11 papers
HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents
Hy Vision Team, Huawen Shen, Zhengyang Tang +20
As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embod…
PhoneBuddy: Training Open Models for Agentic Phone Use
Zhengyang Tang, Xin Lai, Pengyuan Lyu +23
Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable phone use remains difficult because the environment that matter…
PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions
Chenxin Li, Zhengyao Fang, Zhengyang Tang +18
Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature sti…
Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents
Zhengyang Tang, Yi Zhang, Chenxin Li +18
When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent re…
PhoneWorld: From Real-App Trajectories to Dynamic and Verifiable Environments for Phone-Use Agents
Yuxuan Liu, Xin Lai, Junyi Li +21
Real applications provide the training setting closest to phone-agent deployment, but are difficult to reset, scale safely, and verify programmatically. Static screenshots and inte…
Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance
Zexi Jia, Pengcheng Luo, Zhengyao Fang +2
Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, an…
Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity
Zhengyao Fang, Zexi Jia, Yijia Zhong +5
Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authentic to real-world photography remains challe…
Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
Zhengyao Fang, Pengyuan Lyu, Chengquan Zhang +3
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens. While prior work shows that a large fraction of visual…
SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding
Zheng Fang, Ziwei Niu, Ziyue Wang +5
Surgical scene Multi-Task Federated Learning (MTFL) is essential for robot-assisted minimally invasive surgery (RAS) but remains underexplored in surgical video understanding due t…
Recognition-Synergistic Scene Text Editing
Zhengyao Fang, Pengyuan Lyu, Jingjing Wu +4
Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and conte…
WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting
Jingjing Wu, Zhengyao Fang, Pengyuan Lyu +4
Transcription-only Supervised Text Spotting aims to learn text spotters relying only on transcriptions but no text boundaries for supervision, thus eliminating expensive boundary a…