MASCAL trains multi-agent systems to pass calibrated confidence
Upstream agents hand off a correctness probability so downstream agents can trust, verify, or recheck web and reasoning answers.
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Stories tagged #confidence-calibration
MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
MARGIN calibrates multi-model confidence at runtime and lifts answer-selection accuracy by up to 14 points.
Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents
A decomposed perception-layout-validation confidence score with conformal risk control lets VLMs auto-approve 49-72% of financial document fields at target error.