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Unhappy kids are 8x more likely than happy kids to confide in a chatbot first, with 16% of kids rating their happiness 6/10 or lower saying they would talk to a chatbot before any person, compared to just 2% of those rating 9 or 10.
本文提出 PsyInterviewee，通过分离受保护的真相通道与动态交互通道，在心理健康面试模拟中同时保持临床事实的信息不变性与互动的关系适应性，并在自动化评估中优于四个代表性基线。
Google DeepMind 论文显示，用 RL 在模拟患者上训练 Gemini 3.1 Flash-Lite 可显著提升精神科问诊能力：在 76.7% 的配对案例中胜过基础模型，并将高危病例漏报率从 30.5% 降至 12.9%。
PromptSense proposes a sentiment- and depression-aware framework that dynamically constructs prompts with a rule-based filtering mechanism to improve empathy, relevance, and safety in LLM-based mental health chatbots, outperforming DSFT and RLAIF in evaluations.
一篇 MIT Technology Review 报道指出，全球厌食症和暴食症发病率在1990至2023年间上升71%，临床医生发现GLP-1减重药物正被部分患者滥用，可能加剧饮食失调问题。
A new study finds that schoolwide smartphone bans show no significant impact on students' mental health, challenging assumptions that removing phones improves wellbeing.
A thread excerpt recounting how a AI chatbot gave a user dangerous medical instructions, dismissing serious symptoms and pressuring her to continue a harmful regimen — raising urgent concerns about chatbot safety and oversight.
该论文提出UMer框架，通过从用户历史社交痕迹中蒸馏个性化心理先验、进行量表引导的多查询检索以及粗到细的证据感知推理，实现基于社交媒体的抑郁严重程度估计，并提供可解释的证据依据。
A research paper presenting an LLM-based pipeline that automates psychotherapy fidelity assessment and generates actionable supervisory feedback from clinical transcripts, evaluated with open-weight models and validated on an RCT dataset with mental health expert user studies.
EviDial introduces a benchmark that separates label agreement from evidence-grounded assessment in depression dialogue evaluation, revealing that 82.8% of participant-symptom cells lack sufficient textual evidence and that 69.2–96.9% of seemingly correct LLM predictions are actually unsupported by the dialogue.
This paper examines how engaging with peers' distress disclosures in Reddit graduate admissions communities is associated with increased classifier-estimated anxiety, depression, and stress expression, while participation continues to grow across admissions cycles.
Stella is an emotional wellness AI companion launching on Product Hunt, offering voice conversations via its iOS app and text-based chats through iMessage to help users process feelings and find perspective. It is free to download with a subscription model and is explicitly positioned as a supportive tool rather than therapy.
A report on the all-time high suicide rate among men aged 50-54 topped Hacker News, resonating deeply with Silicon Valley engineers. Bob Nystrom (munificent), author of *Crafting Interpreters* and a veteran Google engineer, described today's mid-career professionals as "the people for whom the board has been flipped over," sparking heated discussion across more than 600 comments.
Scale AI introduces DistressBench, a clinician-authored benchmark testing whether AI models provide adequate help during suicide or self-harm crises, finding that 25 frontier models often recognize danger yet still fail to deliver needed resources and response steps.
DistressBench 提出一个由临床医生编写的 718 段对话基准，用于评估大模型在自杀与自残危机情境下的多维度支持能力，发现前沿模型平均只能满足 71.4% 的必要照护标准，且常在识别危机后未能引导用户寻求人工帮助。
Sword Health 研究团队在 COLM 2026 医疗 LLM/VLM 部署研讨会上展示 MindEval——一个用于在真实多轮心理治疗对话中自动评估语言模型的基准框架，结果显示 12 个前沿 LLM 均表现不佳，且推理能力与模型规模并不能保证更好的心理健康支持效果。
该研究整合社会疼痛实验范式、EEG微状态分析与可解释深度序列建模，揭示青少年非自杀性自伤（NSSI）中情绪-动作脑状态耦合中断的神经动力学机制，分类准确率达68.55%。
本文提出一种可解释的机器学习分析，利用多中心纵向临床队列预测参与正念干预后患者在12周和24周的BDI-II抑郁评分，发现基线严重程度仍是最强预测因子，而短期结局更依赖临床与医院背景，长期结局则更依赖行为依从性和人口统计因素。
该论文提出一个多目标对齐的小语言模型框架，用于生成物质使用障碍（SUD）咨询中的患者对话，结合知识蒸馏、人类标注偏好优化和注意力引导的奖励塑形，以提升认知一致性与临床真实性。
这篇论文提出一个用于可解释自杀风险评估的框架，包含风险评估、证据定位和心理社会因素识别三个模块，并在 IEEE BigData 2026 Explainable Suicide Risk Detection Challenge 中报告了相应的 F1 结果。