49 citations · 279 across the 48 of their papers we have counts for
14 papers · 1 filter
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…
ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis
Guohong Liu, Jialei Ye, Pengzhi Gao +4
GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments. However, directly doing so in…
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…
ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Gaole Dai, Shiqi Jiang, Ting Cao +5
Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents,…
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…
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…
Advancing Mobile GUI Agents: A Verifier-Driven Approach to Practical Deployment
Gaole Dai, Shiqi Jiang, Ting Cao +5
We propose V-Droid, a mobile GUI task automation agent. Unlike previous mobile agents that utilize Large Language Models (LLMs) as generators to directly generate actions at each s…
AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation
Hao Wen, Shizuo Tian, Borislav Pavlov +8
Large language models (LLMs) have brought exciting new advances to mobile UI agents, a long-standing research field that aims to complete arbitrary natural language tasks through m…
LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design
Rui Kong, Qiyang Li, Xinyu Fang +7
Recent literature has found that an effective method to customize or further improve large language models (LLMs) is to add dynamic adapters, such as low-rank adapters (LoRA) with…
Empowering In-Browser Deep Learning Inference on Edge Devices with Just-in-Time Kernel Optimizations
Fucheng Jia, Shiqi Jiang, Ting Cao +9
Web is increasingly becoming the primary platform to deliver AI services onto edge devices, making in-browser deep learning (DL) inference more prominent. Nevertheless, the heterog…
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…
AutoDroid: LLM-powered Task Automation in Android
Hao Wen, Yuanchun Li, Guohong Liu +7
Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones. However, existing approaches suffer from poor scalab…
SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget
Rui Kong, Yuanchun Li, Qingtian Feng +5
Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) with conditionally-activated parallel experts. However, serving MoE models on me…