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.本研究确立了测地线硬盘（Geodesic Hard Drive, GHD）的现有技术，该系统面向AI内存完整性需求，结合了双层压缩（符号压缩+原始数据压缩）与不可变胶囊重放技术。该方法可同时保留语义结构与原始数据，实现可验证的内存重建，且不会出现幻觉漂移。 核心贡献包括： - 双层绑定：符号压缩数据与原始数据通过Poseidon2哈希进行密码学绑定，确保可确定性恢复。 - 不可变胶囊：内存状态被封装于密封胶囊中，支持零知识可验证（zk-verifiable）重放。 - 重放完整性指标：胶囊内嵌压缩质量方差（Compression Quality Variance, CQV）、对称性保留度以及漂移δ的度量值，且无需披露底层度量计算逻辑。 - 可扩展审计钩子：该设计支持低延迟重放验证与基于角色的访问控制，且不会暴露操作码逻辑或图拓扑结构。 本公开未披露具体实现细节（例如曲率重放算法、操作码指令集或压缩图结构），以保障商业部署的安全态势。本研究旨在为本文所述的高层架构与权利要求确立防御性现有技术。