Authors - Armie E. Pakzad, Nathanael Adrian T. Cua, Louie T. Que, Alvin Josh T. Valenciano, Jana Johannes Valenzuela, Abbasali Pakzad Abstract - Emotional Support Conversation (ESC) seeks to lessen users’ emotional dis tress through sympathetic communication. Current approaches concentrate on comprehending present emotional states and combining support techniques to generate responses. But they fail to take into account an important factor: emotional trajectories (how users’ feelings change over time). Two people expe riencing the same feeling may need essentially different answers depending on whether they are in a therapeutic window (gradually improving), a depressed spiral (continuous hopelessness), or a crisis escalation (rapidly worsening). We propose TRAGEDY (TRAjectory-Guided Emotional Dialogue System), a sys tem that explicitly models clinical patterns and emotional trajectories in order to direct response creation. We present: (1) a trajectory encoder that records the temporal dynamics of emotion and intensity sequences; (2) a clinical pat tern detector that recognizes five psychologically grounded patterns (normal progression, therapeutic window, resistance pattern, depressed spiral, and crisis escalation); and (3) pattern-aware generation that bases responses on trajectories found. Experiments on the ESConv benchmark show that TRAGEDY provides interpretable trajectory insights while outperforming robust baselines, across standard generation metrics. Our approach opens new avenues for trajectory aware conversational AI and emphasizes the significance of temporal dynamics in emotional support.
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