6 papers
MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration
Runxi Huang, Liyu Zhang, Shengzhong Liu +1
Mobile graphical user interface (GUI) agents enable AI models to autonomously operate smartphones on behalf of users. However, most existing systems focus primarily on optimizing t…
AgentDiff: Meaning-Bearing Rewrites Trigger Deeper Divergence than Presentation Changes in LLM Agents
Liyun Zhang, Jiayi Guo
LLM agents should respond to what an input means, not how it is presented. We show that they do not treat these two kinds of variation equally. AgentDiff measures the difference be…
ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
Arash Akbari, Arman Akbari, Masih Eskandar +11
Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical. Aggressiv…
Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT
Liyu Zhang, Yejia Liu, Kwun Ho Liu +2
A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes in sensor placement or ambient…
Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models
Shengjie Ma, Xuhui Jiang, Chengjin Xu +3
Large Language Models (LLMs) have achieved remarkable success but remain data-inefficient, especially when learning from small, specialized corpora with limited and proprietary dat…
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
Jiayi Guo, Zan Chen, Yingrui Ji +4
Automated Machine Learning (AutoML) has simplified complex ML processes such as data pre-processing, model selection, and hyper-parameter searching. However, traditional AutoML fra…