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This paper identifies a phenomenon called correlated agreement blindness in multi-agent systems, where improved base learners converge and inadvertently weaken safety monitoring. The authors introduce ARAT, a directed-star architecture that combines a Random Forest agent, a k-NN agent, and a calibrated meta-model to effectively mitigate this issue. Experimental results reveal that ARAT significantly reduces under-prediction rates from 4.80% to 1.70%, highlighting the importance of fostering productive disagreement among agents to enhance safety in decision-making processes.
Disagreement-triggered escalation can blind multi-agent systems to critical failures, but ARAT shows how to turn this risk into a safety advantage.
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.
