Engram Commitments: A Cryptographically Verifiable Substrate‑Rooted Identity for Large Language Models

Abstract

Engram Commitments introduce a cryptographically verifiable, substrate-rooted identity primitive for large language models. The method extracts engrams from differential execution behavior, aggregates them into an engram vector, compresses this representation using locality-sensitive hashing, and seals it inside a binding-and-hiding cryptographic commitment. Zero-knowledge proofs enable verification of identity continuity and lineage without revealing model parameters. The construction remains stable under non-destructive transformations and degrades predictably under destructive ones, supporting collapse-aware auditing, tamper-evident provenance, and regulator-verifiable attestation. This work unifies the engram calculus, identity ontology, collapse taxonomy, and cryptographic commitments into a single framework for AI provenance, governance, and safety.