6 citations · 8 across the 6 of their papers we have counts for
6 papers
MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions
Yuxuan Liu, Hongda Sun, Wei Liu +3
Mobile phone agents can assist people in automating daily tasks on their phones, which have emerged as a pivotal research spotlight. However, existing procedure-oriented agents str…
ReachAgent: Enhancing Mobile Agent via Page Reaching and Operation
Qinzhuo Wu, Wei Liu, Jian Luan +1
Recently, mobile AI agents have gained increasing attention. Given a task, mobile AI agents can interact with mobile devices in multiple steps and finally form a GUI flow that solv…
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models
Xiaolin Hu, Xiang Cheng, Peiyu Liu +4
Low-rank adaptation (LoRA) reduces the computational and memory demands of fine-tuning large language models (LLMs) by approximating updates with low-rank matrices. However, low-ra…
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
Qinzhuo Wu, Weikai Xu, Wei Liu +6
Recently, mobile AI agents based on VLMs have been gaining increasing attention. These works typically utilize VLM as a foundation, fine-tuning it with instruction-based mobile dat…
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
Qibin Wang, Xiaolin Hu, Weikai Xu +3
Low-rank adaptation (LoRA) and its variants have recently gained much interest due to their ability to avoid excessive inference costs. However, LoRA still encounters the following…
Mixture of Diverse Size Experts
Manxi Sun, Wei Liu, Jian Luan +2
The Sparsely-Activated Mixture-of-Experts (MoE) has gained increasing popularity for scaling up large language models (LLMs) without exploding computational costs. Despite its succ…