Simulated patients offer a scalable way to train psychotherapy micro-skills such as empathic responding and exploratory probing, but current systems either follow fixed scripts or rely on LLMs that drift unpredictably over long sessions. We present the Adaptive Virtual Patient (AVP), which adapts its disclosure behavior in response to trainee skill, from guarded, through moderate openness, to full disclosure. The AVP is informed by a structural equation model fit to nearly 2000 hours of real-world psychotherapy transcripts, which quantifies how therapist empathy and exploration shift a patient’s openness over time. An LLM generates the AVP’s utterances conditioned on a disclosure level that the dynamics module updates each turn. In an evaluation with 20 clinicians and trainees over 80 sessions (1,033 turns), the AVP’s disclosure rises in response to therapist empathy and exploration, while a prompt-only baseline stays flat; ablations confirm that the empirically motivated parameterization outperforms alternatives, with exploration carrying most of the adaptive signal.

Comprehensive pathway datasets are essential resources for advancing biological research, yet constructing these datasets is labor intensive. Recognizing the labor-intensive nature of constructing these critical resources, we present BioGraphia, a web-based annotation platform designed to facilitate collaborative pathway graph annotation. BioGraphia supports multi-user collaboration with real-time monitoring, curation, and interactive pathway graph visualization. It enables users to directly annotate the nodes and relations on the candidate graph, guided by detailed instructions. The platform is further enhanced with a large language model that automatically generates explainable and span-aligned pre-annotation to accelerate the annotation process. Its modular design allows flexible integration of external knowledge bases, and customization of the definition of annotation schema and, to support adaptation to other graph-based annotation tasks. Code is available at https://github.com/LeiLiLab/BioGraphia