1 citations · 1 across the 12 of their papers we have counts for
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Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving
Shiva Sreeram, Erfan Aasi, Tsun-Hsuan Wang +2
Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture temporal dynamics in safety-critical…
Kernel Reboot: Breaking the Boundaries of Neural Tangent Kernels for Neural Fields
Amir Mallak, Alaa Maalouf, Lior Wolf +2
Neural fields (NFs) map continuous coordinates to signals such as color or density, but fast high-quality reconstruction from sparse observations remains difficult. Classical Neura…
Supportive Token Revealing for Fast Diffusion Language Model Decoding
Giries Abu Ayoub, Mario Barbara, Lluís Pastor-Pérez +4
Discrete diffusion language models can generate text efficiently by updating multiple masked positions in parallel, but this parallelism introduces a quality-latency trade-off. Agg…
Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching
Alaa Khamis, Alaa Maalouf
Test-time finetuning (TTFT) is a rapidly evolving paradigm that adapts a language model to each prompt by retrieving related sequences, updating the model on them, and then evaluat…
Autonomous Sea Turtle Robot for Marine Fieldwork
Zach J. Patterson, Emily Sologuren, Levi Cai +4
Autonomous robots can transform how we observe marine ecosystems, but close-range operation in reefs and other cluttered habitats remains difficult. Vehicles must maneuver safely n…
Robustness Is a Function, Not a Number: A Factorized Comprehensive Study of OOD Robustness in Vision-Based Driving
Amir Mallak, Alaa Maalouf
Out of distribution (OOD) robustness in autonomous driving is often reduced to a single number, hiding what breaks a policy. We decompose environments along five axes: scene (rural…
See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection
Amir Mallak, Erfan Aasi, Shiva Sreeram +3
Recent advances in end-to-end autonomous driving show that policies trained on patch-aligned features extracted from foundation models generalize better to Out-of-Distribution (OOD…