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Cody H. Fleming

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Preprint Sep 2026

A Mean-Field Approach for Safe Routing of Multi-Destination Urban Air Mobility Networks

It is demonstrated that under specific underloaded conditions, a system initialized without queues will remain queue-free indefinitely, which allows us to transform a complex, infinite-dimensional continuous-time optimal control problem into a tractable, finite-dimensional algebraic optimization.

Nameer Fawwaz Ahmed, Cody H. Fleming, Yasser Shoukry · 0 citations
#machine learning Preprint Oct 2026

It Is Not Seeing the Hazard: A Frozen Vision-Language Safety Score Measures Its Caption Bank

Frozen vision-language models increasingly provide safety signals for reinforcement learning. Their use assumes that similarity to language describing danger indicates the hazard itself. Yet policy return and collision rate cannot reveal whether a score detects hazards or responds to correlated features of the scene. V...

Samuel T. K. Tetteh, Cody H. Fleming · 0 citations
#machine learning Preprint Sep 2026

Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation

VLM-Safe-RL is proposed, a framework that integrates frozen CLIP signals into PPO-Lagrangian through reward shaping and an augmented multiplier update, and shows a conditional reduction in observed catastrophe rate without evidence of collision anticipation.

Samuel T. K. Tetteh, Cody H. Fleming · 1 citation
#machine learning Preprint Sep 2026

Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks

A plant-property lower bound is proved on the minimum jam count required for an immediate hold-last medium-access-control adversary to force a crash against a self-triggered controller (STC) satisfying a Lyapunov contract, and a certificate-level analog of the consecutive-grouping optimality of prior count-budget DoS s...

Adam Haroon, Erick J. Rodríguez-Seda, Tristan K. Schuler et al. · 0 citations
#machine learning Preprint Sep 2026

Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning

This workCertified safety curation answers with a filter-then-clone pipeline: a state-only value trained from segment comparisons scores whole trajectories, Learn-then-Test calibration certifies a selection threshold under a distribution-free bound on the unsafe fraction of the selection, and behavior cloning follows.

Adam Haroon, Cody H. Fleming · 0 citations

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