49 citations · 279 across the 48 of their papers we have counts for
14 papers · 1 filter
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…
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,…
ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
Hao Wen, Yifan Su, Feifei Zhang +4
Recent advances in Large Language Models (LLMs) have been driven by test-time compute scaling - a strategy that improves reasoning by generating longer, sequential thought processe…
BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
Hao Wen, Xinrui Wu, Yi Sun +7
Recent advancements in Large Language Models (LLMs) have leveraged increased test-time computation to enhance reasoning capabilities, a strategy that, while effective, incurs signi…
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…
LLM-Explorer: Towards Efficient and Affordable LLM-based Exploration for Mobile Apps
Shanhui Zhao, Hao Wen, Wenjie Du +5
Large language models (LLMs) have opened new opportunities for automated mobile app exploration, an important and challenging problem that used to suffer from the difficulty of gen…
Mobile-Bench-v2: A More Realistic and Comprehensive Benchmark for VLM-based Mobile Agents
Weikai Xu, Zhizheng Jiang, Yuxuan Liu +7
VLM-based mobile agents are increasingly popular due to their capabilities to interact with smartphone GUIs and XML-structured texts and to complete daily tasks. However, existing…
GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration
Yuchen Sun, Shanhui Zhao, Tao Yu +5
GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applicat…
KVShare: An LLM Service System with Efficient and Effective Multi-Tenant KV Cache Reuse
Huan Yang, Renji Zhang, Mingzhe Huang +5
Recent advances in long-text understanding have pushed the context length of large language models (LLMs) up to one million tokens. It boosts LLMs's accuracy and reasoning capacity…
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…
Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference
Zihao Zheng, Yuanchun Li, Jiayu Chen +3
Enhancing the computational efficiency of on-device Deep Neural Networks (DNNs) remains a significant challengein mobile and edge computing. As we aim to execute increasingly compl…