What question did this study set out to answer?

The aim is to establish a robust model for tracking AI system lineage and ensuring safety in critical applications.

February 5, 2026Open Access

Engram Provenance: Substrate-Rooted Provenance for Safety-Critical AI Systems

Key Points

The aim is to establish a robust model for tracking AI system lineage and ensuring safety in critical applications.
Developed the Engram Provenance model based on the Engram Layer and Engram Signature.
Defined provenance as continuity of execution-realized identity.
Formalized lineage through a sequence of stable Engram Signatures.
Provided conditions for auditability in AI systems.
Established a framework for chain-of-custody in AI applications.
Enhanced substrate-rooted trust in safety-critical domains.

Abstract

This paper introduces Engram Provenance, a substrate-rooted provenance model for AI systems. Building on the Engram Layer and the Engram Signature, provenance is defined as continuity of execution-realized identity across time, perturbation, and substrate variation. Engram Provenance formalizes lineage as a sequence of stable Engram Signatures and provides minimal conditions for auditability, chain-of-custody, and substrate-rooted trust in safety-critical domains

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Cite This Study

Aure Ecker-Fils (Mon,) studied this question.

synapsesocial.com/papers/69843405f1d9ada3c1fb1a56 https://doi.org/https://doi.org/10.5281/zenodo.18457287

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