AI心理陪伴正在从聊天走向“记得你”
大模型在心理健康领域的角色正在经历三次升级：先是当“工具”做评估，再是当“聊天对象”做即时陪伴，现在正走向第三阶段——一个记得你、了解你、能长期跟进的“个性化伴侣”。前两阶段的问题很明显：它不记得你上周说过什么，每次对话都是“失忆”的。第三阶段的关键是把模型做成“有状态的智能体”，给它配上档案、记忆、推理和规划能力，让它能跨时间追踪你的状态变化。这篇综述把散落的研究串成这条进化线，并整理了相关数据集和评测资源。它不是你明天就能用上的产品，但它划出了这个领域正在往哪走：AI心理支持的下一个分水岭，不是更会聊天，而是“记得你”。
📄 原文摘要(英文)
The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, high cost, stigma, and privacy concerns, has created an urgent need for accessible and scalable support. Large Language Models (LLMs) have emerged as a transformative technology with strong potential to democratize mental health support through advanced natural language understanding and generation. However, the rapidly expanding, fragmented body of work in this area lacks a coherent evolutionary narrative, making it difficult to contextualize current progress and identify future directions. This survey addresses this gap by organizing and analyzing the literature around a central thesis: the role of LLMs in mental health is evolving through three distinct, increasingly sophisticated phases. We trace this trajectory from Phase I, in which LLMs act primarily as passive Information Tools and Pattern Recognizers for assessment; through Phase II, where they function as Empathetic Conversationalists for in-the-moment, stateless interactions; to the current frontier, Phase III, which seeks Longitudinal, Personalized Companions implemented as stateful cognitive agents. To support this framework, we systematically review core technologies, agent architectures (Profile, Memory, Reasoning, and Planning), and the critical infrastructure of datasets and benchmarks, highlighting how their evolution underpins this developmental path. Viewing the field through this developmental lens, we provide a comprehensive synthesis of existing work, an insightful narrative of its trajectory, and a clear roadmap for future innovation in responsible, effective, and human-centered AI for mental healthcare. A curated collection of the resources reviewed in this survey is available at our project repository: https://github.com/Emo-gml/Awesome-Mental-Health-LLMs.