Geodesic Hard Drive (GHD): Provable AI Memory via Dual-Layer Compression and Immutable Capsule Replay

This work establishes prior art for the Geodesic Hard Drive (GHD), a system for AI memory integrity that combines dual-layer compression (symbolic + raw) with immutable capsule replay. The method preserves semantic structure and raw data simultaneously, enabling verifiable memory reconstruction without hallucination drift. Core contributions include: Dual-Layer Binding – Symbolic compression and raw data are cryptographically bound via a Poseidon2 hash to ensure deterministic recovery. Immutable Capsules – Memory states are packaged in sealed capsules supporting zk-verifiable replay. Replay Integrity Metrics – Capsules include embedded measures of compression quality variance (CQV), symmetry retention, and drift δ without disclosing underlying metric computation. Scalable Audit Hooks – The design supports low-latency replay verification and role-based access control without exposing opcode logic or graph topology. This disclosure omits implementation specifics (e.g., curvature replay algorithms, opcode instruction sets, or compression graph structure) to preserve the security posture of commercial deployments. It is intended to establish defensive prior art for the high-level architecture and claims herein.