Ekova: A Personality-Support Agent for Self-Discovery Dialogue
This paper proposes a new research direction for dialogue systems, targeting self-discovery and cognitive clarity, which could be valuable for mental well-being applications, but its impact is currently limited to a specific dataset and domain.
The authors introduce a new paradigm called Personality Support (PS) for dialogue systems, distinct from emotional support, and build a dataset and system for it. Their multi-persona system, trained with a tailored framework, achieves an average relative gain of 16.3% over the strongest baseline.
Emotional Support (ES) systems have long optimized a single objective: alleviating the user's emotional distress in the moment. We argue that a complementary need, helping users see themselves more clearly, defines a distinct paradigm we call Personality Support (PS). PS is not counseling or clinical intervention: it targets cognitive clarity and self-articulation, not symptom relief or diagnosis. We instantiate this paradigm in three layers. First, we present DSD, a Chinese self-discovery PS Dataset of 8,590 samples collected through real longitudinal interaction across five minimal units, Coach, Warm, Tsukkomi, Real, and Gonzo. Second, we build DeepSupport, a multi-persona PS system trained with OrthoTune, a PS-tailored framework with style-specific adapters and a style-consistency regularizer. Third, we unify the five DeepSupport personas into Ekova, a persistent personality-support agent with a unified cross-session memory layer, supporting both adaptive routing and user-customized persona selection. Experiments show that OrthoTune-trained models outperform all baselines with an average relative gain of 16.3% across all metrics over the strongest prompt-based baseline. Code is available at https://github.com/Yukyin/Ekova.