arXiv:2606.03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits. Recent work has explored e-value formulations for conformal inference, yet a direct connection between p- and e-value formulations in CP has been missing, especially regarding their statistical efficiency. We first identify limitations of classical p-to-e calibrators in the CP setting, showing that they are not set-preserving a
This paper addresses a fundamental methodological challenge in AI development by seeking more flexible and efficient ways to quantify uncertainty, a critical need as AI systems become more complex and integrated.
Improved methods for uncertainty quantification directly enhance AI model reliability and trustworthiness, which is essential for deploying AI in sensitive or impactful applications.
The development of a direct connection between p-values and e-values in conformal prediction offers new tools for combining evidence and assessing statistical efficiency in AI models, potentially leading to more robust and adaptable systems.
- · AI developers
- · Machine learning researchers
- · Industries relying on AI for decision-making
- · AI systems with poor or opaque uncertainty quantification
More accurate and flexible uncertainty estimates for AI models become available, improving predictive robustness.
This methodological advancement could facilitate the adoption of AI in high-stakes domains where verifiable confidence levels are paramount.
As AI models become more trustworthy due to better uncertainty quantification, their integration into automated decision-making and agentic systems could accelerate.
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Read at arXiv cs.LG