AI 的下一个前沿：自己提出问题，而不是等你下指令
我们习惯的 AI 是解题机器：你给问题，它给答案。这篇论文提出下一步——让 AI 参与「提出问题」本身。研究者把这种能力叫「发现智能」，并搭了个框架：AI 不再只在一个固定问题上推理，而是能自己发现新问题、构造新表述、提出假设、设计实验、根据结果修正，再循环。他们真做了一个叫 GALILEO 的系统，把 AI 的干实验室推理和机器人的湿实验操作闭环起来，AI 提出假设、机器人动手做实验、结果反馈回来修正假设。这不是你明天能用上的东西，但它划了一条线：AI 的进化方向，从「回答得更好」转向「发现得更多」。
📄 原文摘要(英文)
Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the next frontier is a further transition: from solving and acting within problems specified by humans to participating in the process by which new problems, representations, explanations, and knowledge are created. We refer to this capability as Discovery Intelligence. We formulate Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery. A DFM operates over a revisable research state and supports seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual discovery improvement. We instantiate this framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, external grounding, and cross-task Discovery Skill evolution. We further ground the framework with GALILEO, a real therapeutic-discovery system in which Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, external biological evidence, and iterative hypothesis and design revision form a closed physical discovery loop. We then formulate a unified approach to capability formation and process-centered evaluation, enabling discovery behavior to be trained, improved, and measured beyond final-answer performance. Together, these components establish discovery as a learnable, executable, and evaluable capability of foundation-model systems. We view this shift as a broader progression in intelligence scaling: from learning over existing knowledge, to learning from action outcomes, and ultimately to participating in the construction, testing, and revision of the structures through which new knowledge is discovered. Code: https://github.com/Gen-Verse/DFM-Plans