1 citations · 1 across the 10 of their papers we have counts for
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GrndCtrl: Grounding World Models via Self-Supervised Reward Alignment
Haoyang He, Jay Patrikar, Dong-Ki Kim +5
Recent advances in video world modeling have enabled large-scale generative models to simulate embodied environments with high visual fidelity, providing strong priors for predicti…
Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers
Yutian Chen, Yuheng Qiu, Ruogu Li +4
We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a l…
The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving
Jay Patrikar, Apoorva Sharma, Sushant Veer +3
Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisel…
AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Rebecca Martin, Jay Patrikar, Sebastian Scherer
Specialized machine learning models, regardless of architecture and training, are susceptible to failures in deployment. With their increasing use in high risk situations, the abil…
Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid
Parv Kapoor, Ian Higgins, Nikhil Keetha +9
Assured safe-separation is essential for achieving seamless high-density operation of airborne vehicles in a shared airspace. To equip resource-constrained aerial systems with this…