31 citations · 31 across the 8 of their papers we have counts for
10 papers
AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interaction
Shanhui Zhao, Jiacheng Liu, Guohong Liu +13
AI agents are driving a new software paradigm, with the ability to autonomously call tools, extract information, manage memory, and complete tasks that span applications and data s…
Joint Agent Memory and Exploration Learning via Novelty Signals
Shizuo Tian, Xiaohong Weng, Rui Kong +9
In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retai…
AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Management
Shizuo Tian, Hao Wen, Yuxuan Chen +6
The rapid development of mobile GUI agents has stimulated growing research interest in long-horizon task automation. However, building agents for these tasks faces a critical bottl…
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision
Ge Chang, Jinbo Su, Jiacheng Liu +7
Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that…
GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning
Ge Chang, Jinbo Su, Jiacheng Liu +7
Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) techniques have exhibited remarkable performance across a wide range of domains. However, existing…
Mobile GUI Agents under Real-world Threats: Are We There Yet?
Guohong Liu, Jialei Ye, Jiacheng Liu +5
Recent years have witnessed a rapid development of mobile GUI agents powered by large language models (LLMs), which can autonomously execute diverse device-control tasks based on n…
An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint
Yi Sun, Han Wang, Jiaqiang Li +8
Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much h…
ChainStream: An LLM-based Framework for Unified Synthetic Sensing
Jiacheng Liu, Yuanchun Li, Liangyan Li +5
Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-con…
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Yuanchun Li, Hao Wen, Weijun Wang +22
Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming…
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Wenxing Xu, Yuanchun Li, Jiacheng Liu +5
Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environme…