propose CPM-MultiAgent, a CPM-grounded emotion evolution multi-agent framework for supporting emotional changes in persona-based dialogue. Instead of treating a character's emotion as a fixed attribute, CPM-MultiAgent represents it as a latent state that is continuously reshaped by dialogue triggers. Through affective trigger extraction, CPM-based collaborative appraisal, and emotion state updating, the framework enables more emotionally consistent role simulation in multi-turn interactions.Experiments with baseline comparisons, ablation studies, human evaluation, and case analyses demonstrate that CPM-MultiAgent effectively models dynamic emotional evolution in emotionally sensitive role-simulation settings.

Problem

• persona-based dialogue
• emotional evolution
• affective computing
• emotion modeling
• dialogue systems

Innovation

• Component Process Model
• dynamic emotional evolution
• persona-based dialogue
• multi-agent framework
• affective trigger