[2603.25414] Decidable By Construction: Design-Time Verification for Trustworthy AI

Computer Science > Programming Languages arXiv:2603.25414 (cs) [Submitted on 26 Mar 2026] Title:Decidable By Construction: Design-Time Verification for Trustworthy AI Authors:Houston Haynes View a PDF of the paper titled Decidable By Construction: Design-Time Verification for Trustworthy AI, by Houston Haynes View PDF HTML (experimental) Abstract:A prevailing assumption in machine learning is that model correctness must be enforced after the fact. We observe that the properties determining whether an AI model is numerically stable, computationally correct, or consistent with a physical domain do not necessarily demand post hoc enforcement. They can be verified at design time, before training begins, at marginal computational cost, with particular relevance to models deployed in high-leverage decision support and scientifically constrained settings. These properties share a specific algebraic structure: they are expressible as constraints over finitely generated abelian groups $\mathbb{Z}^n$, where inference is decidable in polynomial time and the principal type is unique. A framework built on this observation composes three prior results (arXiv:2603.16437, arXiv:2603.17627, arXiv:2603.18104): a dimensional type system carrying arbitrary annotations as persistent codata through model elaboration; a program hypergraph that infers Clifford algebra grade and derives geometric product sparsity from type signatures alone; and an adaptive domain model architecture preserving both...