82 citations · 83 across the 3 of their papers we have counts for
4 papers
HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?
Tu Trinh, Mohamed Elfeki, Guangze Luo +9
Frontier coding agents solve complex tasks when given complete context but collapse when specifications are incomplete or ambiguous. The bottleneck is not raw capability, but judgm…
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges
Nathan Fulton, Nathan Hunt, Nghia Hoang +1
Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees. Over the past decade, techniques based on forma…
Clinical Intervention Prediction and Understanding using Deep Networks
Harini Suresh, Nathan Hunt, Alistair Johnson +3
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs). This task is complicated by data sources that are noisy, sparse, heterogeneou…