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20232026
most citedReinforced UI Instruction Grounding: Towards a Generic UI Task Automation API

2 citations · 3 across the 22 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding

Kerui Chen, Jinglu Wang, Xiaoyi Zhang +1

Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos involve dense occlusion, rapid moti…

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…

eess.IV2026

Generative Video Compression with Adaptive Score Distillation

Naifu Xue, Zhaoyang Jia, Haosen Li +7

Diffusion models provide strong generative capabilities for video compression at ultra-low bitrates. Existing diffusion-based video codecs adapt base models originally developed fo…

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.AI2026

Multi-agent Framework for Time-Sensitive Complementary Collaboration in Minecraft

Juheon Yi, Jinglu Wang, Xiaoyi Zhang +1

We present TickingCollabBench, a Minecraft-based multi-agent benchmark for a novel class of time-sensitive complementary collaboration tasks. Our benchmark reflects four core chara…

cs.CV2026

From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations

Yuchen Guan, Xiao Li, Zongyu Guo +4

We propose a new paradigm for long video understanding by treating a long video as a Neural Knowledge Representation (NKR). NKR represents video contents neither as a stream of tok…

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

Generative Video Compression with One-Dimensional Latent Representation

Zihan Zheng, Zhaoyang Jia, Naifu Xue +7

Recent advancements in generative video codec (GVC) typically encode video into a 2D latent grid and employ high-capacity generative decoders for reconstruction. However, this para…

cs.LG2026

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

Zongyu Guo, Jiajun He, Zhaoyang Jia +6

Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixels, latents, or tokens) remain external…

cs.AI2026

From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation

Zezhou Wang, Ziyun Zhang, Xiaoyi Zhang +2

Vision-language models are increasingly deployed as computer-use agents (CUAs) that operate desktops and browsers. Top-performing CUAs are framework-based systems that decompose pl…

cs.CL2026

InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training

Ziyun Zhang, Zezhou Wang, Xiaoyi Zhang +4

GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants. However, training such agents is hindered by the…