Generator-Independent Runtime Assurance under Partial Observation
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Abstract (arXiv)
Proposal-based controllers---learned policies, language-model planners, and other black-box generators---are increasingly deployed behind runtime verification gates. We ask when the closed-loop safety guarantee decouples from the generator. The prevailing per-candidate certification pattern does not compose: under retry or best-of-k selection a per-candidate false-admission level α can inflate to 1-(1-α)k. Our main theorem shows that simultaneous setwise soundness---certifying a set of admissible proposals containing no nonviable action---is necessary and sufficient for generator-independent admission soundness, the worst case over all generators of executing a nonviable proposal equalling the probability of setwise failure; together with a design-time certificate and a no-bypass rule it is sufficient for contract safety, with violation bound Gamma+sumtvarepsilont+η invariant under arbitrary, even adversarial, replacement of the generator. A second theorem bounds every admission mechanism under partial observation: for a fixed probing and admission policy, if two state hypotheses whose information laws lie within total-variation distance δ require different safe decisions, then abar+β+δ≥1. A sequential risk ledger makes the guarantee implementable with time-uniform confidence tubes, and shows that deterministic admission computations concentrate all statistical risk in state estimation. Simplex-style runtime assurance and control-barrier-function filtering are recovered as degenerate cases.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 5 Sep 2026.
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References · 9
- 2017Safe Reinforcement Learning via Shieldingcited by 449
- 2019Control Barrier Functions: Theory and Applicationscited by 372
- 2018Time-uniform, nonparametric, nonasymptotic confidence sequencescited by 229
- 2021Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systemscited by 27
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 20
- 2023Risk-aware Control for Robots with Non-Gaussian Belief Spacescited by 2
- 2023Bridging the Gap: Applying Assurance Arguments to MIL-HDBK-516C Certification of a Neural Network Control System with ASIF Run Time Assurance Architecturecited by 1
- 2026Safety-critical Control Under Partial Observability: Reach-Avoid POMDP meets Belief Space Controlcited by 1
- 2026Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Roboticscited by 1
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