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cs.CV2026

Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model

Senqiao Yang, Kaichen Zhang, Zhaoyang Jia +20

Standard vision-language models (VLMs) suffer from Moravec's paradox: they excel at complex offline visual reasoning but struggle with simple streaming perception tasks and process…

cs.CV2026

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

Xinjie Zhang, Peng Zhang, Shicheng Zheng +21

Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to…

cs.CV2026

Ultra-Fast Neural Video Compression

Jiahao Li, Wenxuan Xie, Zhaoyang Jia +4

While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to real-world deployment. This…

cs.CV2026

An Efficient Streaming Video Understanding Framework with Agentic Control

Jinming Liu, Jianguo Huang, Zhaoyang Jia +7

Streaming video requires handling dynamic information density under strict latency budgets. Yet, existing methods typically employ static strategies, such as fixed memory compressi…

cs.CV2026

CoD-Lite: Real-Time Diffusion-Based Generative Image Compression

Zhaoyang Jia, Naifu Xue, Zihan Zheng +7

Recent advanced diffusion methods typically derive strong generative priors by scaling diffusion transformers. However, scaling fails to generalize when adapted for real-time compr…

cs.CV2026

Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding

Jialuo Li, Bin Li, Jiahao Li +1

The application of Large Multimodal Models (LMMs) to long-form video understanding is constrained by limited context lengths and the computationally prohibitive cost of processing…