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

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

CPM-MultiAgent, a novel framework grounded in the Component Process Model (CPM), addresses a key gap in persona-based dialogue agents by enabling dynamic emotional evolution. Unlike systems that treat emotions as static traits or focus on empathetic responses to users, CPM-MultiAgent models the agent's own evolving emotional state. It achieves this through affective trigger extraction, CPM-based collaborative appraisal, and emotion state updating. The framework has been experimentally validated in emotion-sensitive role-simulation settings across healthcare training, educational communication, and customer service scenarios. It consistently outperforms baseline methods and demonstrates robustness across various LLM backbones, including GPT-5.4, GPT-5.4-mini, GPT-5.4-nano, and Qwen3.6-35B-A3B.

Key takeaway

For NLP Engineers developing persona-based dialogue agents, traditional static emotion models are insufficient for realistic role-playing. You should consider adopting the CPM-MultiAgent framework to enable dynamic, trigger-driven emotional evolution in your agents. This approach, grounded in psychological appraisal theory, offers more coherent and consistent emotional states, crucial for sensitive applications like healthcare or education. Be aware of its increased inference latency due to multi-agent decomposition.

Key insights

CPM-MultiAgent enables dynamic, psychologically-grounded emotional evolution in persona-based dialogue agents by appraising external triggers.

Principles

• Emotion is a dynamic process, not a fixed attribute.
• External events trigger emotional evolution via appraisal.
• Decomposing appraisal into specialized agents improves coherence.

Method

The framework uses a Trigger Analyzer, four CPM Appraisal Agents (relevance, implication, coping potential, normative significance), an Integration Agent, and a Critic Agent for staged, trigger-driven emotional state updates.

In practice

• Apply CPM-MultiAgent in healthcare training simulations.
• Use for educational communication role-playing.
• Implement in customer service agent simulations.

Topics

• Persona-Based Dialogue
• Emotional AI
• Component Process Model
• Multi-Agent Systems
• Large Language Models
• Affective Computing

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.