4 citations · 6 across the 8 of their papers we have counts for
8 papers
When Multi-Robot Systems Meet Agentic AI:Towards Embodied Collective Intelligence
Yuxuan Yan, Yuanyuan Jia, Qianqian Yang
Embodied AI is increasingly becoming agentic, shifting robots from perception--control pipelines towards closed-loop systems that can retrieve context, deliberate during execution,…
AdaNav: Adaptive Reasoning with Uncertainty for Vision-Language Navigation
Xin Ding, Jianyu Wei, Yifan Yang +10
Vision Language Navigation (VLN) requires agents to follow natural language instructions by grounding them in sequential visual observations over long horizons. Explicit reasoning…
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
Wenqing Zhou, Yuxuan Yan, Qianqian Yang
Retrieval-Augmented Generation (RAG) improves factuality by grounding LLMs in external knowledge, yet conventional centralized RAG requires aggregating distributed data, raising pr…
AVA: Towards Agentic Video Analytics with Vision Language Models
Yuxuan Yan, Shiqi Jiang, Ting Cao +5
AI-driven video analytics has become increasingly important across diverse domains. However, existing systems are often constrained to specific, predefined tasks, limiting their ad…
Retrieval-augmented Generation for GenAI-enabled Semantic Communications
Shunpu Tang, Ruichen Zhang, Yuxuan Yan +4
Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communicatio…
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Yuxuan Yan, Qianqian Yang, Shunpu Tang +1
Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data i…
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +3
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to…
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +4
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applica…