mlsanityData to 14 Oct 2026

Latent Safety Filters: When a Lossy Encoder Admits a Transferable Certificate

arXiv 2610.04297 ↗First posted 3 Oct 2026eess.SY cs.LG cs.ROcited by 0

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Abstract (arXiv)

Latent safety filters certify safety on a learned low-dimensional representation of the state, enabling constraints that resist analytic description. Because the encoder is lossy, a filter can report safe while the physical state is unsafe, with no detectable model error. Existing transfer conditions leave the effect of discarded safety information implicit. We ask when a lossy encoder admits a safety certificate that transfers to the physical system, and show the answer is governed by the detectability of the discarded safety-relevant dynamics. We construct a system whose latent model is exact and whose latent signals always report safe, while the physical state becomes arbitrarily unsafe. For this system no certificate exists and no monitor downstream of the encoder can detect the failure. When the discarded dynamics contract, a latent barrier certifies true safety up to two explicit margins, one for the latent-model error and one for the variation of safety across states the encoder cannot distinguish. In the linear case and under boundedness and non-degeneracy conditions, every calibrated barrier transfers with a finite margin when the safety-relevant subspace is detectable, and none does otherwise. On learned cartpole encoders, the model error does not indicate for which representations the estimated bound is non-vacuous, while the second margin does.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 3 Oct 2026.

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22 of 27 references.

References · 22

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  2. 2019Control Barrier Functions: Theory and Applicationscited by 372
  3. 2025Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysiscited by 45
  4. 2020Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functionscited by 33
  5. 2025Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failurescited by 33
  6. 2022Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theorycited by 19
  7. 2023In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution Statescited by 16
  8. 2018Input-to-State Safety With Control Barrier Functionscited by 15
  9. 2025How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraintscited by 9
  10. 2021Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in Statecited by 8
  11. 2022Data-driven reduced order models using invariant foliations, manifolds and autoencoderscited by 8
  12. 2024Safety-Critical Control for Autonomous Systems: Control Barrier Functions via Reduced-Order Modelscited by 8
10 more
  1. 2025AnySafe: Adapting Latent Safety Filters at Runtime via Safety Constraint Parameterization in the Latent Spacecited by 7
  2. 2025Latent Representations for Control Design with Provable Stability and Safety Guaranteescited by 6
  3. 2025Designing Latent Safety Filters using Pre-Trained Vision Modelscited by 5
  4. 2022Safe and Robust Observer-Controller Synthesis using Control Barrier Functionscited by 3
  5. 2025When are safety filters safe? On minimum phase conditions of control barrier functionscited by 1
  6. 2025How Well Do Latent World Models Understand Partially Observable Safety Constraints?cited by 1
  7. 2025Verification of Unknown Dynamical Systems via Autoencoder Latent Spacecited by 1
  8. 2026On transferring safety certificates across dynamical systemscited by 1
  9. 2026Reduced-order Control and Geometric Structure of Learned Lagrangian Latent Dynamicscited by 1
  10. 2026From Points to Sets: Set-Based Safety Verification in the Latent Spacecited by 1

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