CHRONICLE: The Missing Epistemic Layer for AI

CHRONICLE — Chronological Recall Optimization via Native Imitation of Consolidated Longitudinal Experience — is an architectural framework that addresses this problem by introducing auditable knowledge boundaries: a persistent, structured, and compounding record of what a system knows, what it does not know, and what it has learned about the difference over time. Chronicle is a unified epistemic ledger — a single queryable store with two record types that together form a complete epistemics architecture. Chronicle-K entries accumulate knowledge derived from real interactions through a four-layer hierarchical compression model (from high-fidelity daily entries through monthly, yearly, and multi-year aggregations), functioning as navigational infrastructure that enables AI systems to traverse decades of accumulated knowledge efficiently. Chronicle-G entries log what the system was queried about and could not adequately answer: a persistent, structured record of knowledge boundaries, with full context, domain classification, and resolution history across the same four-layer compression hierarchy.