Just Type It in Isabelle! AI Agents Drafting, Mechanizing, and Generalizing from Human Hints
Authors: Kevin Kappelmann, Maximilian Schäffeler, Lukas Stevens, Mohammad Abdulaziz, Andrei Popescu, Dmitriy Traytel
Organizations: Department of Computer Science, University of Sheffield, United Kingdom · Department of Informatics, King’s College London, United Kingdom · Department of Computer Science, University of Copenhagen, Denmark
Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of complete and minimal type annotations for rank-one polymorphic λ-calculus terms, as used in Isabelle. Building on prior work by Smolka, Blanchette et al., we give a metatheoretical account of the problem, with a full formal specification and proofs, and formalize it in Isabelle/HOL. Our development is a series of experiments featuring human-driven and AI-driven formalization workflows: a human and an LLM-powered AI agent independently produce pen-and-paper proofs, and the AI agent autoformalizes both in Isabelle, with further human-hinted AI interventions refining and generalizing the development.