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cs.CV2026

RepWAM: World Action Modeling with Representation Visual-Action Tokenizers

Junke Wang, Qihang Zhang, Shuai Yang +5

This work presents RepWAM, a representation-centric world action model (WAM) built on representation visual-action tokenizers. Existing WAMs typically inherit reconstruction-orient…

cs.CV2026

ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations

Junke Wang, Xiao Wang, Jiacheng Pan +16

This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…

cs.CV2026

IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder

Yitong Chen, Zijie Diao, Junke Wang +5

Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spac…

cs.CV2026

OmniGen-AR: AutoRegressive Any-to-Image Generation

Junke Wang, Xun Wang, Qiushan Guo +4

Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, ex…

cs.CV2026

DisCo: World Models with Discrete Camera Motion Control

Hongrui Huang, Junke Wang, Quanhao Li +2

Controllable video world models target interactive world exploration, where models must faithfully execute explicit action commands while preserving visual quality and temporal coh…

cs.CV2026

FluxMem: Adaptive Hierarchical Memory for Streaming Video Understanding

Yiweng Xie, Bo He, Junke Wang +3

This paper presents FluxMem, a training-free framework for efficient streaming video understanding. FluxMem adaptively compresses redundant visual memory through a hierarchical, tw…