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MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose MARGIN, an online calibration method to normalize and correct incomparable confidence scores across multi-model agent pools.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 9 October 2026
- Collected by OneBench
- 3 Aug 2026, 09:18 UK
Stored source excerpt
arXiv:2605.22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.…
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