AI 从解题到发现：一个能自己找问题、自己验证的框架
我们总说 AI 会解题，但真正的难题是没人把问题定义好。这篇提出一个框架，让 AI 不只解你给的题，而是自己去 561 个行业里挖出 423 个高价值问题，挑 20 个来真做。它给 AI 配了工具、约束和验证机制，像带了个实验室助理，做完还能独立检查结果靠不靠谱。在基因治疗载体设计上，它比已发表的最好成绩还高 7%；在药物重定位上，把 GPT-5.5 的预测分数拉高了 2.5 到 7.6 分。这不是你明天能用的工具，但它指向一个拐点：AI 正在从『你问它答』走向『它自己找值得答的问题』。
📄 原文摘要(英文)
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objectives, simulation, verification, and repeated correction. AI now faces a similar transition: frontier models can solve difficult tasks once the problem, tools, and success criteria are specified, yet consequential real-world challenges rarely arrive in an executable or verifiable form. We introduce Apodex Discovery, a framework for building and evaluating discoverative AI through the heavy-duty solver, a system comprising a foundation model, harness, tools, and control policies that pursues extended, stateful, verifiable investigations. It has three core components. First, a problem-scouting process surveyed 561 industries across 16 sectors, assembled 423 high-value real-world problems, and selected 20 for the initial release. Second, a common environment-task-episode abstraction provides data, tools, constraints, feedback, trajectory recording, and verification of intermediate artifacts and final submissions. Third, HDS6 evaluates Tools, Repair, Alternatives, Coherence, Evidence, and Scope independently of final-task success. In AAV capsid design, Apodex surpassed the published state of the art by 7% across viability, tropism, structure prediction, and generative design. In drug repurposing and reformulation, a task-specific biomedical environment improved the mean normalized prediction score of GPT-5.5 and GPT-5.6-sol by 2.5 and 7.6 points over the same closed-book backbone. Controlled ablations show that the fixed TRACES episode interface enables attribution of performance differences to specific solver components. Apodex Discovery moves AI evaluation beyond predefined benchmarks toward verifiable investigations aimed at genuine discovery.