autoresearch-mlx

# Research notebook Read `studio.json` to understand the current controls; `"$STUDIO_TOOLCHAIN/../studio.config.json"` describes their ranges. Run `"$STUDIO_TOOLCHAIN/run.sh" train` to make a new result. Successful artifacts and their measurements are in `out/runs/<id>/`; `out/latest.json` names the current result. A failed run preserves the last success and records the error in the verdict. The local starter trains a small character transition model with NumPy on a bundled, original text corpus. It uses real training and held-out cross-entropy, not generated metrics. It is a CPU baseline, distinct from upstream MLX transformer training, which requires Apple Silicon and its prepared dataset. Use `"$STUDIO_TOOLCHAIN/../README.md"` for the integration contract and commands. Read the relevant files under `$STUDIO_UPSTREAM` before using an upstream API. Keep controls within their documented ranges, preserve the data needed to reproduce a comparison, and distinguish preview results from native service or hardware output. The viewer supports history and artifact downloads; tell the user which run contains the result, and what was actually measured. ## Run a controlled experiment Read `train.py`, `train.txt`, and `holdout.txt`. Save a baseline. Change one training choice or the editable training code, then run `train`. Inspect `evaluation.json`, the saved model, and `learning.csv`. Keep the holdout unchanged; the runner independently reopens the model with pickle disabled and c...