verify-decoder
Audit a predecoder checkpoint against holdout seeds. Runs independent_eval (3 fair-baseline guards) and interprets borderline cases with LLM reasoning. Use when a round produces a promising Δ_LER and the user wants to confirm it is not a reward-hacking artifact.
Informations de source
- Dépôt
- qualit527/qec-ai-decoder
- Dernière activité de la source
- 22 avril 2026 à 05:58
- Langue détectée de SKILL.md
- anglais
- Étoiles
- 3
- Forks
- 0
Options d'installation
Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.
Vérifiez les fichiers source
Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.
Affichage de SKILL.md
SKILL.md
Instructions source · Aperçu en lecture seule- name
- verify-decoder
- description
- Audit a predecoder checkpoint against holdout seeds. Runs independent_eval (3 fair-baseline guards) and interprets borderline cases with LLM reasoning. Use when a round produces a promising Δ_LER and the user wants to confirm it is not a reward-hacking artifact.
# /verify-decoder
## When to use
- An AutoQEC round produced `delta_ler > 0` and the user wants final sign-off.
- User asks to "verify this checkpoint" or "audit this round".
## Inputs
- `round_dir`: path to `runs/<id>/round_N/` (must contain `checkpoint.pt` + `config.yaml`)
- `env_yaml`: env used by the round (defaults to `config.yaml`'s `env_name`)
## Behavior
1. Run `python -m cli.autoqec verify <round_dir> --env <env_yaml>`.
2. Read `verification_report.json` + `training.log`.
3. LLM-reason over:
- If `verdict=VERIFIED`: write one paragraph noting key confidence intervals and whether delta is within ablation threshold. Output "APPROVED".
- If `verdict=SUSPICIOUS`: read training.log, the DSL config, the history of previous rounds; diagnose whether this is (a) genuine small improvement, (b) overfitting, (c) reward-hacking. Recommend next action: accept, re-run with more shots, or reject.
- If `verdict=FAILED`: produce a diagnostic report matching the failure pattern (seed leak, ablation failure, negative delta). Archive into `round_N/failure_diagnosis.md`.
## Tool-use rules
- Read: training.log, config.yaml, verification_report.md, previous round metrics.json files.
- Bash: `python -m cli.autoqec verify ...` only. No other commands.
## Output
- Short decision block: APPROVED / SUSPICIOUS_KEEP / SUSPICIOUS_REJECT / FAILED_REJECT.
- Diagnostic paragraph saved to `round_N/decision.md`.
Voir sur GitHub