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.
Quellinformationen
- Repository
- qualit527/qec-ai-decoder
- Letzte Quellaktivität
- 22. April 2026 um 05:58
- Erkannte Sprache von SKILL.md
- Englisch
- Sterne
- 3
- Forks
- 0
Installationsoptionen
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau- 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`.
Auf GitHub ansehen