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

TL;DR
TheraMind is an innovative AI agent designed for long-term, adaptive psychological counseling, utilizing a dual-loop architecture to improve emotional understanding, strategic planning, and session continuity in web-based mental health support.
Contribution
The paper introduces TheraMind, a novel dual-loop architecture that separates tactical dialogue management from strategic therapeutic planning for longitudinal counseling.
Findings
Outperforms existing methods on multi-session metrics
Demonstrates improved coherence and therapeutic attunement
Validates effectiveness in a high-fidelity simulation environment
Abstract
The shortage of mental health professionals has driven the web to become a primary avenue for accessible psychological support. While Large Language Models (LLMs) offer promise for scalable web-based counseling, existing approaches often lack emotional understanding, adaptive strategies, and long-term memory. These limitations pose risks to digital well-being, as disjointed interactions can fail to support vulnerable users effectively. To address these gaps, we introduce TheraMind, a strategic and adaptive agent designed for trustworthy online longitudinal 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
