27 papers
EventDrive: Event Cameras for Vision-Language Driving Intelligence
Dongyue Lu, Rong Li, Ao Liang +6
Event cameras sense the world through asynchronous brightness changes with microsecond latency and high dynamic range, offering motion fidelity far beyond frame-based sensors and c…
Why Ask One When You Can Ask ? Learning-to-Defer to the Top- Experts
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
Existing Learning-to-Defer (L2D) frameworks are limited to single-expert deferral, forcing each query to rely on only one expert and preventing the use of collective expertise. We…
Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
Yannis Montreuil, Shu Heng Yeo, Axel Carlier +2
Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection, particularly in extractive question answering. This challenge is magnified…
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
Ao Liang, Lingdong Kong, Tianyi Yan +19
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…
Online Learning-to-Defer with Varying Experts
Dang Hoang Duy, Yannis Montreuil, Maxime Meyer +3
Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deploym…
Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
A learning-to-defer (L2D) system decides, for each input, whether to predict on its own or to hand it to one of several available experts. The very well established recipe trains c…