Sign inGet started
Paper · arXiv

Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence

Scientific discovery is not only answer generation but revision of the representational regime in which evidence, artifacts, operations, and verifiers are typed. We develop a category-theoretic account of agentic discovery for materials science. In a fixed regime b with schema category S_b, the system state is a copresheaf I_t: S_b -> Set, and provenance is the category of elements \int_{S_b} I_t. Fixed-regime operation is an update on such states, endofunctorial only when provenance-preserving refinements are specified and preserved. Discovery is instead a verified regime transition u: S_b -> S_b': old artifacts are preserved, transported by the left Kan extension Lan_u I_t, and compared...

AuthorsFiona Y. Wang, Markus J. Buehler
Primary categorycs.AI
Published31 May 2026
Last updated at source21 August 2026
cs.AIcond-mat.mtrl-scics.CLcs.LGmath.CT
Embed this entry
<iframe src="https://silytics.si/embed/item/6a4416dd-f1a8-41ce-a479-8da99d38d577" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Connected in the knowledge graph

Explore this entry in the graph

Models and datasets referencing this paper

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.