Make reference auditors honor `audit_scoring_policy` in the manifest

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#330 4 commentaires 0 réactions 0 personnes assignées Voir sur GitHub

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@0xzoz y travaille déjà.

Depuis le 12/11/2026.

Évaluation

Difficulté
4/5
Temps estimé
3-5 jours
Accessibilité débutants
54/100
Type d'issue
Bug
Clarté
Clairement spécifiée
Activité
Active
Stack technique
python, solidity
Domaine
machine-learning, testing

Piste de recherche

Read both reference implementations of load_scoring_policy, including cache_model_1/services/scoring.py, and compare them with the GI-start consumer at dincli/cli/modelownerd/gi.py and the shipped manifest. Trace both auditors' service tests and run focused cases with two different min_accuracy thresholds. Done means both auditors honor the documented canonical field, edge cases for missing, legacy, conflicting, and malformed policies are covered, and the example manifests and documentation explain the median rule.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Description

Approved

Observed against ed175d0deceddfa2e63175ec7752c800b31c1fbf during the November 10 Fashion-MNIST local test investigation. The subsequent full round with three notebooks and a Raspberry Pi used recorded workarounds; its success does not establish that this upstream defect is fixed. Related: #276 and #39.

Problem and evidence

The shipped manifests put thresholds under audit_scoring_policy, and GI-start code reads that field. Both reference load_scoring_policy implementations read scoring_policy. With the shipped layout they silently use defaults instead of the configured audit thresholds.

A focused reproduction extracted the actual upstream loader and default dictionary, wrote the shipped manifest with min_accuracy changed to 99, and called the loader. Both models returned an effective threshold of 50.0. This is a loader-level reproduction, not a second live rejection/acceptance experiment.

The numeric defaults matched our intended gates during the completed round, so this does not show that a model was wrongly accepted in that run. It shows that a model owner's non-default policy can be ignored.

Proposed scope

Choose and document one canonical field across manifests and consumers. If supporting the old name, define explicit precedence or reject conflicting values. Validate policy shape and test non-default values through the auditor service. Align the stale legacy_mean_until_contract_v2 metadata with the median already used on-chain; do not change the contract aggregation rule as part of this fix.

Acceptance criteria
  • Changing audit_scoring_policy.metrics.thresholds.min_accuracy changes the policy used by both reference auditors.
  • A known model passes/fails as expected under two deliberately different thresholds.
  • Missing fields, legacy aliases, conflicting fields and malformed policies have documented behavior.
  • GI pass-score configuration and auditor eligibility policy remain distinct, intentional controls.
  • Example manifests and documentation describe the effective median rule.
Full-round test report

See Discussion #331 — Fashion-MNIST with three notebooks and a Raspberry Pi for the setup diagram, completed protocol roles, results and experiment adaptations.

Langage dominant
Python
Étoiles
4
Forks
7
Merge moyen
5 j 15 h
PR mergées (30 j)
43

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