Hugging Face · 논문
Hugging Face 일일 논문 목록에 오른 논문입니다 (arXiv 2610.22174).
Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulated context.원문 피드·공개 API 가 내보낸 소개 문구의 앞부분입니다.
Long-context training of large language and multimodal models couples structured attention masks (document, prefix-LM, share-question, sliding-window, block-sparse) with multi-node context parallelism, making the mask a dominant bottleneck.
We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. We show that a pretrained video generator can be repurposed into a feed-forward 4D perceiver without iterative denoising.
Multimodal deep search is moving from short queries to long-horizon investigations that continually acquire evidence.
Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property restrictions, and difficulty verifying open-ended findings…
Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling.
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