arXiv:2605.20515v1 Announce Type: new Abstract: Modern artificial intelligence systems require calibrated uncertainty estimates that remain reliable in sequential and non-stationary environments. Online conformal prediction (OCP) addresses this challenge through adaptively updated prediction sets that provide deterministic long-run miscoverage guarantees. These guarantees, however, hinge on the assumption of perfect feedback about the coverage of past prediction sets. In practice, the observed miscoverage indicator may be corrupted by noise, communication failures, or adversarial manipulation,
The increasing deployment of AI systems in real-world, sequential environments necessitates robust uncertainty quantification methods, and the inherent unreliability of real-world feedback loops is becoming a known challenge.
Reliable uncertainty estimates are crucial for AI systems in critical applications, and addressing corrupted feedback ensures the continued trustworthiness and safety of advanced AI deployments.
This research contributes to making Online Conformal Prediction (OCP) more resilient to real-world imperfections, improving its applicability in practical, non-stationary AI systems.
- · AI system developers
- · Industries relying on sequential AI (finance, healthcare, automation)
- · Researchers in AI safety and robustness
- · AI systems lacking robust uncertainty handling
- · Methods relying on perfect feedback assumptions
Online Conformal Prediction methods become more robust and deployable in complex, real-world scenarios.
Increased adoption of OCP in safety-critical AI applications, leading to more reliable AI decision-making.
This resilience could accelerate the development and deployment of more autonomous AI agents in environments with imperfect information.
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Read at arXiv cs.LG