@ProfBuehlerMIT: We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific dis…
Summary
Researchers at MIT present a paper on self-evolving AI scientists that can discover and adapt their own scientific vocabulary, using a categorical framework to mathematically quantify genuine novelty and separate discovery from mere search or retrieval.
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We’ve made a breakthrough in self-evolving AI scientists moving from “search” to “principled discovery”: Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover the scientific vocabulary it reasons in. Evidence, tools, artifacts, verifiers, failures & claims become typed provenance. We show three distinct modalities: 1) retrieval, adding known objects; 2) search, exploring a fixed schema; and critically: 3) discovery, a verified regime transition.
We solve the open-endedness evaluation problem by lifting agentic workflows into a typed copresheaf and proving, via a Kan obstruction, that true discovery is not unbounded generation but a verifiable schema expansion: old evidence is transported by Left Kan extension, and genuine novelty is mathematically quantified by the pointwise residual beyond the transported image - separating discovery from mere search and making novelty objective and measurable rather than a subjective judgment or benchmark delta.
Our AI scientist is built in a way that does not pre-conceive the approach it chooses; instead, we endow the system with formal power to adapt, evolve, and reason from first principles. Case studies include: Builder/Breaker model that discovers mode-conditioned compliance in proteins; CategoryScienceClaw that finds anisotropic fiber-network stiffness rules.
Great work in collaboration with my graduate student @fwang108_ @MITdeptofBE
F.Y. Wang & M.J. Buehler, Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence, arXiv:2606.01444, 2026
Paper:
Thank you @somi_ai great points. The critique would apply if typing were post hoc but in our framework the schema itself is part of the audited state: typed operations, immutable lineage, gates, rejected alternatives, and explicit regime transitions. Novelty is only the residual beyond the transported image (i.e. what carrying old evidence over by Left Kan extension cannot produce) and only once the new state passes its own gate. In Builder/Breaker mode-conditioned compliance survived MDL on enlarged evidence and arbitrary refinements were rejected/retracted. In other words subjectivity is not hidden in the ontology choice - it is exposed, constrained, and audited.
Thank you @AlphaWireHQ !
Thank you!
You’re right that L is subadditive and we don’t assume additivity. Discovery cost is defined as the conditional code length L(I′ | im ρ̄); the subtraction L(I′) − L(im ρ̄) is only its additive special case (a subadditive upper bound otherwise). The figure numbers aren’t that cost anyway but instead they are paired MDL acceptance gains at fixed evidence which we flag explicitly as ‘not a direct numerical discovery cost’. So the conditional form is the cross-regime primitive; the figures’ before/after gaps are a separate fixed-evidence quantity. We never equate them.
Thank you
Thank you!
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