The Very Big Video Reasoning (VBVR) Dataset is introduced, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets.
Maijunxian Wang, Rui-Si Wang, Ju-Yi Lin et al.· arXiv.org· 28 citations· ⚡5
Native unified modelling is position as a promising path towards systems that perceive, reason and create within a fully end-to-end framework through SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture.
Hai-Wen Diao, Jia-Hao Wang, Chen-Jing Ding et al.· 3 citations
This work introduces VBVR-Pro, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable, and identifies recurring failure modes of the prevalent VLM-as-a-judge paradigm.
Junhua Xu, Rui-Si Wang, Fanyi Pu et al.· 5 citations
Apple-PI is introduced, the first benchmark that anchors video-model evaluation explicitly in physical laws, and is positioned as a diagnostic foundation for guiding future video models toward world models with law-grounded physical intelligence.
Runmao Yao, Kairui Hu, Yukang Cao et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
