arXiv:2605.27908v1 Announce Type: cross Abstract: Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited interpretability and little support for systematic skill improvement. We propose ESC-Skills, a skill-centric framework that discovers and self-evolves executable emotional support skills. We first model localized support interactions as Intervention Units (IUs), which capture state--action--outcome dynamics between seeker states, support interventions, and post-response emotional changes. Based on
The proliferation of AI in conversational systems necessitates more sophisticated and interpretable methods for handling complex human interactions, especially in sensitive areas like emotional support.
This research introduces a novel, skill-centric approach that moves beyond black-box AI responses, offering a path to more effective, systematic, and ethical emotional support systems.
The shift from end-to-end response generation to a skill-based, self-evolving framework allows for greater transparency, targeted improvements, and potentially more personalized and robust emotional support AI.
- · AI developers in mental health
- · Users of emotional support AI
- · AI ethics researchers
- · Healthcare providers
- · Developers of uninterpretable end-to-end conversational AI
- · Traditional, one-size-fits-all mental health app providers
Improved efficacy and interpretability of AI systems designed for emotional support.
Increased user trust and adoption of AI-powered mental health interventions.
Potential for AI to scale advanced therapeutic techniques by systematically applying and evolving 'skills' previously exclusive to human practitioners.
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Read at arXiv cs.AI