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cs.CV2026
Lifting Embodied World Models for Planning and Control
Alex N. Wang, Trevor Darrell, Pavel Izmailov +2
World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are high-dimensional and difficult t…
cs.CV2025
Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes
Joseph Hoche, Andrei Bursuc, David Brellmann +4
Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust uncertainty estimation essential. Recent work on semantic uncertainty estimates r…