arXiv:2507.20975v5 Announce Type: replace-cross Abstract: Operator models are regression algorithms between Banach spaces of functions. They have become an increasingly critical tool for spatiotemporal forecasting and physics emulation, especially in high-stakes scenarios where robust, calibrated uncertainty quantification is required. We introduce Local Sliced Conformal Inference (LSCI), a distribution-free framework for generating function-valued, locally adaptive prediction sets for operator models. We prove finite-sample validity and derive a data-dependent upper bound on the coverage gap
The increasing deployment of AI in high-stakes operational environments demands robust uncertainty quantification, which current methods often lack, making this development timely.
This research provides a rigorous method for quantifying uncertainty in complex AI models, which is critical for their safe and reliable deployment in sensitive applications like spatiotemporal forecasting and physics emulation.
Operator models can now be deployed with theoretically guaranteed, locally adaptive prediction sets, enhancing trustworthiness and applicability in 'high-stakes' scenarios where previous uncertainty metrics were insufficient.
- · AI model developers
- · Spatiotemporal forecasting sector
- · Physics emulation industry
- · Regulatory bodies
- · Developers of less robust uncertainty quantification methods
- · Sectors reliant on uncalibrated AI predictions
Increased adoption of operator models in critical infrastructure and scientific domains due to enhanced reliability.
New regulatory frameworks may emerge, mandating conformal inference or similar rigorous uncertainty quantification for AI deployments in highly sensitive areas.
Accelerated development of AI systems capable of operating autonomously in complex, real-world scenarios with minimized risk, potentially collapsing the need for human oversight in certain operational loops.
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Read at arXiv cs.LG