arXiv:2605.30653v1 Announce Type: new Abstract: Multi-agent LLM systems often treat agreement as evidence: when many agents in a panel give the same answer, that answer is assumed to be more reliable. We show that this assumption can fail after agents communicate. Communication can induce correlated failures and false consensus, so the same vote share may reflect reliable agreement in one topology but over-confidence in another. We propose CAGE-CAL, a counterfactual agent-graph calibration framework for multi-agent LLMs. For each query, CAGE-CAL compares an observed post-communication agent gr
The rapid development and deployment of multi-agent LLM systems necessitates robust calibration techniques to ensure their reliability and prevent correlated failures, moving beyond simple agreement as a metric.
This research directly addresses a critical weakness in current multi-agent LLM systems, which are increasingly being proposed for complex, high-stakes decision-making and automation, impacting their trustworthiness and effectiveness.
The understanding of 'consensus' in multi-agent LLMs shifts from a naive count of identical answers to a nuanced assessment considering communication topology and potential for false consensus, leading to more reliable AI agent systems.
- · AI safety researchers
- · Developers of multi-agent LLM systems
- · Industries deploying AI agents for critical tasks
- · Systems relying on naive multi-agent agreement
- · Applications vulnerable to deceptive AI consensus
Improved reliability and trustworthiness of multi-agent LLM applications in various sectors.
Accelerated adoption of AI agents in scenarios requiring high levels of assurance and auditability.
The development of new AI governance and regulatory frameworks that incorporate sophisticated calibration and trust mechanisms for multi-agent systems.
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