OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education
Authors: Pengyang Shao, Yonghui Yang, Chen Gao, Lei Chen, Kun Zhang, Chenyi Zhuang, Le Wu, Yong Li, Meng Wang
Venue: KDD
Year: 2025

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion.Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.
FLEXIS: FLEXible Frequent Subgraph Mining using Maximal Independent Sets
Authors: Akshit Sharma, Sam Reinehr, Dinesh Mehta, Bo Wu
Venue: KDD
Year: 2025

Frequent Subgraph Mining (FSM) is the process of identifying common subgraph patterns that occur over a certain threshold. The NP-hardness of FSM makes it a complex and time-consuming task. FSM is generally solved in 2 steps, 1). Generation step: determining the possible patterns that can be frequent and 2). Metric step: determining if the pattern is frequent. In the literature, the generation step is usually solved by vertex or edge extension methods. These methods produce a lot of redundant candidate patterns which must be removed, and hence increasing the latency. To address these challenges, the paper introduces a vertex based merging method which reduces the redundancies, and introduces effective pruning mechanisms. Moreover, the existing metrics to determine if a pattern is frequent or not, either is highly accurate while demanding significant computational time (Maximum Independent Set (MIS)) or overestimates the pattern count while taking less time (Minimum Node Image (MNI)). Thus, the paper introduces ''Maximal Independent Set (mIS)'' metric, which minimizes latency while obtaining accuracy close to MIS, which is controlled by a pattern overlap parameter (łambda).Through extensive experimentation, our proposed method achieves an average of 10.58× speedup when compared to GraMi and an average of 3× speedup when compared to T-FSM.
CURE: A dataset for Clinical Understanding & Retrieval Evaluation
Authors: Nadia Sheikh, Daniel Buades Marcos, Anne-Laure Jousse, Akintunde Oladipo, Olivier Rousseau, Jimmy Lin
Venue: KDD
Year: 2025

Given the dominance of dense retrievers which do not generalize well beyond their training dataset distributions, domain-specific test sets are essential in evaluating retrieval performance. Few test datasets are available for retrieval systems intended for use by healthcare providers in a point-of-care setting. To fill this gap we have collaborated with medical professionals to create CURE, a test dataset for passage retrieval composed of 2,000 expert written queries spanning 10 medical domains with a monolingual (English) and two cross-lingual (French/Spanish to English) conditions. In this paper, we describe how CURE was constructed and provide baseline results to showcase its effectiveness as an evaluation tool. CURE is published with a CC BY-NC 4.0 license and can be accessed on https://huggingface.co/datasets/clinia/CUREv Hugging Face and as a retrieval task on fhttps://github.com/embeddings-benchmark/mteb MTEB.
AliBoost: Ecological Boosting Framework in Alibaba Platform
Authors: Qijie Shen, Yuanchen Bei, Zihong Huang, Jialin Zhu, Keqin Xu, Boya Du, Jiawei Tang, Yuning Jiang, Feiran Huang, Xiao Huang, Hao Chen
Venue: KDD
Year: 2025

Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. This ''rich-get-richer'' phenomenon hinders the growth of potentially valuable cold items and harms the platform's ecosystem. Existing cold-start models primarily focus on improving initial recommendation performance for cold items but fail to address users' natural preference for popular content. In this paper, we introduce AliBoost, Alibaba's ecological boosting framework, designed to complement user-oriented natural recommendations and foster a healthier ecosystem. AliBoost incorporates a tiered boosting structure and boosting principles to ensure high-potential items quickly gain exposure while minimizing disruption to low-potential items. To achieve this, we propose the Stacking Fine-Tuning Cold Predictor to enhance the foundation CTR model's performance on cold items for accurate CTR and potential prediction. AliBoost then employs an Item-oriented Bidding Boosting mechanism to deliver cold items to the most suitable users while balancing boosting speed with user-personalized preferences. Over the past six months, AliBoost has been deployed across Alibaba's mainstream platforms, successfully cold-starting over a billion new items and increasing both clicks and GMV of cold items by over 60% within 180 days. Extensive online analysis and A/B testing demonstrate the effectiveness of AliBoost in addressing ecological challenges, offering new insights into the design of billion-scale recommender systems.
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation
Authors: Maying Shen, Nadine Chang, Sifei Liu, José M. Álvarez
Venue: KDD
Year: 2025

In recent years, the data collected for artificial intelligence has grown to an unmanageable amount. Particularly within industrial applications, such as autonomous vehicles, model training computation budgets are being exceeded while model performance is saturating -- and yet more data continues to pour in. To navigate the flood of data, we propose a framework to select the most semantically diverse and important dataset portion. Then, we further semantically enrich it by discovering meaningful new data from a massive unlabeled data pool. Importantly, we can provide explainability by leveraging foundation models to generate semantics for every data point. We quantitatively show that our Semantic Selection and Enrichment framework (SSE) can a) successfully maintain model performance with a smaller training dataset and b) improve model performance by enriching the smaller dataset without exceeding the original dataset size. Consequently, we demonstrate that semantic diversity is imperative for optimal data selection and model performance.
LLMs Are Noisy Oracles! LLM-based Noise-aware Graph Active Learning for Node Classification
Authors: Zeang Sheng, Weiyang Guo, Yingxia Shao, Wentao Zhang, Bin Cui
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have achieved significant success in various graph learning tasks. However, their success relies heavily on the availability of adequate high-quality labels, which requires high annotation costs. Recently, Large Language Models (LLMs) became known to the community for their strong zero-shot performance on diverse textual tasks. To utilize LLM's zero-shot abilities, existing work substitutes the oracle in the graph active learning setting with an LLM and achieves low-cost annotation compared to consulting human experts. However, annotations from LLMs inevitably contain noise, and we find through an empirical analysis that LLMs possess complex and dataset-specific annotation noise distributions. This intricacy of LLMs' annotation noise is neglected by existing work, leading to sub-optimal annotation data quality under the same budget. In this paper, we propose a Dataset- and LLM-Aware graph active learning framework called DMA that explicitly models LLM's annotation noise distributions on the given dataset. Concretely, DMA queries the oracle LLM with manually constructed prompts for each class's pseudo samples that are then used to measure the class-wise annotation noise rates. We then equip DMA with a new noise-aware graph active learning algorithm to utilize this fine-grained annotation noise distribution. Extensive performance optimizations are applied to the implementation to boost DMA's scalability and runtime efficiency on large-scale datasets. Evaluations on five text-attributed graph datasets show that DMA consistently outperforms all the baselines in terms of annotation data quality, which illustrates the importance of careful handling of LLM's intricate annotation noise.
Cooled-KLL: Enhancing Quantile Estimation by Filtering Hot Item
Authors: Qilong Shi, Wei Zhou, Yizhuo Zheng, Xinye Xu, Ting Yang, Yuanyuan Zhang, Long Yao, Yangyang Wang, Mingwei Xu
Venue: KDD
Year: 2025

Quantile estimation is critical for diverse applications, including database management and network traffic monitoring. Probabilistic quantile sketches are widely employed in practice, with the KLL sketch (introduced in 2016) being particularly notable for its theoretically space-optimal properties. However, KLL overlooks the inherent repetition of elements often present in real-world data streams. Such streams are frequently highly skewed, characterized by ''hot items''-items that appear with high frequency. The KLL sketch processes these hot items without accounting for their prevalence, resulting in suboptimal space utilization due to redundant insertions and storage. To overcome this limitation, we propose Cooled-KLL, an enhanced KLL sketch. Cooled-KLL introduces a novel ''Hot Filter'' structure that efficiently identifies and stores hot items as compact key-value pairs. This mechanism ensures that only ''cold'' (less frequent) items are subsequently processed by the core KLL sketch. Our approach significantly reduces memory consumption without compromising processing speed. Extensive experiments demonstrate that Cooled-KLL consistently outperforms five other state-of-the-art algorithms, achieving up to 2.5 orders of magnitude higher accuracy compared to the standard KLL sketch.
Spectral Survival Analysis
Authors: Chengzhi Shi, Stratis Ioannidis
Venue: KDD
Year: 2025

Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. We identify a fundamental connection between rank regression and the CoxPH model: this allows us to adapt and extend the so-called spectral method for rank regression to survival analysis. Our approach is versatile, naturally generalizing to several CoxPH variants, including deep models. We empirically verify our method's scalability on multiple real-world high-dimensional datasets; our method outperforms legacy methods w.r.t. predictive performance and efficiency.
ComRecycle: An Intelligent Computation Recycling Framework for Online Advertising
Authors: Chufeng Shi, Yangsu Liu, Rui Qiu, Zhenzhe Zheng, Dagui Chen, Ruitao Zhu, Fan Wu
Venue: KDD
Year: 2025

xisting online advertising systems generate high-quality ad recommendations through a complex online serving pipeline whenever a user's ad request arrives. However, our analyses on the display advertising system in Taobao show that this paradigm could lead to an inefficient utilization of computational resources. In this work, we propose an intelligent computation recycling framework called ComRecycle, which caches and reuses unexposed ad sets for repetitive ad requests to improve the computational resource utilization. We introduce fine-grained computation recycling strategies, and formulate the computation recycling decision as an online constrained optimization problem. Therefore, ComRecycle can achieve the goal of reducing computation costs while guaranteeing the same level of recommendation performance. Extensive offline experiments validate the correctness and effectiveness of ComRecycle. Our online A/B testing demonstrates that ComRecycle can save over 20% computational resources while maintaining the same system performance as the baseline.
FlowXpert: Expertizing Troubleshooting Workflow Orchestration with Knowledge Base and Multi-Agent Coevolution
Authors: Binpeng Shi, Yu Luo, Jingya Wang, Yongxin Zhao, Shenglin Zhang, Bowen Hao, Chenyu Zhao, Yongqian Sun, Zhi Zhang, Ronghua Sun, Haihua Li, Wei Song, Xiaolong Chen, Jingbo Miao, Dan Pei
Venue: KDD
Year: 2025

Incident management remains a critical yet challenging task for large-scale cloud services. Most cloud service providers abstract troubleshooting into predefined workflows for different incidents, offering step-by-step guidance. However, manually crafting workflows is resource-consuming and knowledge-intensive, hindering large-scale deployment. Most automated techniques for workflow orchestration rely on large language models (LLMs) to handle complex tasks but overlook key aspects of troubleshooting, including complex expertise, domain requirements, and the reliability of AI feedback. These limitations undermine workflow quality. Therefore, we propose FlowXpert, a novel framework for troubleshooting workflow orchestration. Leveraging LLMs, it first builds a knowledge base centered on incident-aware nodes to precisely depict expertise. Then, fed into AI feedback and synthetic preference data, reinforcement learning is applied to refine the workflow generator and evaluator. To assess troubleshooting workflows, we introduce OpsFlowBench based on Huawei Cloud's datacenter switch operation documents. Benchmark tests under the tailored STEPScore metric validate its effectiveness. Furthermore, during a 10-week deployment in Huawei Cloud's datacenter network, FlowXpert provided valuable support to both on-call engineers and AI executors, as evidenced by empirical data and case study.
HeavyLocker: Lock Heavy Hitters in Distributed Data Streams
Authors: Qilong Shi, Xirui Li, Hanyue Zheng, Tong Yang, Yangyang Wang, Mingwei Xu
Venue: KDD
Year: 2025

In recent years, sketching has emerged as a pivotal technique for identifying heavy hitters (items with high frequency) in large-scale data streams. Despite this progress, the majority of existing sketch algorithms are tailored primarily for detecting local heavy hitters within a single data stream, with only a few capable of extending their application to global heavy hitters across distributed data streams. A common challenge encountered by these algorithms is balancing performance with accuracy. To address this challenge, we introduce HeavyLocker, a novel sketch algorithm that takes advantage of a distinct feature of real data streams: the separability of heavy hitters. By leveraging this attribute, HeavyLocker precisely locks and protects potential heavy hitters during the data stream processing, ensuring accuracy in local heavy hitter detection without compromising on speed. This unique capability also facilitates its application to global detection tasks. Through theoretical analysis, we validate the efficacy of HeavyLocker's locking mechanism. Our extensive experiments show that HeavyLocker outperforms five benchmarked algorithms in accuracy and maintains fast speed for both local and global heavy hitter detection, significantly reducing errors by up to an order of magnitude compared to the renowned Double-Anonymous Sketch.
Enhancing Bone Mineral Density Estimation from X-ray Images with Cross-Modal Knowledge Distillation
Authors: Keyue Shi, Qianqian Shen, Zhongda Qi, Junyao Yang, Zhaoming Ye, Jiajun Bu, Haishuai Wang
Venue: KDD
Year: 2025

Dual-energy X-ray absorptiometry (DXA) enables accurate bone mineral density but requires specialized equipment and protocols. X-ray-based BMD screening offers opportunistic early detection, though prior methods struggle with X-ray intensity variations and demand large datasets. We introduce a cross-modal knowledge distillation BMD prediction framework (CMKD-BMD) fusing X-ray/CT data to enhance X-ray-only BMD prediction. Each single-modal student network employs a multi-scale visual extractor for hierarchical features, an unsupervised graph-based structural learner for anatomical relationships, and an adaptive fusion module to generate a unified representation. The teacher network integrates single-modal representations from students and transfers multimodal knowledge from the teacher to students. Our model outperforms existing methods on both the collected dataset (comprising 1620 X-rays and 280 CT cases) and the publicly available VerSe2019 dataset, demonstrating superior BMD estimation performance. Furthermore, we developed OrthoSim, an orthopedic surgical simulation platform with CMKD-BMD, which has shown promising clinical effectiveness in trial evaluations. Our code is available at https://github.com/KeyueShi/CMKD-BMD.
AntAkso: Claims Management System for Health Insurance in Alipay
Authors: Qitao Shi, Jun Zhou, Ya-Lin Zhang, Longfei Li, Chaoyi Ma, Yifan Wu, Xiaobo Qin
Venue: KDD
Year: 2025

The rapid growth of health insurance and the rising incidence of fraudulent claims underscore the necessity for an efficient and professional claims management system. However, there is a noticeable lack of shared relevant experience from previous research in this field. In response to this challenge, we introduce AntAkso, a robust claims management system specifically designed for health insurance operations within Alipay. AntAkso incorporates a digital and professional management system, achieving a notable decrease in the volume of false claims, reduction in administrative costs, and heightened satisfaction among its policyholders. We begin by highlighting the core components of this system, including the case stratification, hospital recommendation, and case dispatch modules, along with the pivotal algorithms employed, i.e., the fraud detection, recommendation, and robust satisficing algorithms. We also detail the system's implementation and deployment. We substantiate the proposed system's effectiveness and efficiency with empirical evidence from experiments on a large set of real-world health insurance claims data.
BiQAP: Neural Bi-level Optimization-based Framework for Solving Quadratic Assignment Problems
Authors: Liangliang Shi, Haoran Zhang, Shuheng Shen, Changhua Meng, Weiqiang Wang, Junchi Yan
Venue: KDD
Year: 2025

The Quadratic Assignment Problem (QAP) has attracted lasting attention for its wide applications and computational challenges. Despite the rich literature in machine learning for QAP, most works often address the problem in the setting of image matching, whereby deep networks could play a vital role in extracting useful features for the subsequent matching. While its power on pure numerical QAP instances is limited in node embedding, often with a vanilla graph neural network. This paper tries to tap the potential of deep nets for QAP, specifically by modifying the input instance, which is orthogonal to previous efforts. Specifically, we develop a bi-level unsupervised framework, where the inner optimization involves trying to solve the modified instance with entropic regularization that can be solved iteratively using the Sinkhorn algorithm without affecting backpropagation by truncating gradients during training. The outer minimization deals with the quadratic objective function of the original QAP. In particular, seeing the intractable scale of the most general form, i.e., Lawler's QAP and the practical utility of the more efficient Koopmans-Beckmann QAP (KBQAP) form for solving other graph and combinatorial problems like TSP and graph edit distance, we embody our network on the KBQAP, and show its strong performance on various benchmarks in our experiments.
Off-Policy Evaluation and Learning for the Future under Non-Stationarity
Authors: Tatsuhiro Shimizu, Kazuki Kawamura, Takanori Muroi, Yusuke Narita, Kei Tateno, Takuma Udagawa, Yuta Saito
Venue: KDD
Year: 2025

We study the novel problem of future off-policy evaluation (F-OPE) and learning (F-OPL) for estimating and optimizing the future value of policies in non-stationary environments, where distributions vary over time. In e-commerce recommendations, for instance, our goal is often to estimate and optimize the policy value for the upcoming month using data collected by an old policy in the previous month. A critical challenge is that data related to the future environment is not observed in the historical data. Existing methods assume stationarity or depend on restrictive reward-modeling assumptions, leading to significant bias. To address these limitations, we propose a novel estimator named Off-Policy Estimator for the Future Value (OPFV), designed for accurately estimating policy values at any future time point. The key feature of OPFV is its ability to leverage the useful structure within time-series data. While future data might not be present in the historical log, we can leverage, for example, seasonal, weekly, or holiday effects that are consistent in both the historical and future data. Our estimator is the first to exploit these time-related structures via a new type of importance weighting, enabling effective F-OPE. Theoretical analysis identifies the conditions under which OPFV becomes low-bias. In addition, we extend our estimator to develop a new policy-gradient method to proactively learn a good future policy using only historical data. Empirical results show that our methods substantially outperform existing methods in estimating and optimizing the future policy value under non-stationarity for various experimental setups.