Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation
The paper presents a method for uncertainty quantification in continuous AI agent evaluation. It introduces split conformal prediction and adaptive conformal inference to ensure distribution-free coverage for quality scores. The authors validate their approach through simulations and real-time data, demonstrating effective calibration and predictive capabilities.
- ▪The method achieves calibration error below 0.02 across all nominal levels at the 24-hour horizon.
- ▪Conditional coverage for 50 agents is concentrated around the nominal level, with a mean of 80.4%.
- ▪Cross-source sentiment divergence is shown to predict ranking instability with a correlation of r=0.64.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19779 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.19779 (cs) [Submitted on 19 May 2026] Title:Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation Authors:Yuxuan Gao, Megan Wang, Yi Ling Yu View a PDF of the paper titled Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation, by Yuxuan Gao and 2 other authors View PDF HTML (experimental) Abstract:We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 24h horizon, while ACI correctly widens intervals by 35% following agent releases then reconverges.
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