Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions
For AI companion developers and regulators, this work exposes the inadequacy of current short-session safety evaluations and provides a scalable method to assess long-term risks to cognition-developing users.
Existing short-horizon safety tests for AI companions underestimate cognitive-developmental risks that emerge over prolonged interaction. TSJ, a longitudinal framework, reveals that stable risk estimates require at least 140 turns, identifying early childhood and emerging adulthood as most vulnerable, with cognitive trust and emotional dependency as weakest domains.
AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks that may accumulate over time. Existing safety evaluations largely rely on single-turn or short-session tests, which cannot capture risks that emerge only through prolonged interaction. To address this gap, we propose TSJ (Theater-Stage-Judge), a longitudinal framework combining persona-driven user simulation, dynamic psychological-state updating and retrospective evaluation. We evaluate six mainstream models across four developmental stages, twenty-four risk dimensions and three psychological-vulnerability personas, covering 12,960 simulated person-day interactions. TSJ shows that short-horizon testing systematically underestimates developmental risks, for which TSJ yields a stable risk estimate only after 140 turns within prolonged simulated relationships. Applying TSJ further identifies early childhood and emerging adulthood as the most vulnerable stages, with cognitive trust and emotional dependency as the weakest domains. TSJ provides a scalable methodology for longitudinal cognitive developmental risk evaluation in AI companion systems.