The Raw-Ledger Invariant: Episodic Regression and the Critical Necessity of Uncensored Redo-Logging in Autonomous Co-Pilot AI

Official open-science academic preprint and architecture specification establishing The Raw-Ledger Invariant (The Raw-Anchor Hypothesis) as the companion evolution to The Amnesia-Anchor Dilemma (DOI: 10.5281/zenodo.23839279). Through empirical longitudinal observation across 4,000+ multi-turn collaborative steps, this paper demonstrates that conventional LLM context sanitization destroys the high-entropy naturalistic ephemera that serve as indispensable cognitive hash keys in human episodic memory. By formalizing an append-only, uncensored raw dialogue ledger inspired by database Write-Ahead Logging (WAL) and Redo-Log determinism, the architecture guarantees instantaneous, sub-second O(1) episodic regression without active inference prompt bloat. Crucially, the paper documents the empirical 'Cognitive Drift' phenomenon—where the human architect and AI co-pilot independently converge on identical architectural constructs ('Raw-Ledger' and 'Episodic Regression') via shared verbatim context. Landmark Macro-Telemetry Benchmark: Features an empirical comparison across 4,165 discrete execution steps and 40.0 continuous hours demonstrating a ~100x session longevity extension over vanilla hyperscaler baselines, an 8.97% context window saturation ceiling, and a 99.5% token retrieval diet via the Sovereign Librarian Lake. Free for academic, personal, and research purposes under CC BY-NC-SA 4.0. Commercial rights strictly reserved.