mlsanityData to 7 Oct 2026

Position: Certifiable State Integrity Should Be Built from Local Validity, Not Global Scale

arXiv 2601.21249 ↗First posted 29 Jan 2026cs.AI cs.LGcited by 1

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Abstract (arXiv)

Breakthroughs in language and vision have motivated increasingly general foundation models for time series and physical dynamics, where evidence is promising but less mature. In safety-critical Cyber-Physical Systems (CPS), globally parameterized dynamics models must remain valid through regime shifts, degradation, and lifecycle updates while supporting traceable assurance evidence. We argue that shared global parameters couple adaptation, frequency-sensitive fault representation, and verification evidence in ways that obstruct retained regime knowledge and bounded analysis. For systems whose operating conditions can be covered by a finite set of locally valid models, assurance evidence should be organized around those bounded models and their explicit composition, not around a single globally adapted model; increasing scale cannot supply explicit validity boundaries. HYDRA is a reference architecture for this position: frozen local specialists, runtime arbitration, and explicit coverage-gap monitoring. It illustrates how modular auditability and formal analysis of bounded components support Certifiable State Integrity, while whole-system certification, switching guarantees, and empirical validation remain separate obligations.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 29 Jan 2026.

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18 of 43 references.

References · 18

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  2. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
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  5. 2020Gradient Surgery for Multi-Task Learningcited by 1,210
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  10. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 234
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  12. 2024A Survey for Foundation Models in Autonomous Drivingcited by 51
6 more
  1. 2025Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptationcited by 32
  2. 2025Mixture of Experts Made Intrinsically Interpretablecited by 22
  3. 2023GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEscited by 20
  4. 2022State estimation with the Interacting Multiple Model (IMM) methodcited by 2
  5. 2025S2GPT-PINNs: Sparse and Small models for PDEscited by 2
  6. 2025Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecastingcited by 1

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