Off-topic for aiqbench
This story was gathered from a source we follow but falls outside AI and quantum coverage, so there is no aiqbench take for it.
Runtime Monitoring of Supermartingale Certificates
arXiv:2610.19701v1 Announce Type: new Abstract: Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathematical model of the world, leaving a sim-to-real gap that can invalidate them for the deployed system. We formulate the runtime monitoring problem for supermartingale certificates, in which a monitor observes a single system trajectory and raises an alarm when there is sufficient statistical evidence that the supermartingale certificate has been violated during execution. We present an algorithm to automatically construct a monitor using betting-based sequential tests that bound the probability of ever raising a false alarm at a user-specified level. Within this betting framework, we formulate strategies that allow the monitor to exploit spatial regularity to improve detection power while preserving this statistical guarantee. We demonstrate the efficacy of our approach on supermartingale certificate benchmarks from the literature.
Abstract:Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathematical model of the world, leaving a sim-to-real gap that can invalidate them for the deployed system. We formulate the runtime monitoring problem for supermartingale certificates, in which a monitor observes a single system trajectory and raises an alarm when there is sufficient statistical evidence that the supermartingale certificate has been violated during execution. We present an algorithm to automatically construct a monitor using betting-based sequential tests that bound the probability of ever raising a false alarm at a user-specified level. Within this betting framework, we formulate strategies that allow the monitor to exploit spatial regularity to improve detection power while preserving this statistical guarantee. We demonstrate the efficacy of our approach on supermartingale certificate benchmarks from the literature.
Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
arXiv:2610.19701 [cs.AI]
(or arXiv:2610.19701v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.19701 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 236 words
Related coverage

Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations
AWS · Sep 11, 2026

Supercharge regulated workloads with Claude Code and Amazon Bedrock
AWS · Oct 5, 2026

Validate GPU Cluster Readiness Before AI Workloads Land
NVIDIA · Sep 23, 2026

Article: Governing AI in the Cloud: A Practical Guide for Architects
InfoQ · Jun 15, 2026