STRIDE-ED Framework Advances AI Empathetic Dialogue Through Strategy-Grounded Reasoning

A new research framework called STRIDE-ED addresses fundamental limitations in empathetic dialogue systems by introducing structured, strategy-conditioned reasoning. The approach models empathetic dialogue as a complex multi-stage cognitive process requiring both emotional recognition and strategy-aware decision-making throughout response generation. Existing methods have been constrained by inadequate strategy frameworks, insufficient multi-stage reasoning alignment, and poor quality strategy-aware training data. To overcome these challenges, the framework incorporates a strategy-aware data refinement pipeline that uses LLM-based annotation, multi-model consistency-weighted evaluation, and dynamic sampling techniques. This pipeline constructs high-quality training data specifically aligned with empathetic strategies. The research was published on arXiv under identifier 2604.07100v2 with announcement type replace-cross. The work fundamentally reimagines how AI systems approach emotional understanding in conversational contexts.