Beyond Small Talk: Modeling Disclosure Trajectories in Human–AI Conversation

Conversational agents are increasingly used for social and emotional support, where disclosure unfolds over time. Yet evaluations typically examine isolated conversational states, and in analyses of naturally occurring conversations, disclosure history and current disclosure are confounded, making their effects difficult to isolate. We present a framework grounded in Social Penetration Theory that constructs predefined disclosure trajectories and evaluates how preceding disclosure history shapes model behavior under matched current disclosure. Across multiple models and relational behavior benchmarks, the same current disclosure elicits different responses depending on the preceding trajectory. In the trajectories tested, these differences track accumulated exposure to deeper disclosure rather than any single moment of vulnerability or the order of disclosures, and most persist for several turns after disclosure returns to shallow levels. Models thus carry conversational history forward. For several behaviors, they diverge most consistently from human supporters in the Emotional Support Conversation corpus (ESConv) not when users open up but when they step back, as disclosure recedes or changes direction. These results suggest that relational behavior is better understood as a property of an interaction history than of any single response. Our framework provides a systematic way to expose history-dependent relational behavior that evaluations of isolated conversational states may miss.