Key result
Monitor and recover strategies offer a practical alternative to detect and abstain methodologies.
Why the study?
Maintaining reliability in learning-enabled cyber-physical systems under distribution shift poses a challenge, and existing detect-and-abstain methods have limited use in real-world applications.
Proposes a new paradigm for handling distribution shifts in cyber-physical systems using monitoring and recovery rather than detection and abstention.
May support safer AI integration in cardiac cyber-physical systems; hypothesis-generating and requires prospective validation before clinical use.
With the known vulnerability of neural networks to distribution shift, maintaining reliability in learning-enabled cyber-physical systems poses a salient challenge. In response, many existing methods adopt a detect and abstain methodology, aiming to detect distribution shift at inference time so that the learning-enabled component can abstain from decision-making. This approach, however, has limited use in real-world applications. We instead propose a monitor and recover paradigm as a promising direction for future research. This philosophy emphasizes 1) robust safety monitoring instead of distribution shift detection and 2) distribution shift recovery instead of abstention. We discuss two examples from our recent work.
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Lin et al. (2025) studied Distribution shift in learning-enabled cyber-physical systems. Monitor and recover paradigm vs. Detect and abstain methodology was evaluated. A monitor and recover paradigm, emphasizing robust safety monitoring and distribution shift recovery, is proposed as a more practical alternative to the detect and abstain methodology.
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