arxiv
PublishedJuly 10, 2026 at 4:00 AM
—neutral
When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
Publisher summary· verbatim
arXiv:2607.08065v1 Announce Type: new Abstract: LLM-as-judge (Zheng et al., 2023) is increasingly the default for evaluating AI systems in enterprise pipelines, often scaled to ensembles (Verga et al., 2024) or "mixture-of-experts" (Shazeer et al., 2017) panels of judges. These systems share a key a
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAccelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap20harxivTIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation20harxivAdvanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance20harxivDemocratizing Agent Deployment Safety: A Structural Monitoring Approach20hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗