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cs.CV2026

SenseNova-U1.5: Towards Native Unified Visual Intelligence

Haiwen Diao, Jiahao Wang, Chenjing Ding +62

We launch 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.…

cs.CV2026

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

Junxiang Xu, Ruisi Wang, Fanyi Pu +49

Native visual reasoning treats visual generation as the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be understood or outputs to be r…

cs.CV2026

TransPhy: Visual In-Context Learning for Physically Grounded Image Editing

Siyi Xie, Xuanke Shi, Jinsheng Quan +4

Visual demonstrations provide a natural interface for specifying image transformations that are difficult to describe exhaustively with text. However, existing visual in-context le…

cs.CV2026

SPARGen: Unifying Spatial Perception and Reasoning through Native Multimodal Generation

Jinsheng Quan, Jianhua Li, Siyi Xie +7

Spatial perception and reasoning from visual observations require recovering geometric structure, establishing correspondences, and understanding spatial relations. Existing approa…

cs.CV2026

Vision as Unified Multimodal Generation

Xiaoyang Han, Jianhua Li, Kewang Deng +14

We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal…

cs.CV2025

Scaling Spatial Intelligence with Multimodal Foundation Models

Zhongang Cai, Ruisi Wang, Chenyang Gu +26

Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation m…