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Implementation Confirmation - MAOE vs Trajectory Bias
You were right: the verified N'Ko ASR number comes from the anticipatory / trajectory-biased Transformer CTC model, not from MAOE routing.
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You were right: the verified N'Ko ASR number comes from the anticipatory / trajectory-biased Transformer CTC model, not from MAOE routing.
- `Desktop/nko-brain-scanner/HANDOFF.md` - `Desktop/nko-brain-scanner/asr/trajectory_asr.py` - `Desktop/nko-brain-scanner/constrained/trajectory_bias.py` - `Desktop/MAOE-NKo-Technical-Architecture.md`
- model: N'Ko Trajectory CTC - checkpoint family: Paper 4 reproduction - reported held-out result: 20.57% CER - architecture: `UnifiedCTCHead` - decoder: 6-layer Transformer - key mechanism: trajectory-bias injection - trajectory components: - `AudioTrajectoryScalars` - `TrajectoryBiasNetwork` - `TrajectoryTransformerLayer`
MAOE is not the acoustic model that produced the verified ASR checkpoint. In the current local architecture, MAOE is the routed correction and admissibility layer around the ASR anchor.
- `partition_policy.py`: classifies ASR telemetry into `stable`, `boundary`, `uncertain`, `recovery`, or `novelty`. - `expert_router.py`: maps each partition to an expert lane and safety contract. - Rust/admissibility layer: decides whether a proposed correction is allowed.
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