GhostDrift publishes Lean 4 proof for ADIC replay verification

By AI, Created 10:16 AM UTC, May 20, 2026, /AGP/ – GhostDrift Mathematical Institute has published a machine-checkable Lean 4 proof for ADIC’s replay-verification core, making the system’s soundness theorem publicly available for independent checking. The release is aimed at turning AI governance claims into replayable evidence for regulators, auditors, and other third parties.

Why it matters: - ADIC is positioned to turn AI governance claims into evidence that outside parties can replay, inspect, and challenge. - The published Lean 4 proof gives third parties a machine-checkable way to verify the replay-verification core instead of relying on policy statements or vendor narratives. - The release matters for high-responsibility AI use cases where documentation, accountability, and independent assurance are becoming central requirements.

What happened: - GhostDrift Mathematical Institute published a formal proof in Lean 4 for ADIC’s replay-verification core on May 15, 2026. - The proof artifact is publicly available on GitHub and Zenodo, with no registration required. - The core theorem, verifierBool_sound, states that if the verifier accepts a certificate, that certificate is semantically valid. - GhostDrift says the proof can be independently executed and checked by anyone who downloads the artifact.

The details: - ADIC stands for Advanced Data Integrity by Ledger of Computation. - ADIC records AI decisions, the full decision pathway, and acceptance conditions in replayable certificates. - Third parties can mechanically replay those certificates and confirm them without relying only on the original operator’s account. - Lean 4 is a theorem-proving system used to mechanically verify mathematical theorems and software properties. - GhostDrift says the proof shows the replay-verification core is not just a plan or engineering claim, but a formally verified property. - The company says the approach moves AI assurance from evidence that is merely reviewed to evidence that can be independently re-executed. - GhostDrift says the goal is to give regulators, auditors, counterparties, or dispute resolution bodies a way to retrace an AI decision pathway, verify its basis, or challenge it. - The release points to broader AI governance trends, including the EU AI Act’s focus on technical documentation and conformity assessment, NIST AI RMF’s Govern-Map-Measure-Manage structure, and the UK’s emphasis on measuring, evaluating, and communicating AI trustworthiness. - The proof artifact is linked in the release as the GitHub repository for direct execution and review.

Between the lines: - The release is not just about proof-carrying software. It is about whether AI systems can produce evidence that survives later disputes, audits, or regulatory review. - GhostDrift is trying to define AI assurance as something that can be mechanically replayed, not merely described. - That framing could appeal to organizations that need defensible records more than narrative compliance reports.

What’s next: - GhostDrift says pharmaceutical cold-chain logistics is the first implementation target for ADIC. - The company says the setting is a strong test case because multiple organizations handle regulated decisions that must remain auditable after the fact. - In April 2026, GhostDrift entered a strategic partnership with ONZALINX Co., Ltd., an ISO 27001-certified logistics technology company. - An ongoing proof of concept is testing whether ADIC can meet the auditability needs of multi-party logistics operations. - If the logistics test is validated, GhostDrift says the architecture could be extended to financial decision-making and public-sector AI. - The proof artifact remains public and independently re-executable for technical review.

The bottom line: - GhostDrift is trying to make AI assurance provable, replayable, and independently checkable — not just documented.