From Runtime Logs to Decision Proof_ Why Consequential AI Requires Verifiable Decision Objects

This paper argues that increasingly autonomous AI systems require a new evidentiary primitive beyond conventional observability. Logs, traces, metrics, and audit events explain how software executed but do not necessarily preserve the consequential decision itself as an independently verifiable object. The paper introduces the concept of the decision object: a canonical artifact created at runtime that packages the evidence boundary, governing policy, authority chain, decision output, and cryptographic commitment required for later verification. This yields a shift from reconstructing decisions after the fact toward verifying them under declared regimes. The paper distinguishes decision proof from observability, develops the architectural consequences of runtime decision objects, and argues that decision proof constitutes a distinct infrastructure layer for governance, regulation, appeals, adjudication, and institutional trust. VERIFY is presented as a reference implementation of the broader architectural category rather than as the subject of the paper.