RESEARCHMonitorWATCHLIST
UMAS: System-Level Uncertainty Quantification for Multi-Agent LLM Systems
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduced UMAS, a cost-neutral uncertainty quantification method designed specifically for multi-agent LLM architectures.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 27 October 2026
- Collected by OneBench
- 28 Oct 2026, 02:01 UK
Stored source excerpt
arXiv:2610.33013v1 Announce Type: cross Abstract: Multi-Agent Systems (MAS) are a promising paradigm for improving the reasoning capabilities of large language models, yet they remain prone…
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