MARGIN Calibrates Multi-Agent Confidence Without Model Retraining
October 7, 2026
MARGIN performs runtime confidence calibration for heterogeneous foundation models by learning model-specific corrections from observed outcomes. It uses incremental normalization and confidence bands to weight candidate answers in collective decision-making for tasks like coding and mathematics.
HOW THIS AFFECTS YOU
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builderYou can improve multi-model orchestration reliability by correcting inconsistent self-reported confidence scores at runtime.
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founderThis enables more accurate multi-agent systems without the high cost of fine-tuning every individual model.