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MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination

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Briefings

A new method called MARGIN improves multi-agent coordination by learning model-specific confidence corrections from observed outcomes without retraining models. Evaluated on BigCodeBench, MARGIN reduced expected calibration error in code-generation and question-answering tasks compared to five existing baselines. It increased answer-selection accuracy by 4.3 and 14.0 percentage points on two benchmarks relative to uncalibrated confidence weighting, supporting runtime calibration under changing workloads.

Key points

• MARGIN learns model-specific confidence corrections from observed answer outcomes without retraining models or requiring a held-out calibration set.
• On BigCodeBench, model-mean confidence is negatively related to accuracy, and choosing the more confident responder performs below chance on correct/incorrect response pairs.

Context

This research addresses runtime calibration for multi-agent foundation model coordination, specifically when correctness feedback is available for participating responders. The method tracks recent accuracy and stated confidence within confidence bands to correct reported confidence scores.

Limitations

Evaluation covers code generation, question answering, and mathematics using an 18-model pool and a nine-model subset for distribution-shift experiments. The remaining question-answering comparison is inconclusive, and the paper does not specify the exact date of the preprint release.

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