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architecturetechnical paper candidatescore 46
Training And Learning - Source-Grounded Overview
1. **data capture** - MotionMixApp logs 128D SAN input and output frames; 2. **model training** - an offline process that must produce weight artifacts and validation evidence.
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1. **data capture** - MotionMixApp logs 128D SAN input and output frames; 2. **model training** - an offline process that must produce weight artifacts and validation evidence.
The code proves capture and runtime loading exist. It does not, by itself, prove the current models are well trained.
But `MASTER-TASKS.md` describes the CoreML generation models as shell/zero-init placeholders at that checkpoint, so quality must be re-verified before claiming trained generation.
- exact training data path; - frame count; - train/validation split; - training script path; - checkpoint path; - exported `san_weights.bin` and `san_manifest.json`; - parameter count; - validation metrics; - on-device load count; - live non-flat output; - `mixFactor` greater than zero in the behavior being evaluated.
- exact training data path; - proof that `ConditioningEncoder` is not a placeholder; - proof that `FlowGenerator1Step` is not a placeholder; - output token-grid sanity checks; - comparison against rule-based fallback; - latency on device.
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