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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
AI Agent Papers·2026
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Beneath the Surface: What We Overlook When We Talk About Agent Evolution

Xiaobo Wang, Zimu Wang +14·University of Science and Technology of China, University of California, Berkeley +3

Examines whether iteratively changing an agent harness produces reliable, efficient, transferable gains. Finds that higher scores can hide task-level regressions and added deployment costs, then introduces EvoAudit to revise the evolution process.

#paper#research#ai-agent#evaluation#benchmarks
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
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