Harnessing Disagreement: Detecting Correlated Agreement Blindness in Multi-Agent Triage

Abstract:Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weakening safety monitoring where correlated failures concentrate. We term this correlated agreement blindness and present ARAT (Arbitrated Reasoning Agents for Alarm Triage), a directed-star system combining an inductive Random Forest (RF) agent, an analogical case-based k-nearest neighbour (k-NN) agent, and a calibrated meta-model to mitigate this effect. On 82,332 holdout samples from the UNSW-NB15 network intrusion detection dataset, 57.2% of errors occur under agreement and 90.6% of dangerous under-predictions evade disagreement-based monitoring even after conservative override; ablation shows that strengthening base learners increases error correlation while reducing disagreement. ARAT reduces under-prediction relative to soft voting from 4.80% to 1.70% via conservative override (-2.6pp) and a safety-flag gate (-0.5pp), demonstrating architectural gains. Cross-dataset validation on clinical readmission supports these indicators, suggesting that diversification improves safety only when it generates productive disagreement rather than convergence. These results indicate that disagreement-triggered escalation can be blind to correlated failure, a risk that may intensify as agentic pipelines deploy increasingly capable, correlated models.

14 pages, 2 figures, 3 tables. Accepted at PAAMS 2026; this is the author's pre-review submitted version

Multiagent Systems (cs.MA); Machine Learning (cs.LG)

I.2.11; I.2.6; K.6.5

arXiv:2607.19899 [cs.MA]

(or arXiv:2607.19899v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2607.19899 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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