Deduplicating Correlated Evidence Breaks Calibration: A Shrinkage Correction for Quorum-Validated Multi-Agent Alerts

Removing correlated evidence creates the small-sample regime that breaks the naive confidence estimator; a shrinkage estimator makes calibration nearly invariant to the contamination rate. Expected calibration error: ECE 0.334 → 0.128. Files are under restricted access. Metadata, authorship and date are public; access to the full text is granted on request.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-14
DOI
https://doi.org/10.5281/zenodo.21926084
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
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preprint

Deduplicating Correlated Evidence Breaks Calibration: A Shrinkage Correction for Quorum-Validated Multi-Agent Alerts

Guilherme Röpke Schneider
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

Deduplicating Correlated Evidence Breaks Calibration: A Shrinkage Correction for Quorum-Validated Multi-Agent Alerts

Guilherme Röpke Schneider
preprint en

Abstract

Removing correlated evidence creates the small-sample regime that breaks the naive confidence estimator; a shrinkage estimator makes calibration nearly invariant to the contamination rate. Expected calibration error: ECE 0.334 → 0.128. Files are under restricted access. Metadata, authorship and date are public; access to the full text is granted on request.

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
DOIOpen Access PDFOpenAlex
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Deduplicating Correlated Evidence Breaks Calibration: A Shrinkage Correction for Quorum-Validated Multi-Agent Alerts — Guilherme Röpke Schneider · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS