MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
When a coordinator compares answers from heterogeneous foundation models, self-reported confidence may have different meanings across responders and changing workloads. This paper presents MARGIN (Multi-Agent Runtime Grading via Incremental Normalisation), a runtime calibration method that learns model-specific confidence corrections from observed answer outcomes without retraining the models or requiring a held-out calibration set. MARGIN tracks recent accuracy and stated confidence within confidence bands, uses their ratio to correct reported confidence, and blends sparse-band corrections toward a model-level estimate. The corrected scores weight candidate answers in a collective decisi...
| Authors | Joss Armstrong |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 8 October 2026 |
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