PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
Latent action pretraining learns representations of visual change from pairs of observations, but existing methods ty...
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Latent action pretraining learns representations of visual change from pairs of observations, but existing methods ty...
Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable ...
While recent LLM-based terminal agents have demonstrated promising capabilities, the scarcity of high-quality, execut...
Recently, end-to-end OCR models, exemplified by DeepSeek OCR, have once again thrust OCR into the spotlight. A widely...
While large and diverse datasets have driven recent advances in large models, identifying the optimal data mixture fo...
Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack ...
Vision Transformers (ViT) dominate computer vision. However, their reliance on rigid patch projectors hinders transfe...
Terminal-using agents have quickly become the most popular downstream application of language models (LMs). Despite t...
Massive unstructured multimodal streams suffer from high "data entropy," impeding both efficient human knowledge acqu...
With the rapid spread of retrieval-augmented generation and semantic search, choosing the right embedding and retriev...
Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the ...
Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underex...