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cs.CV2026

UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action

Yuhao Yang, Zhen Yang, Zi-Yi Dou +10

Computer-use agents face a fundamental limitation. They rely exclusively on primitive GUI actions (click, type, scroll), creating brittle execution chains prone to cascading failur…

cs.CV2025

Ferret-UI Lite: Lessons from Building Small On-Device GUI Agents

Zhen Yang, Zi-Yi Dou, Di Feng +13

Developing autonomous agents that effectively interact with Graphic User Interfaces (GUIs) remains a challenging open problem, especially for small on-device models. In this paper,…

cs.CV2025

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer

Yanghao Li, Rui Qian, Bowen Pan +24

Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from…

cs.CV2025

MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models

Wenbo Hu, Jia-Chen Gu, Zi-Yi Dou +4

Existing multimodal retrieval benchmarks primarily focus on evaluating whether models can retrieve and utilize external textual knowledge for question answering. However, there are…

cs.CV2024

Unlocking Exocentric Video-Language Data for Egocentric Video Representation Learning

Zi-Yi Dou, Xitong Yang, Tushar Nagarajan +5

We present EMBED (Egocentric Models Built with Exocentric Data), a method designed to transform exocentric video-language data for egocentric video representation learning. Large-s…

cs.CV2024

Matryoshka Query Transformer for Large Vision-Language Models

Wenbo Hu, Zi-Yi Dou, Liunian Harold Li +3

Large Vision-Language Models (LVLMs) typically encode an image into a fixed number of visual tokens (e.g., 576) and process these tokens with a language model. Despite their strong…