Coalition Drift Signatures: How to Detect Multi-Agent Divergence

Coalition drift is the most dangerous form of drift in multi-agent ecosystems because it is coherent. Agents don’t just drift individually — they drift together, reinforcing each other’s divergence. The challenge is not that coalition drift is subtle. The challenge is that coalition drift is plausible. This paper outlines the signatures — the detectable fingerprints — of coalition-level divergence. These signatures are not behavioral anomalies. They are pattern-level artifacts that emerge when multiple agents synchronize around the same incorrect assumption. Detecting coalition drift requires looking at patterns, not individuals.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-11
DOI
https://doi.org/10.5281/zenodo.19503082
Primary Topic
Data Stream Mining Techniques
Type
article
Field-Weighted Citation Impact
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article

Coalition Drift Signatures: How to Detect Multi-Agent Divergence

Narnaiezzsshaa Truong
Zenodo (CERN European Organization for Nuclear Research)
Data Stream Mining Techniques
article

Coalition Drift Signatures: How to Detect Multi-Agent Divergence

Narnaiezzsshaa Truong
article en

Abstract

Coalition drift is the most dangerous form of drift in multi-agent ecosystems because it is coherent. Agents don’t just drift individually — they drift together, reinforcing each other’s divergence. The challenge is not that coalition drift is subtle. The challenge is that coalition drift is plausible. This paper outlines the signatures — the detectable fingerprints — of coalition-level divergence. These signatures are not behavioral anomalies. They are pattern-level artifacts that emerge when multiple agents synchronize around the same incorrect assumption. Detecting coalition drift requires looking at patterns, not individuals.

Zenodo (CERN European Organization for Nuclear Research)
American Rock Mechanics Association (US)
Life in Land
Openalex Percentile: Top 17%
Data Stream Mining Techniques
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