2 citations · 4 across the 10 of their papers we have counts for
10 papers
World-Model-Augmented Web Agents with Action Correction
Zhouzhou Shen, Xueyu Hu, Xiyun Li +3
Web agents based on large language models have demonstrated promising capability in automating web tasks. However, current web agents struggle to reason out sensible actions due to…
OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use
Xueyu Hu, Tao Xiong, Biao Yi +26
The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of (multi-modal) large la…
InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization
Yuhang Liu, Zeyu Liu, Shuanghe Zhu +10
The emergence of Multimodal Large Language Models (MLLMs) has propelled the development of autonomous agents that operate on Graphical User Interfaces (GUIs) using pure visual inpu…
Can Large Language Models Be Trusted Paper Reviewers? A Feasibility Study
Chuanlei Li, Xu Hu, Minghui Xu +3
Academic paper review typically requires substantial time, expertise, and human resources. Large Language Models (LLMs) present a promising method for automating the review process…
Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks
Yurun Chen, Xavier Hu, Keting Yin +2
As researchers continue to optimize AI agents for more effective task execution within operating systems, they often overlook a critical security concern: the ability of these agen…
InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection
Yuhang Liu, Pengxiang Li, Zishu Wei +7
Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers an…
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
Yuhang Liu, Xueyu Hu, Shengyu Zhang +3
Retrieval-Augmented Generation (RAG) has proven to be an effective method for mitigating hallucination issues inherent in large language models (LLMs). Previous approaches typicall…
Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling
Yuwei Yang, Siqi Ouyang, Xueyu Hu +3
Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures. Existing methods either use conditional generative model to learn…
Leveraging Print Debugging to Improve Code Generation in Large Language Models
Xueyu Hu, Kun Kuang, Jiankai Sun +2
Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algori…
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
Xueyu Hu, Ziyu Zhao, Shuang Wei +14
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-e…