From Speech to Profile: A Protocol-Driven LLM Agent for Psychological Profile Generation

Patient simulation is essential for developing and evaluating mental health dialogue systems. As most existing approaches rely on snapshot-style prompts with limited profile information, homogeneous behaviors and incoherent disease progression in multi-turn interactions have become key chellenges. In this work, we propose DEPROFILE, a data-grounded patient simulation framework that constructs unified, multi-source patient profiles by integrating demographic attributes, standardized clinical symptoms, counseling dialogues, and longitudinal life-event histories from real-world data. We further introduce a Chain-of-Change agent to transform noisy longitudinal records into structured, temporally grounded memory representations for simulation. Experiments across multiple large language model (LLM) backbones show that with more comprehensive profile constructed by DEPROFILE, the dialogue realism, behavioral diversity, and event richness have consistently improved and exceed state-of-the-art baselines, highlighting the importance of grounding patient simulation in verifiable longitudinal evidence.

Psychological counseling unfolds over many sessions, and what a client brings to each session depends on what has happened in their life since the last one. Existing synthetic counseling corpora are mostly single-session, and multi-session ones advance along a therapy plan without modeling the client's life between sessions. We present Psy-Chronicle, an event-driven framework that grounds multi-session counseling synthesis in a student's semester timeline: each simulated student receives a profile and a semester-long temporal stress event graph, and counseling sessions are generated along this timeline with memory carried across them. Applied to campus counseling, it yields CPCD, an open Chinese dataset of 100 students and 1{,}248 sessions spanning a full semester. The dialogues follow their timelines: dependencies between events carry across sessions, and references to earlier sessions rarely introduce content absent from the trajectory. Under a published evaluation protocol, in basic quality and exceeds all single-session corpora in working alliance. A model fine-tuned on CPCD connects each week's events to earlier ones more often than a model trained on the same data without the dependencies between events, while matching it when the client's situation does not change.

Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling, but adapting them to this safety-critical domain is hindered by limited real-world data due to privacy constraints. Synthetic datasets provide a promising alternative, but existing approaches often rely on unstructured or semi-structured text inputs and overlook structural dependencies between a client's cognitive, emotional, and behavioral states, leading to psychologically inconsistent and less realistic interactions. We introduce Graph2Counsel, a framework for generating synthetic counseling sessions grounded in Client Psychological Graphs (CPGs) that encode relationships among clients' thoughts, emotions, and behaviors. Graph2Counsel uses a structured prompting pipeline guided by counselor strategies and CPG, and explores prompting strategies including CoT and Multi-Agent Feedback. It produces 760 sessions from 76 CPGs across diverse client profiles. In expert evaluation, our dataset outperforms prior datasets on specificity, counselor competence, authenticity, conversational flow, and safety (Krippendorff's $α$ = 0.70). Fine-tuning an open-source model on this dataset improves performance on several metrics on CounselingBench and CounselBench, while matching baselines on others. We further introduce Therapy-Eval, a multi-turn evaluation framework, and demonstrate the effectiveness of our fine-tuned model in realistic therapeutic conversations. We make our code, data and fine-tuned model public.