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Machine Learning Foundation Papers·2026
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SLA: Scaling Spatial-Language-Action Model via Continual Pre-training

Fuhao Li, Wenxuan Song +8·The Hong Kong University of Science and Technology (Guangzhou), OLA Dimensions +2

Integrates 3D spatial supervision into continual pre-training for robot vision-language-action policies, helping spatial understanding transfer to downstream tasks. Across eight real-world deployment conditions, it improves average success over π0.5 by 24 percentage points.

#paper#robotics#embodied-ai#manipulation#foundation-model+2
Computer Vision Papers·2026
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VFlash: Fast, Accurate Visual Planning via Flash Primitive Decoding

Yu-Cheng Chou, Shihao Wang +17·NVIDIA Research

Predicts structured geometry such as bounding boxes and robot trajectories in parallel, rather than generating coordinates one at a time. It reduces errors on evaluated robot-planning tasks while cutting decoding time.

#paper#robotics#embodied-ai#vision#physical-reasoning+1
Computer Vision Papers·2026
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WorldFoundry: Towards Unified Infrastructure for World Intelligence

Juanxi Tian, Jiadong Hou +14·OpenEnvision

A realistic video is not proof of a modeled world. The paper defines world models through persistent state, intervention-conditioned dynamics, and feedback, then presents infrastructure for reproducible inference and evaluation across video, 3D/4D, and embodied systems.

#paper#world-model#ai-infra#evaluation#benchmark+1
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