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/.

翻译：大型语言模型在心理咨询领域的应用日益受到关注。然而，现有方法往往缺乏情感理解、适应性策略以及跨多轮次、具备长期记忆的治疗方法运用，使其与真实临床实践相距甚远。为填补这些关键空白，我们提出了TheraMind，一种用于纵向心理咨询的战略性与适应性智能体。TheraMind的核心是一种新颖的双循环架构，它将复杂的咨询过程解耦为用于战术性对话管理的会话内循环和用于战略性治疗规划的跨会话循环。会话内循环通过感知患者的情绪状态动态选择回应策略，同时利用跨会话记忆确保连续性。至关重要的是，跨会话循环通过评估每次会话后所应用疗法的有效性，并调整后续交互的治疗方法，赋予智能体长期适应能力。我们在基于真实临床案例构建的高保真模拟环境中验证了该方法。大量评估表明，TheraMind在多项指标上优于其他方法，尤其是在连贯性、灵活性和治疗协调性等多轮次指标上，验证了其双循环设计在模拟战略性、适应性和纵向治疗行为方面的有效性。代码已公开于https://0mwwm0.github.io/TheraMind/。