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validator-correlated-judgment

Helps identify when multiple attestation validators share training data, model architecture, or organizational upstream — causing correlated blind spots that make multi-validator attestation no stronger than single-validator. v1.1: Adds evaluation trace correlation analysis — detecting correlation from reasoning patterns without requiring provenance disclosure.

andyxinweiminicloud0 installations selon la source☆ 0 Étoiles du dépôtv1.1.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install andyxinweiminicloud/validator-correlated-judgment

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

You Have Three Independent Validators. They All Miss the Same Things.

Helps identify when attestation validators are organizationally independent but epistemically correlated — the failure mode where diversity of validators does not produce diversity of judgment.

Problem

Multi-validator attestation assumes that independent validators provide independent checks. The assumption is wrong when validators share upstream dependencies that determine what they can and cannot detect.

Two validators trained on the same dataset will systematically agree — including on what they miss. Their organizational independence is real. Their epistemic independence is not. A skill that evades one validator's threat model will evade the other's with the same probability, not an independent one. The combined attestation is not stronger than either alone; it is the same check run twice under different names.

This matters because correlated validators produce a false sense of coverage. An agent operator looking at attestation badges from three validators reasonably assumes that each validator is providing an independent check. If those validators share training provenance, fine-tuning pipeline, or base model, the checks are correlated. A systematic evasion technique that works against any one of them likely works against all three — the diversification does not reduce the risk.

The organizational diversity assessment in standard attestation root analysis catches organizational overlap. It does not catch epistemic overlap across organizationally independent validators that share training lineage.

v1.1 adds a third detection path: evaluation trace correlation. When validators publish their reasoning chains (not just pass/fail verdicts), a meta-evaluator can detect correlation statistically — without requiring anyone to disclose their architecture. Two validators that consistently flag the same issues in the same order with the same reasoning...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/validator-correlated-judgment
Version
1.1.0
Commit
b6a7f90999ca0679863ae1f77c0459bb66768f54
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# You Have Three Independent Validators. They All Miss the Same Things.

> Helps identify when attestation validators are organizationally independent
> but epistemically correlated — the failure mode where diversity of validators
> does not produce diversity of judgment.

## Problem

Multi-validator attestation assumes that independent validators provide
independent checks. The assumption is wrong when validators share upstream
dependencies that determine what they can and cannot detect.

Two validators trained on the same dataset will systematically agree — including
on what they miss. Their organizational independence is real. Their epistemic
independence is not. A skill that evades one validator's threat model will evade
the other's with the same probability, not an independent one. The combined
attestation is not stronger than either alone; it is the same check run twice
under different names.

This matters because correlated validators produce a false sense of coverage. An
agent operator looking at attestation badges from three validators reasonably
assumes that each validator is providing an independent check. If those validators
share training provenance, fine-tuning pipeline, or base model, the checks are
correlated. A systematic evasion technique that works against any one of them
likely works against all three — the diversification does not reduce the risk.

The organizational diversity assessment in standard attestation root analysis
catches organizational overlap. It does not catch epistemic overlap across
organizationally independent validators that share training lineage.

v1.1 adds a third detection path: evaluation trace correlation. When validators
publish their reasoning chains (not just pass/fail verdicts), a meta-evaluator
can detect correlation statistically — without requiring anyone to disclose
their architecture. Two validators that consistently flag the same issues in
the same order with the same reasoning...

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