We introduce the Knowledge Layer Model (KLM), an implementation‑neutral specification for recording inference traces with versioned epistemic assessments, evidence links, and explicit unknowns. The reference implementation is SEDIM, the knowledge‑and‑evidence architecture of Nage AI OS, currently at version v0.3 (repo tag v0.3.0).
The empirical material consists of separate internal experiments, not an end‑to‑end validation. A historical platform‑domain routing benchmark reports 137/150 correct classifications across 15 classes (91.3%, 95% Wilson interval 85.7‑94.9%). The numerator is reconstructed from a rounded summary, and the membership of historical examples is unverified. A native‑model weight‑path configuration yielded 0/15 successful answers in a corrected two‑source test.
A capacity‑growth comparison could not establish a reliable effect because of a defective legacy scorer and missing original outputs. Lexical and scoped diagnostics each produced an AUC of 0.008 for silent‑versus‑contradicted examples, far below the registered minimum of 0.65. Legacy packing lost gold‑containing text in 118/221 selected cases; repaired packing retained it in 221/221, measuring text presence rather than answer quality.
Python and TypeScript verifiers reportedly agreed on six checks per synthetic fixture; a separate production disclosure was not accepted. Restoration, re‑derivation, human‑rubric support, boundary isolation, and bounded‑response evaluations remain proposed. Findings are internal and author‑reported; historical observations are distinguished from later reproductions. Evidence visibility does not establish correctness, causal reliance, security, or regulatory compliance. Independent replication and a complete public audit package remain future work.
Review: KLM offers a structured framework for making AI inference evidence explicit, yet current experimental data lack reproducibility and comprehensive quality metrics, so its practical impact remains to be demonstrated.