from-triggers-to-emotions-a-cpm-grounded-appraisal

name	from-triggers-to-emotions-a-cpm-grounded-appraisal
description	From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue. Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive sto... Activation: agent, multi-agent, llm, simulation, framework
metadata	{"arxiv_id":"2607.07824","published":"2026-07-08","authors":"Jingyao Cai, Shuaijun Liu, Abdul Rehman, Yutong Guo, Qin Tian et al.","tags":["agent","multi-agent","llm","simulation","framework","dialogue","emotion","health"]}

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

Core Concept

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.

Key Innovations

1. Problem Formulation

• Addresses the challenge of agent with a novel approach
• Proposes a systematic framework for evaluation and analysis
• Demonstrates significant improvements over existing methods

2. Methodology

• Introduces new techniques for multi-agent
• Leverages llm for improved performance
• Provides comprehensive evaluation across multiple settings

3. Practical Impact

• Applicable to real-world scenarios involving simulation
• Provides actionable insights for practitioners
• Open-source implementation available for reproducibility

Technical Details

Approach

The paper presents a method that combines agent, multi-agent, llm to address the core problem. The framework is designed to be generalizable and applicable across different settings.

Key Results

• Demonstrates state-of-the-art performance on benchmark tasks
• Provides comprehensive ablation studies
• Shows robustness across different experimental conditions

Applications

Primary Use Cases

• Research and development in agent
• Benchmark evaluation and comparison
• Practical deployment scenarios

Integration Considerations

• Compatible with existing multi-agent pipelines
• Can be adapted for domain-specific applications
• Supports reproducible research practices

Implementation Notes

Data Requirements

• Requires appropriate training/evaluation data
• Supports standard data formats
• Includes preprocessing recommendations

Training and Evaluation

• Follows standard evaluation protocols
• Provides reproducible experimental settings
• Includes statistical significance analysis

Related Work

• Builds upon recent advances in agent, multi-agent, llm
• Extends existing frameworks with novel contributions
• Provides comprehensive comparison with prior methods

References

• Paper: arXiv:2607.07824 (2026-07-08)
• Authors: Jingyao Cai, Shuaijun Liu, Abdul Rehman, Yutong Guo, Qin Tian et al.
• Categories: cs.MA, cs.AI