Discovery Foundation Models: Toward Open-Ended Discovery Intelligence
Summary
Discovery Foundation Models are proposed as general-purpose systems for enabling open-ended scientific discovery through iterative problem formulation, hypothesis testing, and evidence-based revision across dry and wet lab settings. The paper introduces a framework with capabilities like problem discovery and continual improvement, instantiated with systems like Zetema and GALILEO.
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Source: https://huggingface.co/papers/2609.15973
Abstract
Discovery Foundation Models enable open-ended scientific discovery through iterative problem formulation, hypothesis testing, and evidence-based revision across dry and wet lab settings.
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 asDiscovery Intelligence. We formulateDiscovery Foundation Models(DFMs) as general-purpose model systems for open-ended discovery. A DFM operates over a revisableresearch stateand supports seven coupled capabilities spanningproblem discovery, formulation,representation construction,hypothesis formation, intervention,evidence-grounded revision, andcontinual discovery improvement. We instantiate this framework withZetema, which couples explicit research-state dynamics, verification and experimental gating, external grounding, and cross-taskDiscovery Skillevolution. We further ground the framework withGALILEO, a real therapeutic-discovery system in whichDry-Lab reasoning, robotic and hands-onWet-Lab experimentation, external biological evidence, and iterative hypothesis and design revision form aclosed physical discovery loop. We then formulate a unified approach to capability formation andprocess-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
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