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20242026
most citedKimi K2.5: Visual Agentic Intelligence

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

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19 papers · 1 filter

cs.CV2026

OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning

Haocong He, Chenfei Liao, Zichen Wen +13

Multimodal Large Language Models (MLLMs) have demonstrated promising spatial reasoning capabilities, while these abilities remain underexplored in the emerging visual modality of p…

cs.CV2026

EvoStreaming: Your Offline Video Model Is a Natively Streaming Assistant

Zichen Wen, Boxue Yang, Junlong Ke +5

Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness against verbosity. Yet most vide…

cs.CV2026

StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding

Junxi Wang, Te Sun, Jiayi Zhu +6

Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memory overhead, leading to high…

cs.CV2026

Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods

Chenfei Liao, Wensong Wang, Zichen Wen +10

Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly…

cs.CV2026

Flash-Unified: A Training-Free and Task-Aware Acceleration Framework for Native Unified Models

Junlong Ke, Zichen Wen, Boxue Yang +6

Native unified multimodal models, which integrate both generative and understanding capabilities, face substantial computational overhead that hinders their real-world deployment.…

cs.CV2026

Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving

Minhao Xiong, Zichen Wen, Zhuangcheng Gu +9

Vision-Language Models (VLMs) have emerged as a promising paradigm in autonomous driving (AD), providing a unified framework for perception and decision-making. However, their real…