Mixture of Anticipatory Orthogonal Experts for N'Ko ASR

MAOE-N'Ko, the Mixture of Anticipatory Orthogonal Experts for N'Ko ASR, is a modular speech-language correction architecture that keeps the acoustic model sovereign while allowing language-prior intelligence to act only where it is admissible. The system begins with a verified N'Ko trajectory CTC acoustic model, currently anchored by the Paper 4 reproduction checkpoint with 20.57 percent CER on the locked N'Ko run. Instead of replacing that model with a monolithic audio-language system, MAOE-N'Ko routes each ASR chunk into an anticipation partition: stable, boundary, uncertain, recovery, or novelty. Each partition activates a different expert lane with a distinct authority contract: acoustic preservation, boundary completion, uncertain repair, recovery context, or novelty quarantine. The architecture differs from a conventional mixture-of-experts model because the experts are orthogonal in authority, not merely parallel in capacity. A normal MoE router selects among experts that all try to improve token likelihood. MAOE-N'Ko selects among experts that are allowed to do fundamentally different things. Stable evidence is preserved. Boundary evidence can accept a small completion. Uncertain evidence can consult an AGP correction prior and TurboQuant-backed retrieval. Recovery evidence can use context, but under a stricter edit cap. Novel evidence is blocked from language-prior normalization and routed into review/corpus growth. The final accept/reject decision is not made by the neural model. It is made by a deterministic Rust control plane with an admissibility witness. The result is not merely "ASR plus a language model." It is a layered authority system for endangered-script speech recognition. The acoustic model answers what was heard. AGP proposes what may be structurally plausible. TurboQuant compresses retrieval and provenance state. RAG++ and Graph Kernel-style witnesses preserve evidence chains. Rust enforces bounded correction. Future Core ML / Apple Neural Engine heads can run the small route, vitality, and partition classifiers without claiming that ANE replaces full transformer inference. The paper contribution becomes credible if same-snapshot Paper 4 replay shows lower CER after expert routing while accepted-worse corrections remain zero or statistically negligible.