arXiv:2605.22949v1 Announce Type: new Abstract: Foundation model agents increasingly operate in multi-agent deployments where a coordinator must decide which agent's response to trust. The standard approach weights agents by their self-reported confidence, but recent evidence shows that foundation model confidence is systematically mis-calibrated and, on hard tasks, inversely correlated with accuracy. Design-time calibration methods (temperature scaling, Platt scaling, histogram binning) cannot address this problem because they fit a fixed correction to held-out data and degrade under distribu
As multi-agent foundation model deployments become more sophisticated, the critical need for reliable coordination and trust among agents emerges as a primary challenge.
The ability to accurately calibrate confidence in AI agents directly impacts the reliability, safety, and effectiveness of complex AI systems, especially in high-stakes environments.
This research introduces runtime confidence calibration for multi-agent systems, moving beyond static, design-time methods to address dynamic performance issues and improve coordination.
- · AI developers
- · Organizations deploying multi-agent systems
- · AI safety researchers
- · Systems relying on miscalibrated self-reported confidence
Improved decision-making and reduced errors in multi-agent AI deployments.
Accelerated adoption and integration of complex AI agent systems into critical infrastructure and enterprise operations.
Enhanced trust in autonomous AI systems, potentially leading to broader societal acceptance and regulatory frameworks that depend on verifiable AI reliability.
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Read at arXiv cs.LG