Public articles linked to the same research event.
arXiv The work introduces Deep Persona, a psychologically grounded three-layered architecture that organizes personas into observable expression, latent beliefs, and core motivational drives, governed by scripted determinism and bounded agency, together with a reference-free evaluation framework (ADOS-inspired metrics for pragmatic fluency, joint attention, affective congruence, and emotional expression diversity, plus a Mahalanobis-distance Dialogue Naturalness Score, DNS); on human-human and human-LLM dialogue datasets such as DailyDialog and CounselChat, LLMs show high pragmatic fluency (0.88-0.
The work introduces Deep Persona, a psychologically grounded three-layered architecture that organizes personas into observable expression, latent beliefs, and core motivational drives, governed by scripted determinism and bounded agency, together with a reference-free evaluation framework (ADOS-inspired metrics for pragmatic fluency, joint attention, affective congruence, and emotional expression diversity, plus a Mahalanobis-distance Dialogue Naturalness Score, DNS); on human-human and human-LLM dialogue datasets such as DailyDialog and CounselChat, LLMs show high pragmatic fluency (0.88-0.
The work introduces Deep Persona, a psychologically grounded three-layered architecture that organizes personas into observable expression, latent beliefs, and core motivational drives, governed by scripted determinism and bounded agency, together with a reference-free evaluation framework (ADOS-inspired metrics for pragmatic fluency, joint attention, affective congruence, and emotional expression diversity, plus a Mahalanobis-distance Dialogue Naturalness Score, DNS); on human-human and human-LLM dialogue datasets such as DailyDialog and CounselChat, LLMs show high pragmatic fluency (0.88-0.
The work introduces Deep Persona, a psychologically grounded three-layered architecture that organizes personas into observable expression, latent beliefs, and core motivational drives, governed by scripted determinism and bounded agency, together with a reference-free evaluation framework (ADOS-inspired metrics for pragmatic fluency, joint attention, affective congruence, and emotional expression diversity, plus a Mahalanobis-distance Dialogue Naturalness Score, DNS); on human-human and human-LLM dialogue datasets such as DailyDialog and CounselChat, LLMs show high pragmatic fluency (0.88-0.