OpenRaiser/Intern-Atlas huggingface dataset:
Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Authors: Yuanpeng He, Lijian Li, Tianxiang Zhan, Chi-Man Pun, Wenpin Jiao, Zhi Jin
Venue: KDD
Year: 2025

Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is currently a lack of effective strategies that can explore the uncertain aspects of both simultaneously. To address the aforementioned issues, we propose Evidential Prototype Learning (EPL), which utilizes an extended probabilistic framework to effectively fuse voxel-level evidential predictions from different classifiers and achieves prototype fusion utilization of labeled and unlabeled data under a generalized evidential framework, leveraging voxel-level dual uncertainty masking. The uncertainty measure not only enables the model to self-correct predictions but also improves the guided learning process with pseudo-labels and is able to feed back into the construction of hidden features. The method proposed in this paper has been experimented on LA, Pancreas-CT and TBAD datasets, achieving the state-of-the-art performance in three different labeled ratios, which strongly demonstrates the effectiveness of our strategy. The source code will be made publicly available.
Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery
Authors: Buyun He, Xiaorui Jiang, Qi Wu, Hao Liu, Yingguang Yang, Yong Liao
Venue: KDD
Year: 2025

Detecting social media bots is essential for maintaining the security and trustworthiness of social networks. While contemporary graph-based detection methods demonstrate promising results, their practical application is limited by label reliance and poor generalization capability across diverse communities. Generative Graph Self-Supervised Learning (GSL) presents a promising paradigm to overcome these limitations, yet existing approaches predominantly follow the homophily assumption and fail to capture the global patterns in the graph, which potentially diminishes their effectiveness when facing the challenges of interaction camouflage and distributed deployment in bot detection scenarios. To this end, we propose BotHP, a generative GSL framework tailored to boost graph-based bot detectors through heterophily-aware representation learning and prototype-guided cluster discovery. Specifically, BotHP leverages a dual-encoder architecture, consisting of a graph-aware encoder to capture node commonality and a graph-agnostic encoder to preserve node uniqueness. This enables the simultaneous modeling of both homophily and heterophily, effectively countering the interaction camouflage issue. Additionally, BotHP incorporates a prototype-guided cluster discovery pretext task to model the latent global consistency of bot clusters and identify spatially dispersed yet semantically aligned bot collectives. Extensive experiments on two real-world bot detection benchmarks demonstrate that BotHP consistently boosts graph-based bot detectors, improving detection performance, alleviating label reliance, and enhancing generalization capability.
Hyperbolic Deep Learning for Foundation Models: A Survey
Authors: Neil He, Hiren Madhu, Ngoc Bui, Menglin Yang, Rex Ying
Venue: KDD
Year: 2025

Foundation models pre-trained on massive datasets, including large language models (LLMs), vision-language models (VLMs), and large multimodal models, have demonstrated remarkable success in diverse downstream tasks. However, recent studies have shown fundamental limitations of these models: (1) limited representational capacity(2) lower adaptability, and (3) diminishing scalability. These shortcomings raise a critical question: is Euclidean geometry truly the optimal inductive bias for all foundation models, or could incorporating alternative geometric spaces enable models to better align with the intrinsic structure of real-world data and improve reasoning processes? Hyperbolic spaces, a class of non-Euclidean manifolds characterized by exponential volume growth with respect to distance, offer a mathematically grounded solution. These spaces enable low-distortion embeddings of hierarchical structures (e.g., trees, taxonomies) and power-law distributions with substantially fewer dimensions compared to Euclidean counterparts. Recent advances have leveraged these properties to enhance foundation models, including improving LLMs' complex reasoning ability, VLMs' zero-shot generalization, and cross-modal semantic alignment, while maintaining parameter efficiency. This paper provides a comprehensive review of hyperbolic neural networks and their recent development for foundation models. We further outline key challenges and research directions to advance the field.
Explaining GNN Explanations with Edge Gradients
Authors: Jesse He, Akbar Rafiey, Gal Mishne, Yusu Wang
Venue: KDD
Year: 2025

In recent years, the remarkable success of graph neural networks (GNNs) on graph-structured data has prompted a surge of methods for explaining GNN predictions. However, the state-of-the-art for GNN explainability remains in flux. Different comparisons find mixed results for different methods, with many explainers struggling on more complex GNN architectures and tasks. This presents an urgent need for a more careful theoretical analysis of competing GNN explanation methods. In this work we take a closer look at GNN explanations in two different settings: input-level explanations, which produce explanatory subgraphs of the input graph, and layerwise explanations, which produce explanatory subgraphs of the computation graph. We establish the first theoretical connections between the popular perturbation-based and classical gradient-based methods, as well as point out connections between other recently proposed methods. At the input level, we demonstrate conditions under which GNNExplainer can be approximated by a simple heuristic based on the sign of the edge gradients. In the layerwise setting, we point out that edge gradients are equivalent to occlusion search for linear GNNs. Finally, we demonstrate how our theoretical results manifest in practice with experiments on both synthetic and real datasets.
Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs
Authors: Morgan Lindsay Heisler, Linzi Xing, Ge Shi, Hanieh Sadri, Gursimran Singh, Weiwei Zhang, Tao Ye, Ying Xiong, Yong Zhang, Zhenan Fan
Venue: KDD
Year: 2025

Huawei Cloud users leverage LoRA (Low-Rank Adaptation) as an efficient and scalable method to fine-tune and customize large language models (LLMs) for application-specific needs. However, tasks that require complex reasoning or deep contextual understanding are often hindered by biases or interference from the base model when using typical decoding methods like greedy or beam search. These biases can lead to generic or task-agnostic responses from the base model instead of leveraging the LoRA-specific adaptations. In this paper, we introduce Contrastive LoRA Decoding (CoLD), a novel decoding framework designed to maximize the use of task-specific knowledge in LoRA-adapted models, resulting in better downstream performance. CoLD utilizes contrastive decoding by scoring candidate tokens based on the divergence between the probability distributions of a LoRA-adapted expert model and the corresponding base model. This approach prioritizes tokens that better align with the LoRA's learned representations, enhancing performance for specialized tasks. Despite its effectiveness, a naive implementation of CoLD can be computationally expensive. Each decoding step requires evaluating multiple token candidates across both the base and expert models, creating significant inefficiencies. To address this challenge, we developed an optimized kernel for Huawei's Ascend NPU. CoLD demonstrates consistent performance improvements, achieving up to a 5.54% increase in task accuracy while reducing end-to-end latency by 28% compared to greedy decoding. This work offers valuable insights into practical and efficient decoding strategies for fine-tuned LLMs, particularly in resource-constrained environments, and has broad implications for applied data science in both cloud and on-premises settings.
Depth-Adaptive Graph Neural Networks via Learnable Bakry-Émery Curvature
Authors: Asela Hevapathige, Ahad N. Zehmakan, Qing Wang
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities for graph-based tasks. Recent advances on GNNs leverage geometric properties, such as curvature, to enhance their representation capabilities by modeling complex connectivity patterns and information flow within graphs. However, most existing approaches primarily focus on discrete graph topology, overlooking diffusion dynamics and task-specific dependencies essential for effective learning. To address this, we propose a learnable integration of Bakry-Émery curvature, which captures both structural and diffusion aspects of information propagation. We develop an efficient, learnable approximation strategy, making curvature computation scalable for large graphs. Furthermore, we introduce an adaptive depth mechanism that dynamically adjusts message-passing layers per vertex based on its curvature, ensuring efficient propagation. Our theoretical analysis establishes a link between curvature and feature distinctiveness, showing that high-curvature vertices require fewer layers, while low-curvature ones benefit from deeper propagation. Extensive experiments on diverse downstream tasks validate the effectiveness of our approach, showing that the proposed depth-adaptive mechanism consistently uplifts the performance of a wide range of GNN architectures.
D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
Authors: Shingo Higashiguchi, Yasuko Matsubara, Koki Kawabata, Taichi Murayama, Yasushi Sakurai
Venue: KDD
Year: 2025

Large quantities of social activity data, such as weekly web search volumes and the number of new infections with infectious diseases, reflect peoples' interests and activities. It is important to discover temporal patterns from such data and to forecast future activities accurately. However, modeling and forecasting social activity data streams is difficult because they are high-dimensional and composed of multiple time-varying dynamics such as trends, seasonality, and interest diffusion. In this paper, we propose D-Tracker, a method for continuously capturing time-varying temporal patterns within social activity tensor data streams and forecasting future activities. Our proposed method has the following properties: (a) Interpretable: it incorporates the partial differential equation into a tensor decomposition framework and captures time-varying temporal patterns such as trends, seasonality, and interest diffusion between locations in an interpretable manner; (b) Automatic: it has no hyperparameters and continuously models tensor data streams fully automatically; (c) Scalable: the computation time of D-Tracker is independent of the time series length. Experiments using web search volume data obtained from GoogleTrends, and COVID-19 infection data obtained from COVID-19 Open Data Repository show that our method can achieve higher forecasting accuracy in less computation time than existing methods while extracting the interest diffusion between locations. Our source code and datasets are available at https://github.com/Higashiguchi-Shingo/D-Tracker.
LLM4HAR: Generalizable On-device Human Activity Recognition with Pretrained LLMs
Authors: Zhiqing Hong, Yiwei Song, Zelong Li, Anlan Yu, Shuxin Zhong, Yi Ding, Tian He, Desheng Zhang
Venue: KDD
Year: 2025

A long-standing challenge for pushing sensor-based human activity recognition (HAR) to industrial usage is the distribution shift between training data and testing data: significant variations in data distribution lead to a notable decline in performance. Recently, Large Language Models (LLMs) have demonstrated exceptional generalization capability, which provides a new opportunity to mitigate the distribution shift problem of HAR. However, since LLMs are inherently designed and trained on textual data, their potential to enhance generalization in HAR applications remains an open question. In this paper, we introduce LLM4HAR, a novel LLM-based model to improve cross-domain HAR. LLM4HAR consists of three main modules: (i) the Sensor Data Adaptation module, which aligns IMU signals with LLMs via sensor embedding(ii) the Sensor Knowledge Learning module, which injects sensor knowledge into LLMs for activity recognition, and (iii) the Efficiency Enhancement module, which employs a partial training strategy and reduces the model size by more than 10 times. Extensive evaluations show that LLM4HAR outperforms the existing methods by 13.82% in average F1 score, demonstrating the feasibility and effectiveness of transferring knowledge from pretrained LLMs to enhance HAR. Further, LLM4HAR has been adopted by JD Logistics to support downstream applications such as Courier Welfare Improvement and Map Data Generation.
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models
Authors: Min Hou, Yueying Wu, Chang Xu, Yu-Hao Huang, Chenxi Bai, Le Wu, Jiang Bian
Venue: KDD
Year: 2025

As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-dimensional data distribution in an unsupervised manner. Despite their success, diffusion models are highly data-driven and prone to inheriting the imbalances and biases present in real-world data. Some studies have attempted to address these issues by designing text prompts for known biases or using bias labels to construct unbiased data. While these methods have shown improved results, real-world scenarios often contain various unknown biases, and obtaining bias labels is particularly challenging. In this paper, we emphasize the necessity of mitigating bias in pre-trained diffusion models without relying on auxiliary bias annotations. To tackle this problem, we propose a framework, InvDiff, which aims to learn invariant semantic information for diffusion guidance. Specifically, we propose identifying underlying biases in the training data and designing a novel debiasing training objective. Then, we employ a lightweight trainable module that automatically preserves invariant semantic information and uses it to guide the diffusion model's sampling process toward unbiased outcomes simultaneously. Notably, we only need to learn a small number of parameters in the lightweight learnable module without altering the pre-trained diffusion model. Furthermore, we provide a theoretical guarantee that the implementation of InvDiff is equivalent to reducing the error upper bound of generalization. Extensive experimental results on three publicly available benchmarks demonstrate that InvDiff effectively reduces biases while maintaining the quality of image generation. Our code is available at https://github.com/Hundredl/InvDiff.
Generating Long Semantic IDs in Parallel for Recommendation
Authors: Yupeng Hou, Jiacheng Li, Ashley Shin, Jinsung Jeon, Abhishek Santhanam, Wei Shao, Kaveh Hassani, Ning Yao, Julian J. McAuley
Venue: KDD
Year: 2025

Semantic ID-based recommendation models tokenize each item into a small number of discrete tokens that preserve specific semantics, leading to better performance, scalability, and memory efficiency. While recent models adopt a generative approach, they often suffer from inefficient inference due to the reliance on resource-intensive beam search and multiple forward passes through the neural sequence model. As a result, the length of semantic IDs is typically restricted (e.g., to just 4 tokens), limiting their expressiveness. To address these challenges, we propose RPG, a lightweight framework for semantic ID-based recommendation. The key idea is to produce unordered, long semantic IDs, allowing the model to predict all tokens in parallel. We train the model to predict each token independently using a multi-token prediction loss, directly integrating semantics into the learning objective. During inference, we construct a graph connecting similar semantic IDs and guide decoding to avoid generating invalid IDs. Experiments show that scaling up semantic ID length to 64 enables RPG to outperform generative baselines by an average of 12.6% on the NDCG@10, while also improving inference efficiency. Code is available at: https://github.com/facebookresearch/RPG_KDD2025.
TransPlace: Transferable Circuit Global Placement via Graph Neural Network
Authors: Yunbo Hou, Haoran Ye, Shuwen Yang, Yingxue Zhang, Siyuan Xu, Guojie Song
Venue: KDD
Year: 2025

Global placement, a critical step in designing the physical layout of computer chips, is essential to optimize chip performance. Prior global placement methods optimize each circuit design individually from scratch. Their neglect of transferable knowledge limits solution efficiency and chip performance as circuit complexity drastically increases. This study presents TransPlace, a global placement framework that learns to place millions of mixed-size cells in continuous space. TransPlace introduces i) Netlist Graph to efficiently model netlist topology, ii) Cell-flow and relative position encoding to learn SE(2)-invariant representation, iii) a tailored graph neural network architecture for informed parameterization of placement knowledge, and iv) a two-stage strategy for coarse-to-fine placement. Compared to state-of-the-art placement methods, TransPlace-trained on a few high-quality placements-can place unseen circuits with 1.2x speedup while reducing congestion by 30%, timing by 9%, and wirelength by 5%.
Multi-level Matching Network for Multimodal Entity Linking
Authors: Zhiwei Hu, Víctor Gutiérrez-Basulto, Ru Li, Jeff Z. Pan
Venue: KDD
Year: 2025

Multimodal entity linking (MEL) aims to link ambiguous mentions within multimodal contexts to corresponding entities in a multimodal knowledge base. Most existing approaches to MEL are based on representation learning or vision-and-language pre-training mechanisms for exploring the complementary effect among multiple modalities. However, these methods suffer from two limitations. On the one hand, they overlook the possibility of considering negative samples from the same modality. On the other hand, they lack mechanisms to capture bidirectional cross-modal interaction. To address these issues, we propose a Multi-level Matching network for Multimodal Entity Linking(M3EL). Specifically, M3EL is composed of three different modules: (i) a Multimodal Feature Extraction module, which extracts modality-specific representations with a multimodal encoder and introduces an intra-modal contrastive learning sub-module to obtain better discriminative embeddings based on uni-modal differences; (ii) an Intra-modal Matching Network module, which contains two levels of matching granularity: Coarse-grained Global-to-Global and Fine-grained Global-to-Local, to achieve local and global level intra-modal interaction; (iii) a Cross-modal Matching Network module, which applies bidirectional strategies, Textual-to-Visual and Visual-to-Textual matching, to implement bidirectional cross-modal interaction. Extensive experiments conducted on WikiMEL, RichpediaMEL, and WikiDiverse datasets demonstrate the outstanding performance of M3EL when compared to the state-of-the-art baselines.
Multi-level Mixture of Experts for Multimodal Entity Linking
Authors: Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan
Venue: KDD
Year: 2025

Multimodal Entity Linking (MEL) aims to link ambiguous mentions within multimodal contexts to associated entities in a multimodal knowledge base. Existing approaches to MEL introduce multimodal interaction and fusion mechanisms to bridge the modality gap and enable multi-grained semantic matching. However, they do not address two important problems: (i) mention ambiguity, i.e., the lack of semantic content caused by the brevity and omission of key information in the mention's textual context; (ii) dynamic selection of modal content, i.e., to dynamically distinguish the importance of different parts of modal information. To mitigate these issues, we propose a Multi-level Mixture of Experts (MMoE) model for MEL. MMoE has four components: (i) the description-aware mention enhancement module leverages large language models to identify the WikiData descriptions that best match a mention, considering the mention's textual context; (ii) the multimodal feature extraction module adopts multimodal feature encoders to obtain textual and visual embeddings for both mentions and entities; (iii)-(iv) the intra-level mixture of experts and inter-level mixture of experts modules apply a switch mixture of experts mechanism to dynamically and adaptively select features from relevant regions of information. Extensive experiments on WikiMEL, RichpediaMEL and WikiDiverse datasets demonstrate the outstanding performance of MMoE compared to the state-of-the-art. MMoE's code is available at: https://github.com/zhiweihu1103/MEL-MMoE.
Learning Adaptive Reserve Price in Display Advertising
Authors: Kun Hu, Shumin Zhang, Lixia Wu, Yongjun Dai, Minfang Lu, Yuting Qiang, Minglong Li
Venue: KDD
Year: 2025

Real-Time Bidding (RTB) is a trading mechanism that allocates advertising (ad) requests through online auctions. Participants in these auctions typically include an ad exchange (AdX) and several demand-side platforms (DSPs). When an RTB auction begins, the AdX first establishes the reserve price set by publishers as the starting bid, after which the DSPs bid to compete for potential ad impressions. The reserve price strategy is crucial to the ad revenue of publishers; however, due to the strategic and dynamic bidding behavior of DSPs, optimizing the reserve price presents a significant challenge. In this work, we report a novel adaptive reserve price strategy based on reinforcement learning (RL). In our scheme, value bucket identification is leveraged to estimate the intrinsic values of ad inventories. Following this estimation, specialized reward functions are utilized to generate informative reward signals for RL models. Furthermore, we study the issue of risk management on the publisher side and develop a risk-aware instantiation to model risk tendency, considering both empirical expert knowledge and real-time trading conditions. Extensive experiments using real-world datasets collected from operational environments have demonstrated the effectiveness of the proposed method.
KDD 2025 - AI Reasoning Day
Authors: Jun Huan, Xiangyu Zhang, Ye Xing, Wee Hyong Tok, Ruzica Piskac
Venue: KDD
Year: 2025

Generative AI and the use of large language models (LLMs) are changing the way we work, create, play, and live. As we have witnessed in the past few years, there is significant progress in training LLMs to have a deep understanding of the semantics of language so that such models begin to perform ''reasoning''. (Human) Reasoning is the process of applying logic to derive conclusions based on new or existing information with the goal of finding the truth. Reasoning is a form of high-level human intelligence. There are many types of reasoning: mathematical reasoning, common sense reasoning, temporal reasoning, among others. Multi-hope reasoning with LLM is an emerging capability for LLMs with tens of billions of parameters. Such ''reasoning models'', including Sonnet 3.7, Chat GPT O1, have powered important application areas such as AI4coding, agentic workflow, among others. The first KDD AI Reasoning Day is a special event that we organize in order to increase the awareness of this important research topic for the research community. We bring leaders from industry and academia to present the latest progresses on improving LLM's reasoning capability and enabling reasoning for different application development.