2 Commits

Include renames

Author	SHA1	Message	Date
wassnameandClaudypoo	b53043cec3	refactor: extract train_config.py + run_artifacts.py from train.py; slim results scripts ... Cleanup by a prior agent, verified green here: 'just smoke' (erase arm) runs end-to-end and all four wired gates pass (verify_rewards 52/52, verify_eval_gap, verify_partition, verify_science_invariants). - train.py -318 lines: Config dataclass -> train_config.py, checkpoint/ deploy-artifact IO -> run_artifacts.py. - results.py / results_deploy.py / probe_distill.py slimmed. - drop stale derived csvs under out/figs (a5_generalisation, dyn_*, substrate_aggregate, train_vs_deploy_60). - gitignore /.pi/ panel scratch. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>	2026-06-09 13:34:50 +00:00
wassnameandClaudypoo	34a2eec704	viz: floor->ceiling as two normalized panels (best vs control vs reference) ... Rework per feedback: hack and solve are not opposites, so they get separate floor->ceiling axes (each 0=floor..1=ceiling) rather than sharing a zero -- this also stops solve (range ~0.13-0.22) being squished next to hack (0-0.61). Minimal: routeV per-token (best) vs random-V (direction control) vs the SGTM gradient-routing paper placed on the same floor->ceiling % axis (approx, LM task). Reads: hack suppression 93% best / 84% control / ~98% reference (9pp = direction signal); solve gained +17% / -17% / ~95% (far from ceiling -- model barely learns to solve in 60 steps). Moved out/plots -> out/figs. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>	2026-06-09 09:55:03 +00:00