🤖 AI Summary
This work addresses the challenges faced by monolithic temporal foundation models in safety-critical cyber-physical systems, where catastrophic forgetting, oversmoothing of high-frequency fault signals, and lack of verifiability compromise lifecycle-wide state integrity. To overcome these limitations, the authors propose a modular sovereignty paradigm comprising a frozen library of condition-specific expert models, coupled with an uncertainty-aware hierarchical fusion mechanism (HYDRA) that rigorously disentangles aleatoric and epistemic uncertainties. This approach enables module-level auditability and validity guarantees, effectively resolving the plasticity–stability dilemma. By doing so, it provides a certifiable pathway aligned with functional safety standards such as ISO 26262, ensuring high robustness and state integrity under non-stationary operating conditions.
📝 Abstract
The machine learning community has achieved remarkable success with universal foundation models for time-series and physical dynamics, largely overcoming earlier approximation barriers in smooth or slowly varying regimes through scale and specialized architectures. However, deploying these monolithic models in safety-critical Cyber-Physical Systems (CPS), governed by non-stationary lifecycle dynamics and strict reliability requirements, reveals persistent challenges. Recent evidence shows that fine-tuning time-series foundation models induces catastrophic forgetting, degrading performance on prior regimes. Standard models continue to exhibit residual spectral bias, smoothing high-frequency discontinuities characteristic of incipient faults, while their opacity hinders formal verification and traceability demanded by safety standards (e.g., ISO 26262, IEC 61508). This position paper argues that the plasticity-stability paradox cannot be fully resolved by global parameter updates (whether via offline fine-tuning or online adaptation). Instead, we advocate a Modular Sovereignty paradigm: a library of compact, frozen regime-specific specialists combined via uncertainty-aware blending, which we term"HYDRA"(Hierarchical uncertaintY-aware Dynamics for Rapidly-Adapting systems). This paradigm ensures regime-conditional validity, rigorous disentanglement of aleatoric and epistemic uncertainties, and modular auditability, offering a certifiable path for robust state integrity across the CPS lifecycle.