PsyMem: Role-Asymmetric Information Flow for Longitudinal Counseling Dialogue Synthesis

Longitudinal counseling-style dialogue research requires data that capture substantive interaction within sessions and continuity across sessions, yet authentic linked records are difffcult to obtain and share. We introduce PsyMem, a synthesis framework combining persona-grounded client simulation, reffectionguided utterance generation, and role-asymmetric cross-session state updates. The client retains a ffxed private persona and an evolving concern state, while the counselor carries forward memory derived from preceding dialogues. PsyMem produces 150 linked four-session sequences comprising 600 sessions, with an average of 61 turns per session. We assess within-session quality using a 15- item WAI-derived observer rubric and introduce the Memory Continuity Analysis Framework (MCAF) to evaluate cross-session continuity in stored role summaries. Under GPT-4o evaluation, PsyMem scores 6.04 on WAIOS, versus 5.71 for the best evaluated synthetic baseline on this metric, and 4.96/5 on MCAF. Removing cross-session state lowers MCAF by about 3.9 points; among variants retaining such state (MCAF: 4.94–4.95), the role-asymmetric design attains the highest observed WAIOS (6.04 vs. 5.97–6.02). Across six open-source base models, models ffne-tuned on PsyMem-generated dialogues perform best in most MentalChat16K comparisons, while WAIOS results vary by model and evaluator.