OpenRaiser/Intern-Atlas huggingface dataset:
Neural Network Pruning for Invariance Learning
Authors: Derek Xu, Yuanzhou Chen, Yizhou Sun, Wei Wang
Venue: KDD
Year: 2025

Model scaling laws have driven the development of neural networks with more-and-more parameters. As a result, there is a growing need for neural network pruning to enable the efficient deployment during inference. We take a deeper look at neural network pruning from the lens of invariance preservation. We argue that successfully pruned neural networks should be invariant to transformations which do not alter the data's underlying semantics (ex. translations). To this goal, we first show existing post-pruning algorithms do not perserve desired invariances. We then propose a framework to discover novel architectures that do capture desired invariances from data via pruning. Specifically, we show contrastive learning with small initialization can effectively transfer invariance preservation encoded in the model weights to the pruning mask. Our approach consistently outperform traditional pruning algorithms on fully-connected, convolutional, and transformer networks across 3 vision, 40 tabular, and 1 natural language datasets.
Leveraging Label Distributions as Anchors to Enhance Video Recommendation
Authors: Yulin Xu, Chao Cui, Shisong Tang, Fan Li, Bing Han, Huafeng Cao, Jiechao Gao, Hechang Chen
Venue: KDD
Year: 2025

In video recommendation systems, accurately predicting watch time is crucial for enhancing user engagement and retention. Traditional methods typically apply label transformations or mitigate duration bias to improve performance but overlook that erroneous instance representations are the primary cause of significant prediction errors. Moreover, these approaches predominantly rely on point perdition, limiting their robustness. To address these challenges, we propose LDA, a novel prediction paradigm that optimizes instance representations by explicitly leveraging label distributions as anchors within the model, enabling more accurate and robust predictions. Our analysis reveals that watch ratio across different duration groups exhibit distinct multi-peak distributions, reflecting the strong aggregation of user behavior. Based on this finding, we employ Vector Quantized Variational Auto-encoder (VQ-VAE) to convert the continuous watch ratio distribution into representative anchors that capture these multi-peak characteristics within each duration group. Subsequently, we project both instance representations and anchors into a common space and utilize Optimal Transport (OT) to generate pseudo-labels aligned with the anchor distribution, allowing instances to obtain structured coordinates within this space during training. Finally, we derive optimized instance representations for watch time prediction by aggregating anchor vectors through weighted integration. Extensive offline experiments on two datasets and large-scale online A/B testing on a short-video platform with over 300 million DAUs demonstrate the consistent superiority of LDA in watch time prediction.
Succinct Interaction-Aware Explanations
Authors: Sascha Xu, Joscha Cüppers, Jilles Vreeken
Venue: KDD
Year: 2025

Shapley values (Shap) are a popular approach to explaining decisions of black-box models by revealing the importance of individual features. Shap explanations are easy to interpret, but as they do not incorporate feature interactions, they are also incomplete and potentially misleading. Interaction-aware methods such as nShap report the additive importance of all subsets up to n features. These explanations are complete, but in practice excessively large and difficult to interpret. In this paper, we combine the best of both worlds. We partition the features into significantly interacting groups, and use these to compose a succinct, interpretable explanation. To determine which partitioning out of super-exponentially many explains a model best, we derive a criterion that weighs the complexity of an explanation against its representativeness for the model's behavior. To be able to find the best partitioning, we show how to prune sub-optimal solutions using a statistical test. This not only improves runtime but also helps to avoid explaining spurious interactions. Experiments show that iShap represents underlying modeling more accurately than Shap and nShap, and a user study suggests that iShap is perceived as more interpretable and trustworthy.
ChatPD: An LLM-driven Paper-Dataset Networking System
Authors: Anjie Xu, Ruiqing Ding, Leye Wang
Venue: KDD
Year: 2025

Scientific research heavily depends on suitable datasets for method validation, but existing academic platforms with dataset management like PapersWithCode suffer from inefficiencies in their manual workflow. To overcome this bottleneck, we present a system, called ChatPD, that utilizes Large Language Models (LLMs) to automate dataset information extraction from academic papers and construct a structured paper-dataset network. Our system consists of three key modules: paper collection, dataset information extraction, and dataset entity resolution to construct paper-dataset networks. Specifically, we propose a Graph Completion and Inference strategy to map dataset descriptions to their corresponding entities. Through extensive experiments, we demonstrate that ChatPD not only outperforms the existing platform PapersWithCode in dataset usage extraction but also achieves about 90% precision and recall in entity resolution tasks. Moreover, we have deployed ChatPD to continuously extract which datasets are used in papers, and provide a dataset discovery service, such as task-specific dataset queries and similar dataset recommendations. We open source ChatPD and the current paper-dataset network on GitHub.
Disclosing Actual Controller based on Equity Knowledge Graph Learning
Authors: Qingying Xu, Liang Hong, Mingxuan Shen, Baokun Yi
Venue: KDD
Year: 2025

Disclosing Actual Controllers (ACs) of a company has been the basis for financial risk governance. A shareholder in a winning stable coalition, where members make consistent decisions and win in votes, is considered an AC. However, existing methods fail to discover stable coalitions due to the ignorance of various relations other than the shareholding relation among shareholders, such as kinship, subsidiary and so on. Moreover, the above relations form a large-scale equity network, which brings challenges for efficiently identifying winning stable coalitions. We construct an Equity Knowledge Graph (EKG) to represent the semantic and structural information of the equity network. In this paper, we propose an AC disclosure method based on Equity Knowledge Graph Learning (EKGL). Specifically, to discover stable coalitions, EKGL designs a multi-relational aggregation module to aggregate the information of different relations horizontally. Based on the aggregated information, EKGL leverages a metapath-based aggregation module to encode the shareholding structure by capturing different shareholding paths on EKG vertically. To identify winning stable coalitions, we propose a control neural network to simulate the voting process of shareholders. Experiments and a case study on the EKG constructed from real datasets demonstrate that EKGL outperforms baselines by achieving 0.33 improvement in F1 score and reducing time cost.
KDD 2025 Workshop on Inference Optimization for Generative AI
Authors: Panpan Xu, Youngsuk Park, Lin Lee Cheong, Yida Wang, Yiying Zhang, George Karypis, Sherry Marcus
Venue: KDD
Year: 2025

The demand for efficient Large Language Model (LLM) inference has surged with the rising adoption of Generative AI (GenAI) applications, particularly in areas such as agents and retrieval-augmented generation. Efficient inference serves two crucial purposes: it enables the deployment of LLM-centered applications that address critical business needs, while also facilitating rapid experimentation for researchers to extract valuable insights and new understandings. However, despite the field's rapid advancement and interdisciplinary nature, there remains a limited exchange of ideas and methodologies between production-facing practitioners and researchers seeking to experiment with new GenAI concepts quickly. To bridge this gap, we are introducing the first KDD workshop on Inference Optimization for Generative AI. Our goal is to create a collaborative platform where researchers and practitioners working across various use cases and stacks of efficient inference can come together to exchange research ideas, establish connections between different disciplines, and identify challenges and research questions that will shape future work.
Mutual Information-aware Knowledge Distillation for Short Video Recommendation
Authors: Han Xu, Taoxing Pan, Zhiqiang Liu, Xiaoxiao Xu
Venue: KDD
Year: 2025

Short-video sharing platforms engaging billions of users have attracted intense interest recently. A key insight is that user feedback on these platforms is heavily influenced by preceding exposed videos in the same request, called context cumulative effects. For example, multiple repeated videos in a request often cause user fatigue and influence user feedback. However, related factors, such as the other exposed items in the same request, are available during model training but not accessible during online serving. Vanilla distillation methods mitigate the training-inference inconsistency, struggling to capture the dynamic dependence between context cumulative effects and user feedback. To address this problem, we propose the Mutual Information-aware Knowledge Distillation (MIKD) framework, which fuses such effects and user-item matching degrees by evaluating their impacts on user feedback based on mutual information estimation. Rigorous analysis and extensive experiments demonstrate that MIKD precisely extracts personal interests and consistently improves performance. We conduct online A/B testing on a leading short-video sharing mobile app, and the results demonstrate the effectiveness of the proposed method. MIKD has been successfully deployed online to serve the main traffic and optimize user experiences.
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
Authors: Xiang Xu, Hao Wang, Wei Guo, Luankang Zhang, Wanshan Yang, Runlong Yu, Yong Liu, Defu Lian, Enhong Chen
Venue: KDD
Year: 2025

Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. Current long-term user behavior modeling algorithms predominantly follow two cascading stages. The first stage retrieves subsequence related to the target item from the long-term behavior sequence, while the second stage models the relationship between the subsequence and the target item. Despite significant progress, these methods have two critical flaws. First, the retrieval query typically includes only target item information, limiting the ability to capture the user's diverse interests. Second, relational information, such as sequential and interactive information within the subsequence, is frequently overlooked. Therefore, it requires to be further mined to more accurately model user interests. To this end, we propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN). Specifically, we first construct queries based on behaviors observed at different time scales to obtain subsequences, each capturing users' interest at various granularities. We then introduce an noval multi-head Fourier transformer to efficiently learn sequential and interactive information within the subsequences, leading to more accurate modeling of user interests. Finally, we employ multi-head target attention to adaptively assess the impact of these multi-granularity interests on the target item. Extensive experiments have demonstrated that MIRRN significantly outperforms state-of-the-art baselines. Furthermore, an A/B test shows that MIRRN increases the average number of listening songs by 1.32% and the average time of listening songs by 0.55% on the Huawei Music App. The implementation code is publicly available at https://github.com/USTC-StarTeam/MIRRN.
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
Authors: Xuan Xu, Fufang Wen, Beilin Chu, Zhibing Fu, Qinhong Lin, Jiaqi Liu, Binjie Fei, Yu Li, Linna Zhou, Zhongliang Yang
Venue: KDD
Year: 2025

In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs' practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much higher computational resources, such as market sentiment analysis in financial reports; (2) Application on generative tasks heavily relies on retrieval augmented generation (RAG) methods to provide current and specialized information, with general retrievers showing suboptimal performance on domain-specific retrieval tasks; (3) There are additional inadequacies in other feature-based scenarios, such as topic modeling. We introduce FinBERT2, a specialized bidirectional encoder pretrained on a high-quality, financial-specific corpus of 32b tokens. This represents the largest known Chinese financial pretraining corpus for models of this parameter size. As a better backbone, FinBERT2 can bridge the gap in the financial-specific deployment of LLMs through the following achievements: (1) Discriminative fine-tuned models (Fin-Labelers) outperform other (Fin)BERT variants by 0.4%-3.3% and leading LLMs by 9.7%-12.3% on average across five financial classification tasks. (2) Contrastive fine-tuned models (Fin-Retrievers) outperform both open-source (e.g., +6.8% avg improvement over BGE-base-zh) and proprietary (e.g., +4.2% avg improvement over OpenAI's text-embedding-3-large) embedders across five financial retrieval tasks; (3) Building on FinBERT2 variants, we construct the Fin-TopicModel, which enables superior clustering and topic representation for financial titles. Our work revisits financial BERT models through comparative analysis with contemporary LLMs and offers practical insights for effectively utilizing FinBERT in the LLMs era.
ScalaGBM: Memory Efficient GBDT Training for High-Dimensional Data on GPU
Authors: Borui Xu, Zeyi Wen, Yao Chen, Weiguo Liu, Weng-Fai Wong, Bingsheng He
Venue: KDD
Year: 2025

Gradient Boosted Decision Trees (GBDTs) are classical machine learning algorithms widely employed in recommendation systems, database queries, etc. Due to the extensive memory access involved in histogram-based GBDT training methods, high-bandwidth GPUs have been widely adopted to accelerate the training. However, when handling millions of feature data, it requires significant memory to store the training data and histograms, posing challenges for training on limited GPU memories. In this paper, we develop a GPU-based GBDT framework named ScalaGBM, aiming to accelerate high-dimensional data training with less memory usage. We first employ a CSR-like data format and CSR-based histogram construction to reduce the memory occupation of the training data. Then, we reorganize the training workflow with a double buffer structure to reduce the overall memory consumption for the histogram. Finally, we develop multi-dimensional parallel histogram construction and global optimal split point reduction to speed up the training process. Experimental results demonstrate that ScalaGBM handles real-world datasets with over 100 million instances of 50 million features with a single commercial GPU while existing GBDT frameworks all run into out-of-memory errors. Meanwhile, ScalaGBM achieves a maximum speedup of 39× over state-of-the-art GBDT counterparts without sacrificing the training quality. The code is available at https://github.com/Xtra-Computing/thundergbm.
Dual Structure-guided Contrastive Network for Incomplete Multi-view Partial Multi-label Classification
Authors: Kaixin Xu, Yangyang Wu, Shijun Wu, Xiaoye Miao, Guoqing Chao, Mengying Zhu, Meng Xi, Xinkui Zhao
Venue: KDD
Year: 2025

Incomplete multi-view partial multi-label classification (IMvPMLC), which tackles the combined challenges of incompleteness in both multi-view and multi-label problems, has drawn considerable attention. Existing IMvPMLC methods have made progress but still face several challenges: (i) They mainly focus on the consistency of representations across multiple views but overlook the relationships among instances, leading to suboptimal representations. (ii) They primarily utilize only the available labels for supervised learning, ignoring the missing label distribution and limiting their ability to capture label correlations. In this paper, we propose a novel model named Dual Structure-guided Contrastive Network (DSCN) for IMvPMLC. Specifically, we introduce a similarity-guided instance-level contrastive learning mechanism to achieve multi-view consistent and discriminative representations across instances by leveraging instance structures, while a multi-view attention-based fusion strategy dynamically facilitates the fusion of multi-view representations to derive a robust consensus representation. Then, we design a multi-view shared classifier integrated with a correlation-guided label-level contrastive learning mechanism to enhance predictions by leveraging complementary information across multiple views and capturing label structures, effectively exploiting missing label distribution. Extensive experiments on five benchmark datasets demonstrate that, DSCN yields a more than 13% accuracy, compared with the state-of-the-art approaches. The code and datasets are available at https://anonymous.4open.science/r/DSCN-D471.
A/B Test and Online Experiment Under Diminishing Marginal Effects: Regret Minimization and Statistical Inference
Authors: Jingxu Xu, Yuhang Wu, Yingfei Wang, Chu Wang, Zeyu Zheng
Venue: KDD
Year: 2025

When large online platforms test a new strategy to implement with their user traffic, the phenomenon of diminishing marginal effects may arise. For example, when a strategy is implemented on 100% of the user traffic, the expected per-user effect can be lower compared to the expected per-user effect when a strategy is implemented on 10% of the user traffic, potentially due to limits of overall budget, resource, attention or content involved with that strategy. This diminishing marginal effect phenomenon brings an additional delicacy to online sequential experiments. In particular, for the classical goal of achieving the largest expected reward, the optimal decision may no longer be assigning 100% traffic to one strategy, but instead a mixture of strategies. We deliver two tasks for online sequential experiments in presence of diminishing marginal effect: (1) Adaptively identify the optimal traffic allocation to maximize the expected cumulative reward and (2) Construct valid central limit theorem (which is critically needed for A/B tests in online platforms) to perform reliable statistical inference for the expected reward under the optimal traffic allocation that is a priori unknown. We show that classical algorithms can fail to deliver the second task, especially because the statistical inference task presents its own difficulty. We develop a new online algorithm that leverages an additional smoothness condition on how the marginal effects change to achieve both tasks. We prove that this algorithm obtains the best achievable expected cumulative reward. Further, crucially for online platforms' need to do trustworthy statistical inference, the algorithm is proved to enjoy a valid central limit theorem. The theoretical findings are illustrated through numerical experiments.
Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling
Authors: Bihan Xu, Shiwei Zhao, Runze Wu, Zhenya Huang, Jiawei Wang, Zhipeng Hu, Kai Wang, Haoyu Liu, Tangjie Lv, Le Li, Changjie Fan, Xin T. Tong, Jiangze Han
Venue: KDD
Year: 2025

Within the domain of Massively Multiplayer Online (MMO) economy research, Agent-Based Modeling (ABM) has emerged as a robust tool for analyzing game economics, evolving from rule-based agents to decision-making agents enhanced by reinforcement learning. Nevertheless, existing works encounter significant challenges when attempting to emulate human-like economic activities among agents, particularly regarding agent reliability, sociability, and interpretability.In this study, we take a preliminary step in introducing a novel approach using Large Language Models (LLMs) in MMO economy simulation. Leveraging LLMs' role-playing proficiency, generative capacity, and reasoning aptitude, we design LLM-driven agents with human-like decision-making and adaptability. These agents are equipped with the abilities of role-playing, perception, memory, and reasoning, addressing the aforementioned challenges effectively. Simulation experiments focusing on in-game economic activities demonstrate that LLM-empowered agents can promote emergent phenomena like role specialization and price fluctuations in line with market rules.
Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows
Authors: Mengtao Yan, Qi Wang, Haining Wang, Ruizhi Chengze, Yi Zhang, Hongsheng Liu, Zidong Wang, Fan Yu, Qi Qi, Hao Sun
Venue: KDD
Year: 2025

Simulation of fluid flows is crucial for modeling physical phenomena like meteorology, aerodynamics, and biomedicine. Classical numerical solvers often require fine spatiotemporal grids to satisfy stability, consistency, and convergence conditions, leading to substantial computational costs. Although machine learning has demonstrated better efficiency, they typically suffer from issues of interpretability, generalizability, and data dependency. Hence, we propose a learnable and differentiable finite volume solver, called LDSolver, designed for efficient and accurate simulation of fluid flows on spatiotemporal coarse grids. LDSolver comprises two key components: (1) a differentiable finite volume solver, and (2) an learnable module providing equivalent approximation for fluxes (derivatives and interpolations), and temporal error correction on coarse grids. Even with limited training data (e.g., only a few trajectories), our model could accelerate the simulation while maintaining a high accuracy with superior generalizability. Experiments on different flow systems (e.g., Burgers, decaying, forced and shear flows) show that LDSolver achieves state-of-the-art performance, surpassing baseline models with notable margins.