28 citations · 108 across the 31 of their papers we have counts for
31 papers
Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
Xinhao Deng, Yixiang Zhang, Jiaqing Wu +15
Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks. However, their tightly coupled…
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction
Chang Su, Dengliang Shi, Siyuan Huang +5
Large language models (LLMs) have increasingly been explored as powerful text embedders. Existing LLM-based text embedding approaches often leverage the embedding of the final toke…
Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Yuqin Dai, Shuo Yang, Guoqing Wang +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges.…
Agent Safety Alignment via Reinforcement Learning
Zeyang Sha, Hanling Tian, Zhuoer Xu +3
The emergence of autonomous Large Language Model (LLM) agents capable of tool usage has introduced new safety risks that go beyond traditional conversational misuse. These agents,…
Gumbel Reranking: Differentiable End-to-End Reranker Optimization
Siyuan Huang, Zhiyuan Ma, Jintao Du +5
RAG systems rely on rerankers to identify relevant documents. However, fine-tuning these models remains challenging due to the scarcity of annotated query-document pairs. Existing…
GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs
Sheng Tian, Xintan Zeng, Yifei Hu +7
Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological…
Mirror-Consistency: Harnessing Inconsistency in Majority Voting
Siyuan Huang, Zhiyuan Ma, Jintao Du +3
Self-Consistency, a widely-used decoding strategy, significantly boosts the reasoning capabilities of Large Language Models (LLMs). However, it depends on the plurality voting rule…
PC: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval
Yue Duan, Zhangxuan Gu, Zhenzhe Ying +3
In the realm of cross-modal retrieval, seamlessly integrating diverse modalities within multimedia remains a formidable challenge, especially given the complexities introduced by n…
E-ANT: A Large-Scale Dataset for Efficient Automatic GUI NavigaTion
Ke Wang, Tianyu Xia, Zhangxuan Gu +5
Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications. With the rapid development of large languag…
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Yunfei Liu, Jintang Li, Yuehe Chen +9
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning…
TroubleLLM: Align to Red Team Expert
Zhuoer Xu, Jianping Zhang, Shiwen Cui +2
Large Language Models (LLMs) become the start-of-the-art solutions for a variety of natural language tasks and are integrated into real-world applications. However, LLMs can be pot…
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Jintang Li, Jiawang Dan, Ruofan Wu +9
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications,…
HeteroNet: Heterophily-aware Representation Learning on Heterogenerous Graphs
Jintang Li, Zheng Wei, Jiawang Dan +9
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literat…
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu +8
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the…
Backpropagation Path Search On Adversarial Transferability
Zhuoer Xu, Zhangxuan Gu, Jianping Zhang +3
Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversari…
On the Robustness of Latent Diffusion Models
Jianping Zhang, Zhuoer Xu, Shiwen Cui +3
Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. However, the robustness of latent diffusio…
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Sheng Tian, Jihai Dong, Jintang Li +7
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally conne…
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
Jintang Li, Sheng Tian, Ruofan Wu +6
The prevalence of large-scale graphs poses great challenges in time and storage for training and deploying graph neural networks (GNNs). Several recent works have explored solution…
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Zhangxuan Gu, Zhuoer Xu, Haoxing Chen +3
Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it…
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Jintang Li, Huizhe Zhang, Ruofan Wu +5
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensi…
DiffUTE: Universal Text Editing Diffusion Model
Haoxing Chen, Zhuoer Xu, Zhangxuan Gu +6
Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text an…
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Jiafu Wu, Mufeng Yao, Dong Wu +6
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner. In practical scenarios…
DiffusionInst: Diffusion Model for Instance Segmentation
Zhangxuan Gu, Haoxing Chen, Zhuoer Xu +3
Diffusion frameworks have achieved comparable performance with previous state-of-the-art image generation models. Researchers are curious about its variants in discriminative tasks…
Hierarchical Dynamic Image Harmonization
Haoxing Chen, Zhangxuan Gu, Yaohui Li +4
Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background. Recent works mainly focus on using global…