PhAI Labs 发布 Discovery Foundation Models（DFM）技术报告，提出让 AI 系统识别有价值未知、提出问题、形成假设、设计实验并随证据修正理解的研究方向。报告包含参考架构、科学基础设施与湿实验室案例，并同步开放 DFM 科学家合作计划，邀请科研团队与实验平台共同探索开放问题。
This is really cool.
PhAI Labs just released DFM (Discovery Foundation Models), a research paradigm for AI systems meant to participate in scientific discovery.
Most AI-for-science systems start after the hard choices have already been made.
The question is defined. The task boundary exists. The model is asked to reason, search, or optimize inside it.
But DFM (Discovery Foundation Models) targets that layer.
The loop begins with a partially understood world, moves through a valuable unknown and a researchable question, then into explanation, intervention, and externally verified knowledge.
PhAI Labs is opening a Scientist Collaboration Program so researchers can bring real problems
So scientists bring the question, data, experimental conditions, and expert judgment. The AI side brings models, tools, analysis, and a way to keep revising the investigation as evidence arrives.
Scientific discovery often requires changing the question itself. Today, PhAI Labs releases the technical report for Discovery Foundation Models (DFM), a research direction for AI systems that can identify valuable unknowns, formulate questions, develop hypotheses, design experiments, and revise their understanding as evidence comes in. The goal is not just to complete one investigation. What a system learns should carry into the next—as reusable methods, accumulated research experience, and better decisions about what to explore. The report introduces a reference architecture, the science infrastructure we're developing, and a wet-lab case study. 🔬 Alongside the report, we're opening the DFM Scientist Collaboration Program. Have a scientific question you'd like to explore with AI? We're inviting scientists, research teams, and experimental platforms to collaborate on meaningful open problems with real opportunities for validation. 🙌 Program details & application: https://phai-labs.com/collaborate/ 📅 Over the next three days, we'll introduce three related research efforts: Sep 16 — ScienceBuddy · AI research companion Sep 17 — ScienceIDE · Scientific integrated development platform Sep 18 — JEPA-Anything · Cross-domain scientific world models A research direction we're pursuing with @Charles_Y_Wu @LingYang_PU @zhenfei_yin_yzf 📑 DFM technical report: https://arxiv.org/abs/2609.15973 🔗 DFM research roadmap: http://github.com/Gen-Verse/DFM-Plans 🏠 More about PhAI Labs: https://phai-labs.com/en/ #PhAILabs #DFM在 X 查看被引用的帖子
来源：Rohan Paul · x.com