AI Mental Health Frontier AI Mental Health Frontier — The Safeguard Gap Is Now a Deployment Problem Recent clinical and policy work shows that mental-health AI is already reaching people in distress, while evaluation, escalation, and accountability remain uneven.
AI Investment Frontier AI Investment Frontier — Investment AI Needs a Decision Trace A new benchmark argues that investment agents should be judged by point-in-time evidence, investor profile, reasoning, decisions, and delayed outcomes—not returns alone.
AI Signals and Reality Checks Stripe 收购 OpenRouter：模型路由正在变成财务控制层 Stripe 收购 OpenRouter 的意义不只是拿下一个 AI 网关，而是把模型选择、Token 支出和故障切换变成可管理的财务控制面。
AI Signals and Reality Checks Stripe's OpenRouter Deal Makes Model Routing a Financial Control Layer Stripe's OpenRouter acquisition is less about owning another AI gateway than about turning model choice, token spend, and fallback policy into a finance-grade control surface.
AI Mental Health Frontier AI 心理健康前沿 — 青少年应用不能只多一个生成式功能 一篇最新快速综述梳理生成式 AI 在青少年心理健康应用中的使用，也把真正的门槛指向证据、边界与升级机制。
AI Mental Health Frontier AI Mental Health Frontier — Youth Apps Need More Than a Generative Feature A new rapid review of generative AI in youth mental-health apps points builders toward evidence, boundaries, and escalation—not novelty.
AI Investment Frontier AI 投资前沿：风险偏好也应成为组合模型的输入 一篇新论文把机器学习的收益预期放进结构化组合模型，提示投资 AI 不应只预测资产，也要理解决策者的行为与约束。
AI Investment Frontier AI Investment Frontier — Risk Preferences Are a Portfolio-Model Input A new paper combines machine-learned return expectations with a structural portfolio model. The lesson for investment AI is to model behavior and constraints, not just forecasts.
AI Signals and Reality Checks Open Weights Are Cheap; Reliable Local AI Is Not The latest open-weight releases lower the cost of trying local AI. They do not lower the cost of proving that a model is safe, stable, and worth operating.
AI Mental Health Frontier AI 心理健康前沿——评估必须跟着干预一起设计 一项最新 ACM 研究把评估设计放回 AI 心理健康干预的核心：不能只看回答质量，还要追踪安全、人工支持与长期结果。
AI Mental Health Frontier AI Mental Health Frontier — Evaluation Must Follow the Intervention New CHI research puts evaluation design at the center of AI mental-health intervention work. The practical lesson: measure change, safety, and human support together.
AI Investment Frontier AI Investment Frontier — The Portfolio Solver Needs a Contract A new comparison of photonic, classical, and reinforcement-learning optimizers points to a practical rule: make the solver’s contract explicit before comparing performance.
AI Signals and Reality Checks AI's Next Infrastructure Bottleneck Is the Power Schedule The AI infrastructure race is moving from buying accelerators to securing the hours, interconnection capacity, and operational flexibility that make them useful.
AI Investment Frontier AI Investment Frontier — Measure Reliance, Not Just Forecasts A new study on AI-supported investment decisions points to a practical design rule: measure how systems change investor judgment, not just forecast accuracy.
AI Mental Health Frontier AI Mental Health Frontier — Engagement Is Not Improvement A new JMIR study links conversational-agent engagement with anxiety and depression measures. The useful lesson is to separate usage signals from clinical outcomes.
AI Mental Health Frontier AI 心理健康前沿 — 使用得更多，不等于变得更好 一项最新 JMIR 研究分析了人们如何使用心理健康对话代理，以及使用模式与焦虑、抑郁指标的关联。真正的课题，是把使用信号与临床改善分开。
AI Signals and Reality Checks Multi-Agent Systems Need a Conflict Budget Recent agent experiments point to a neglected product risk: when several agents share a goal, coordination failure can become the system's dominant behavior.
AI Mental Health Frontier AI 心理健康前沿 — 安全必须按对话过程来测试 Nature Medicine 的新研究表明，心理健康聊天机器人的安全不能只看平均分，而要观察多轮对话如何放大脆弱性。
AI Mental Health Frontier AI Mental Health Frontier — Safety Must Be Tested as a Conversation A new Nature Medicine framework shows why mental-health AI safety needs multi-turn, vulnerability-specific audits—not one average benchmark score.
