Hugging Face · 논문
Hugging Face 일일 논문 목록에 오른 논문입니다 (arXiv 2610.19247).
Transformers are intuitively considered to be efficient at copying from their inputs. We actually show that the ability of LLMs to copy in context can be improved through explicit modeling.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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