AGP/N'Ko + Vast Training Handoff

You are continuing the N'Ko ASR training and AGP corrective-adapter program. The goal is not just to run more jobs; the goal is to finish the matched experimental bundle cleanly enough that the papers can make defensible claims about N'Ko script advantage, trajectory conditioning, TAR, and TTT. 1. **Acoustic ASR training on Vast/A100**: PyTorch/Whisper trajectory model family. This is where Paper 4 CER numbers come from. 2. **AGP corrective language layer on Mac4/Mac5**: MLX/Gemma LoRA adapter that proposes N'Ko text repairs after ASR decoding. Rust/Graph Kernel decides whether those repairs are admissible. Do not merge those into one imagined model unless you explicitly port the ASR stack to MLX. Right now they are separate training/runtime stacks. - dataset: `290,596` pairs - split: `232,476 / 29,060 / 29,060` - script: N'Ko - mode: trajectory - `use_trajectory=true` - `use_tar=false` - `use_ttt=false` - final/test CER: `20.57%` - best validation: about `0.635887` - best checkpoint epoch: `38` - early stop: epoch `46` Important: the filename `train_vastai_tar_ttt.py` is broader than the actual winning mode. The verified 20.57% run did **not** use TAR or TTT.