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