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Yao Wu

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Artificial Intelligence Distributed, Parallel, and Cluster Computing Information Retrieval Machine Learning Multiagent Systems physics.geo-ph Social and Information Networks

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preprint2026arXiv

Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations

Operating and maintaining (O&M) large-scale online engine systems (eg, search, recommendation and advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. Despite the inherent suitability of LLM-based agents for such operational scenarios, the critical bottleneck impeding their practical deployment lies not in reasoning, but in orchestration capability - specifically, the precise selection of relevant data (encompassing metrics, logs, and change events) and applicable knowledge (including handbook-defined rules and empirically derived practitioner experience) tailored to each individual operational event. Feeding all signals indiscriminately causes dilution and hallucination, while manually curating the event-to-(data, knowledge) mapping is intractable under dozens of daily releases. Here we present Bian Que, an agentic operating framework with three contributions: (i) The unified operational paradigm, which abstracts routine daily O&M actions into three canonical patterns: release interception, proactive inspection, and alert root cause analysis; (ii) The flexible Skill Arrangement, each predefined Skill explicitly defines the requisite data and operational knowledge for each specific context. Such Skills can be automatically generated and updated by LLM agents, and can also be iteratively optimized by on-call engineers via natural language instructions. (iii) The unified self-evolving mechanism, where each correction signal enables two parallel evolutionary pathways: distilling event memory into knowledge, and targeted refinement of Skills. Deployed on the e-commerce search engine of KuaiShou, Bian Que reduces alert volume by 75%, achieves 80% root-cause analysis accuracy, cuts mean time to resolution by over 50%, and attains a 99.0% pass rate on offline evaluations. Codes are at https://github.com/benchen4395/BianQue_Assistant.

preprint2023arXiv

Constraints for precise and accurate fluid inclusion stable isotope analysis using water-vapour saturated CRDS techniques

Hydrogen ($δ$2H) and oxygen ($δ$18O) isotopes of water extracted from speleothem fluid inclusions are important proxies used for paleoclimate reconstruction. In our study we use a cavity ring-down laser spectroscopy system for analysis and modified the approach of Affolter et al. (2014) for sample extraction. The method is based on crushing of small sub-gram speleothem samples in a heated and continuously water-vapour purged extraction line. The following points were identified: Injection of reference water shows a precision (1$σ$) of 0.4-0.5 permil for $δ$18O values and 1.1-1.9 permil for $δ$2H values for water amounts of 0.1-0.5 $μ$l, which improves with increasing water amount to 0.1-0.3 permil and 0.2-0.7 permil, respectively, above 1 $μ$l. The accuracy of measurements of water injections and water-filled glass capillaries crushed in the system is better than 0.08 permil for $δ$18O and 0.3 permil for $δ$2H values. The reproducibility (1$σ$) based on replicate analysis of speleothem fluid inclusion samples with water amounts > 0.2 $μ$l is 0.5 permil for $δ$18O and 1.2 permil for $δ$2H values, respectively. Isotopic differences between the water vapour background of the extraction system and the fluid inclusions have no significant impact on the measured fluid inclusion isotope values if they are within 10 permil for $δ$18O and 50 permil for $δ$2H values of the background. Tests of potential adsorption effects with inclusion free spar calcite confirm that the isotope values are unaffected by adsorption for water contents of about 1 $μ$l (fluid inclusion) water per g of carbonate or above. Fluid inclusion analysis on three different modern to late Holocene speleothems from caves in northwest Germany resulted ...

preprint2021arXiv

Multi-Task Representation Learning with Multi-View Graph Convolutional Networks

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks.

preprint2016arXiv

Structural Analysis of User Choices for Mobile App Recommendation

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. While there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this paper, we provide a systematic study for addressing these challenges. Specifically, we develop a Structural User Choice Model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption data set collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art top-N recommendation methods by a significant margin.

preprint2011arXiv

Efficient Multicore Collaborative Filtering

This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique item taxonomy characteristics as well as the large data set size.To handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best prediction result out of more then ten implemented algorithms. For rapidly computing multiple solutions of various algorithms, we have implemented an open source parallel collaborative filtering library on top of the GraphLab machine learning framework. We report some preliminary performance results obtained using the BlackLight supercomputer.