MICA combines incremental per-turn distance rewards and Monte Carlo returns from a shared potential function over user support states to create a mixed advantage signal that enables stable multi-turn RL optimization for emotional support dialogues.
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The authors propose a three-layer trust framework for AI mental health systems and review current evaluation practices to highlight gaps between technical metrics and clinical requirements.
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MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue
MICA combines incremental per-turn distance rewards and Monte Carlo returns from a shared potential function over user support states to create a mixed advantage signal that enables stable multi-turn RL optimization for emotional support dialogues.
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Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders
The authors propose a three-layer trust framework for AI mental health systems and review current evaluation practices to highlight gaps between technical metrics and clinical requirements.