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cs.CV2026

Prisma-World: Camera-Controllable Multi-Agent Video World Model

Huiqiang Sun, Zhan Peng, Size Wu +9

Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single observer. Extending such models t…

cs.CV2026

AdaCodec: A Predictive Visual Code for Video MLLMs

Haowen Hou, Zhen Huang, Zheming Liang +8

Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models (video MLLMs) usually encode…

cs.CV2026

UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing

Dianyi Wang, Chaofan Ma, Feng Han +8

Unified multimodal models often struggle with complex synthesis tasks that demand deep reasoning, and typically treat text-to-image generation and image editing as isolated capabil…

cs.CV2026

DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing

Dianyi Wang, Ruihang Li, Feng Han +17

Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment foot…

cs.CV2026

MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models

Dianyi Wang, Siyuan Wang, Zejun Li +6

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across multi-modal tasks by scaling model size and training data. However, these dense LVLMs incur sig…

cs.CV2026

Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better

Dianyi Wang, Wei Song, Yikun Wang +4

Typical large vision-language models (LVLMs) apply autoregressive supervision solely to textual sequences, without fully incorporating the visual modality into the learning process…