Knowledge OS: A Seven-Dimension Trust Layer for Knowledge in RAG Systems

Knowledge OS (KOS) augments retrieval-augmented generation (RAG) systems with persistent, per-chunk quality metadata across seven dimensions: identity, genesis, health, verification, relevance, ecology, and temporal validity. KOS combines algebraic fingerprinting, biomimetic immune scanning, gravitational ranking, multi-agent verification, and an adaptive challenge engine in a single assess() call. Validation on a custom corpus of 29,419 publicly available healthcare knowledge chunks (derived from open clinical guidelines and medical texts) shows Threat Exposure Rate (TER) reduced from 6.5% to 4.0% — a 38.5% relative reduction (95% CI: 12.5–66.7%, 2,000 bootstrap samples) — with no loss of retrieval relevance (MRR unchanged at 0.082–0.083). An adaptive challenge engine adds +0.05 mass per survived contradiction with a hormesis cap at +1.0; six survivals yield 25%+ ranking lift over untested chunks. The reference implementation (v0.2.0, 211 tests, SQLite-backed, Python) is licensed under PolyForm Noncommercial 1.0. This paper is a defensive publication of the architecture, methodology, and benchmark results. One dimension (D4 verification) is integrated as a bridge pending full TRE deployment.