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cs.LG2026
Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation
Samuel Tetteh, Cody Fleming
Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision costs typically appear only…
cs.LG2026
Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
Samuel Tetteh, Udip Shrestha, Joshua R. Waite +1
Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, ins…
cs.LG2026
Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models
Samuel Tetteh, Cody Fleming
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange mu…