6 papers
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…
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…
When Do LLM Agents Treat Surface Noise Differently from Semantic Noise? A 68-Cell Measurement Study with a Held-Out Trace-Level Validation
Liyun Zhang, Jiayi Guo
We document an empirical phenomenon in chain-of-thought and ReAct agents driven by ten large language models from seven architecture families: meaning-bearing perturbations (e.g.,…
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…