TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological
  Counseling
Authors: name: He Hu, name: Yucheng Zhou, name: Chiyuan Ma, name: Qianning Wang, name: Zheng Zhang, name: Fei Ma, name: Laizhong Cui, name: Qi Tian

Large language models (LLMs) in psychological counseling have attracted
increasing attention. However, existing approaches often lack emotional
understanding, adaptive strategies, and the use of therapeutic methods across
multiple sessions with long-term memory, leaving them far from real clinical
practice. To address these critical gaps, we introduce TheraMind, a strategic
and adaptive agent for longitudinal psychological counseling. The cornerstone
of TheraMind is a novel dual-loop architecture that decouples the complex
counseling process into an Intra-Session Loop for tactical dialogue management
and a Cross-Session Loop for strategic therapeutic planning. The Intra-Session
Loop perceives the patient's emotional state to dynamically select response
strategies while leveraging cross-session memory to ensure continuity.
Crucially, the Cross-Session Loop empowers the agent with long-term
adaptability by evaluating the efficacy of the applied therapy after each
session and adjusting the method for subsequent interactions. We validate our
approach in a high-fidelity simulation environment grounded in real clinical
cases. Extensive evaluations show that TheraMind outperforms other methods,
especially on multi-session metrics like Coherence, Flexibility, and
Therapeutic Attunement, validating the effectiveness of its dual-loop design in
emulating strategic, adaptive, and longitudinal therapeutic behavior. The code
is publicly available at https://0mwwm0.github.io/TheraMind/.