From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue
arXiv:2607.07824v1 Announce Type: cross 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, t
The increasing sophistication of Large Language Models (LLMs) requires more nuanced emotional intelligence for effective human-AI interaction, pushing research into dynamic emotional evolution.
This development moves AI agents beyond static emotional representations, enabling more realistic and adaptable interactions across critical applications like healthcare and education.
AI agents can now model and evolve their own emotional states dynamically in persona-based dialogues, leading to more human-like and contextually aware engagement.
- · AI developers
- · Healthcare sector (AI assistants)
- · Education sector (AI tutors)
- · Interactive entertainment
- · Providers of static persona-based dialogue systems
- · Basic sentiment analysis tools
Persona-based AI agents will exhibit more believable and consistent emotional responses.
This improved emotional depth could enhance user trust and engagement in AI interactions, particularly in sensitive domains.
Ethical considerations around manipulating user emotions and the potential for emotionally complex AI deception may become more prominent debates.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI