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arXiv 2025年

作者： Wu, Liming Huang, Wenbing Jiao, Rui Huang, Jianxing Liu, Liwei Zhou, Yipeng Sun, Hao Liu, Yang Sun, Fuchun Ren, Yuxiang Wen, Jirong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Advance Computing and Storage Lab Huawei Technologies Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China Institute for AI Industry Research Tsinghua University Beijing China

Crystal Structure Prediction (CSP), which aims to generate stable crystal structures from chemical compositions, represents a critical pathway for discovering novel materials. While structure prediction tasks in other domains, such as proteins, have seen remarkable advancements, CSP remains a relatively underexplored area due to the significantly more complex geometries inherent in crystal structures. In this paper, we propose Siamese foundation models specifically designed to address CSP. Our pretrain-finetune framework, named DAO, comprises two complementary foundation models: DAO-G for structure generation and DAO-P for energy prediction. Experiments on CSP benchmarks (MP-20 and MPTS-52) demonstrate that our DAO-G significantly surpasses state-of-the-art (SOTA) methods across all metrics. Extensive ablation studies further confirm that DAO-G excels in generating diverse polymorphic structures, and the dataset relaxation and energy guidance provided by DAO-P are essential for enhancing DAO-G’s performance. When applied to three real-world superconductors (CsV3Sb5, Zr16Rh8O4 and Zr16Pd8O4) that are known to be challenging to analyze, our foundation models achieve accurate critical temperature predictions and structure generations. For instance, on CsV3Sb5, DAO-G generates a structure very close to the experimental one with an RMSE of 0.0085;DAO-P predicts the Tc value with high accuracy (2.26 K vs. the ground-truth value of 2.30 K). In contrast, conventional DFT calculators like Quantum Espresso (QE) only successfully derive the structure of the first superconductor within an acceptable time frame, while the RMSE is nearly 8 times larger, and the computation speed is more than 1000 times slower. These compelling results collectively highlight the potential of our approach for advancing materials science research and development. © 2025, CC BY.

关键词： Crystal structure

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GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation

arXiv

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arXiv 2025年

作者： Sun, Shengyin Yu, Wenhao Ren, Yuxiang Du, Weitao Liu, Liwei Zhang, Xuecang Hu, Ying Ma, Chen Department of Computer Science City University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Advance Computing and Storage Lab Huawei Technologies China Academy of Mathematics and Systems Science Chinese Academy of Sciences China Department of Electronic Engineering Tsinghua University China

Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Existing models do not adequately capture the "face" information in molecular graphs for the reaction center identification. (ii) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules’ inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms state-of-the-art semi-template models across various evaluative metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular docking

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Leveraging RAG-Enhanced Large Language Model for Semi-Supervised Log Anomaly Detection 35

Leveraging RAG-Enhanced Large Language Model for Semi-Superv...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Zhang, Wanhao Zhang, Qianli Yu, Enyu Ren, Yuxiang Meng, Yeqing Qiu, Mingxi Wang, Jilong Tsinghua University Institute for Network Sciences and Cyberspace BNRist Beijing China Zhongguancun Laboratory Beijing China Huawei Technologies Advance Computing and Storage Lab China

ISBN: (纸本)9798350353884

Log-based anomaly detection is critical in monitoring the operations of information systems and in the real-Time reporting of system failures. Utilizing deep learning-based log anomaly detection methods facilitates effective detection of anomalies within logs. However, existing methods are greatly dependent on log parsers, and parsing errors can considerably affect downstream anomaly detection tasks. Additionally, methods that predict the next log event in a sequence are susceptible to the instability of sequences and the emergence of unseen logs as systems evolve, resulting in a higher false positive rate. In this paper, we put forward LogRAG, a semi-supervised log anomaly detection framework based on retrieval-Augmented generation (RAG). This framework conducts phased detection using both Log Tokens and Log Templates to mitigate the impact of log parsing errors. It also utilizes a single-class classifier to model the normal behavior of the system, thereby circumventing the effects of unstable sequences. Finally, it employs large language model (LLM) empowered by RAG to reevaluate detected anomalous logs, thereby improving accuracy. LogRAG demonstrates a 15% improvement in F1 Score on the BGL dataset and a 60% improvement on the Spirit dataset when compared to the previous best semi-supervised learning algorithm. © 2024 IEEE.

关键词： Self-supervised learning

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LogRAG: Semi-Supervised Log-based Anomaly Detection with Retrieval-Augmented Generation

LogRAG: Semi-Supervised Log-based Anomaly Detection with Ret...

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IEEE International Conference on Web Services (ICWS)

作者： Wanhao Zhang Qianli Zhang Enyu Yu Yuxiang Ren Yeqing Meng Mingxi Qiu Jilong Wang Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Advance Computing and Storage Lab Huawei Technologies China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Log-based anomaly detection is critical in monitoring the operation of microservice systems and in the realtime reporting of system failures. Utilizing deep learning-based log anomaly detection methods facilitates effective detection of anomalies within logs. However, existing methods are greatly dependent on log parsers, and parsing errors can considerably affect downstream anomaly detection tasks. Additionally, methods that predict the next log event in a sequence are susceptible to the instability of sequences and the emergence of unseen logs as systems evolve, resulting in a higher false positive rate. In this paper, we propose a semi-supervised log anomaly detection framework based on retrieval-augmented generation (RAG). This framework conducts phased detection using both Log Tokens and Log Templates to mitigate the impact of log parsing errors. It also utilizes a single-class classifier to model the normal behavior of the system, thereby circumventing the effects of unstable sequences. Finally, it employs large language model (LLM) empowered by RAG to reevaluate detected anomalous logs.

关键词： Analytical models Web services Large language models Microservice architectures Anomaly detection Monitoring

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Leveraging RAG-Enhanced Large Language Model for Semi-Supervised Log Anomaly Detection

Leveraging RAG-Enhanced Large Language Model for Semi-Superv...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Wanhao Zhang Qianli Zhang Enyu Yu Yuxiang Ren Yeqing Meng Mingxi Qiu Jilong Wang Institute for Network Sciences and Cyberspace BNRist Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Advance Computing and Storage Lab Huawei Technologies China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Log-based anomaly detection is critical in monitoring the operations of information systems and in the real-time reporting of system failures. Utilizing deep learning-based log anomaly detection methods facilitates effective detection of anomalies within logs. However, existing methods are greatly dependent on log parsers, and parsing errors can considerably affect downstream anomaly detection tasks. Additionally, methods that predict the next log event in a sequence are susceptible to the instability of sequences and the emergence of unseen logs as systems evolve, resulting in a higher false positive rate. In this paper, we put forward LogRAG, a semi-supervised log anomaly detection framework based on retrieval-augmented generation (RAG). This framework conducts phased detection using both Log Tokens and Log Templates to mitigate the impact of log parsing errors. It also utilizes a single-class classifier to model the normal behavior of the system, thereby circumventing the effects of unstable sequences. Finally, it employs large language model (LLM) empowered by RAG to reevaluate detected anomalous logs, thereby improving accuracy. LogRAG demonstrates a 15% improvement in F1 Score on the BGL dataset and a 60% improvement on the Spirit dataset when compared to the previous best semi-supervised learning algorithm.

关键词： Analytical models Accuracy Large language models Software algorithms Semisupervised learning Real-time systems Software reliability Anomaly detection Monitoring Information systems

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Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs

arXiv

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arXiv 2023年

作者： Sun, Shengyin Ren, Yuxiang Chen, Jiehao Ma, Chen Department of Computer Science City University of Hong Kong Hong Kong Advance Computing and Storage Lab Huawei Technologies Shanghai China China Academy of Industrial Internet Beijing China

Inspired by the success of Large Language Models (LLMs) in natural language processing (NLP), recent works have begun investigating the potential of applying LLMs in graph learning. However, most existing work focuses on utilizing LLMs as node feature augmenters, leaving employing LLMs to enhance topological structures an understudied problem. In this paper, we are dedicated to leveraging LLMs’ text comprehension capability to enhance the topological structure of text-attributed graphs (TAGs). First, we propose using LLMs to help remove/add edges in the TAG. Specifically, we first let the LLM output the semantic similarity between nodes through delicate prompt designs, and then perform edge deletion/addition based on the similarity. Second, we propose using pseudo-labels generated by the LLM to improve graph topology;we introduce pseudo-label propagation as a regularization to guide the graph neural network (GNN) in learning proper edge weights. Finally, we incorporate the two aforementioned graph topological refinements into the GNN training, theoretically justify the benefits of the proposed topology refinements, and perform extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed methods. Code available at https://***/sunshy-1/LLM4GraphTopology. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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