OpenRaiser/Intern-Atlas huggingface dataset:
Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations
Authors: Junhao Liu, Pengpeng Zhang, Martin Renqiang Min, Jing Zhang
Venue: KDD
Year: 2025

Cell fate decisions are highly coordinated processes governed by complex interactions among numerous regulatory genes, while disruptions in these mechanisms can lead to developmental abnormalities and disease. Traditional methods often fail to capture such combinatorial interactions, limiting their ability to fully model cell fate dynamics. Here, we introduce MetaVelo, a global feature explanation framework for identifying key regulatory gene sets influencing cell fate transitions. MetaVelo models these transitions as a black-box function and employs a differentiable neural ordinary differential equation (ODE) surrogate to enable efficient optimization. By reparameterizing the problem as a controllable data generation process, MetaVelo overcomes the challenges posed by the non-differentiable nature of cell fate dynamics. Benchmarking across diverse stand-alone and longitudinal single-cell RNA-seq datasets and three black-box cell fate models demonstrates its superiority over 12 baseline methods in predicting developmental trajectories and identifying combinatorial regulatory gene sets. MetaVelo further distinguishes independent from synergistic regulatory genes, offering novel insights into the gene interactions governing cell fate. With the growing availability of high-resolution single-cell data, MetaVelo provides a scalable and effective framework for advancing developmental biology and therapeutic applications.
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Authors: Ruofan Hu, Dongyu Zhang, Huayi Zhang, Elke A. Rundensteiner
Venue: KDD
Year: 2025

Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, this approach heavily depends on the availability of a clean labeled meta-dataset, which is difficult to obtain in practice. In this work, we thus tackle the challenge of meta-learning for noisy label scenarios without relying on a clean labeled dataset. Our approach leverages the data itself while bypassing the need for labels. Building on the insight that clean samples effectively preserve the consistency of related data structures across the last hidden and the final layer, whereas noisy samples disrupt this consistency, we design the Cross-layer Information Divergence-based Meta Update Strategy (CLID-MU). CLID-MU leverages the alignment of data structures across these diverse feature spaces to evaluate model performance and use this alignment to guide training. Experiments on benchmark datasets with varying amounts of labels under both synthetic and real-world noise demonstrate that CLID-MU outperforms state-of-the-art methods. The code is released at https://github.com/ruofanhu/CLID-MU.
Enhancer: A Distribution-Aware Framework with Temporal-Relational Meta-Learning for Stock Prediction
Authors: Weijun Chen, Shun Li, Heyuan Wang, Tengjiao Wang
Venue: KDD
Year: 2025

Accurate stock prediction is critical for portfolio management, where learning to adapt to market changes is the key to sustainable profitability. Financial markets, as complex interactive systems, exhibit evolution in both temporal dynamics and relational structures. While current temporal-relational models have achieved remarkable success in stock prediction, they face fundamental challenges in learning and adapting to market changes, particularly the systematic shifts in temporal and relational distributions that challenge the i.i.d. assumption underlying model training. In this study, we pioneer the research of temporal-relational distribution shifts in stock prediction and introduce Enhancer, a model-agnostic framework that can be applied to any downstream predictor. Enhancer adopts a meta-learning architecture featuring both a Temporal Meta-Learner (TML) and a Relational Meta-Learner (RML). Specifically, we introduce Reactive Point Processes Attention (RPPsAtt) within TML to overcome the limitations of missing fine-grained temporal point information, a common issue with prior methods that rely on distribution inference for mitigating temporal distribution shift. To enhance relational generalization, we introduce the Approximation-Intervention (Ant) mechanism within RML, marking the first method to mitigate relational distribution shift for quantitative investment. We conduct experiments on four long-term stock datasets, categorizing them into two tasks: stock trend prediction and stock investment recommendation. Our experimental results show that Enhancer achieves an average increase of 29.3% in profit ratio and 18.54% in the Sharpe ratio compared to the baselines across two tasks.
MedDiTPro: A Prompt-Guided Diffusion Transformer for Multimodal Longitudinal Medical Data Synthesis
Authors: Yuan Zhong, Xiaochen Wang, Jiaqi Wang, Xiaokun Zhang, Fenglong Ma
Venue: KDD
Year: 2025

Diffusion models have recently emerged as a state-of-the-art approach for synthetic Electronic Health Record (EHR) generation, offering superior fidelity and diversity over traditional generative models. However, existing diffusion-based methods struggle with unique challenges: limited representation learning and modality utilization, where they fail to explicitly capture inter-modality dependencies and fine-grained code-level interactions, and constrained adaptability due to reliance on U-Net-based architectures, which are not well-suited for handling the heterogeneous and evolving nature of EHR data. Furthermore, current evaluation paradigms rely on either perplexity-based sequence modeling or global distributional measures, lacking robustness in assessing both intra-visit code relationships and inter-visit temporal patterns. To address these limitations, we propose MedDiTPro, a diffusion transformer-based framework that enhances multimodal EHR generation by integrating structured modality-aware guidance. Through a unified transformer for intra-visit representation learning, a modality-specific and datawise prompt learner, and a diffusion transformer with structured guidance, MedDiTPro achieves state-of-the-art performance in generating diverse and clinically meaningful synthetic records. Extensive experiments on publicly available datasets demonstrate that MedDiTPro achieves state-of-the-art fidelity, privacy preservation, and utility.
EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens
Authors: Chaoqun Yang, Xinyu Lin, Wenjie Wang, Yongqi Li, Teng Sun, Xianjing Han, Tat-Seng Chua
Venue: KDD
Year: 2025

Large Language Model-based generative recommendation (LLMRec) has achieved notable success, but it suffers from high inference latency due to massive computational overhead and memory pressure of KV Cache. Existing KV Cache reduction methods face critical limitations: cache compression offers marginal acceleration given recommendation tasks' short decoding steps, while prompt compression risks discarding vital interaction history. Through systematic analysis of attention patterns in LLMRec, we uncover two pivotal insights: 1) layer-wise attention sparsity inversion where early layers retain dense informative patterns while later layers exhibit high redundancy, and 2) dual attention sinks phenomenon where attention scores concentrate on both head and tail tokens of input sequences. Motivated by these insights, we propose EARN, an efficient inference framework that leverages the early layers to compress information into register tokens placed at the input sequence boundaries, then focuses solely on these tokens in the subsequent layers. Extensive experiments on three datasets, two LLMRec methods and two LLM architectures demonstrate EARN's superiority, achieving up to 3.79x speedup and 80.8% KV Cache reduction with better accuracy than the general finetuning approach. Our work bridges the efficiency-effectiveness gap in LLMRec, offering practical deployment advantages for industrial scenarios.
Embedding Prior Task-specific Knowledge into Language Models for Context-aware Document Ranking
Authors: Shuting Wang, Yutao Zhu, Zhicheng Dou
Venue: KDD
Year: 2025

Exploiting users' contextual behaviors in the current session has been proven favorable to the document ranking task. Recently, the context-aware document ranking task has benefited from pre-trained language models (PLMs) due to their superior ability in language modeling. Most PLM-based context-aware document ranking models implicitly learn task-specific knowledge by fine-tuning PLMs on historical search logs. However, since search log data is noisy and contains various user intents and search patterns, such a black-box way may prevent models from fully mastering effective context-aware search knowledge. To solve this problem, we propose LOCK, a PLM-based context-aware document ranking model that explicitly embeds task-specific prior knowledge into PLMs to guide the model optimization. From local to global, we identify three types of task-specific knowledge, including intra-turn signals, inter-turn signals, and global session signals. LOCK formulates such prior knowledge into prior attention biases for impacting the fine-tuning of PLMs. This operation can guide the ranking model by task-specific prior knowledge, thereby improving model convergence and ranking ability. Additionally, we introduce a task-specific pre-training stage that involves masked language modeling and the soft reconstruction of the prior attention matrix, which helps the PLMs adapt to our task. Extensive experiments validate the effectiveness and convergence of our method.
Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation
Authors: Meng Qin, Jiahong Liu, Irwin King
Venue: KDD
Year: 2025

Graph neural networks (GNNs), which capture graph structures via a feature aggregation mechanism following the graph embedding framework, have demonstrated a powerful ability to support various tasks. According to the topology properties (e.g., structural roles or community memberships of nodes) to be preserved, graph embedding can be categorized into identity and position embedding. However, it is unclear for most GNN-based methods which property they can capture. Some of them may also suffer from low efficiency and scalability caused by several time- and space-consuming procedures (e.g., feature extraction and training). From a perspective of graph signal processing, we find that high- and low-frequency information in the graph spectral domain may characterize node identities and positions, respectively. Based on this investigation, we propose random feature aggregation (RFA) for efficient identity and position embedding, serving as an extreme ablation study regarding GNN feature aggregation. RFA (i) adopts a spectral-based GNN without learnable parameters as its backbone(ii) only uses random noises as inputs, and (iii) derives embeddings via just one feed-forward propagation (FFP). Inspired by degree-corrected spectral clustering, we further introduce a degree correction mechanism to the GNN backbone. Surprisingly, our experiments demonstrate that two variants of RFA with high- and low-pass filters can respectively derive informative identity and position embeddings via just one FFP (i.e., without any training). As a result, RFA can achieve a better trade-off between quality and efficiency for both identity and position embedding over various baselines. We have made our code public at https://github.com/KuroginQin/RFA
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
Authors: Jie Feng, Jun Zhang, Tianhui Liu, Xin Zhang, Tianjian Ouyang, Junbo Yan, Yuwei Du, Siqi Guo, Yong Li
Venue: KDD
Year: 2025

As large language models (LLMs) continue to advance and gain widespread use, establishing systematic and reliable evaluation methodologies for LLMs and vision-language models (VLMs) has become essential to ensure their real-world effectiveness and reliability. There have been some early explorations about the usability of LLMs for limited urban tasks, but a systematic and scalable evaluation benchmark is still lacking. The challenge in constructing a systematic evaluation benchmark for urban research lies in the diversity of urban data, the complexity of application scenarios and the highly dynamic nature of the urban environment. In this paper, we design CityBench, an interactive simulator based evaluation platform, as the first systematic benchmark for evaluating the capabilities of LLMs for diverse tasks in urban research. First, we build CityData to integrate the diverse urban data and CitySimu to simulate fine-grained urban dynamics. Based on CityData and CitySimu, we design 8 representative urban tasks in 2 categories of perception-understanding and decision-making as the CityBench. With extensive results from 30 well-known LLMs and VLMs in 13 cities around the world, we find that advanced LLMs and VLMs can achieve competitive performance in diverse urban tasks requiring commonsense and semantic understanding abilities, e.g., understanding the human dynamics and semantic inference of urban images. Meanwhile, they fail to solve the challenging urban tasks requiring professional knowledge and high-level numerical abilities, e.g., geospatial prediction and traffic control task. These findings provide critical insights for the effective utilization and further development of LLMs to advance urban-related tasks and research in the future.
SwitchTop-k: Scaling Top-k Compression on Programmable Switches
Authors: Yijun Li, Jiawei Huang, Jingling Liu, Zhaoyi Li, Wanchun Jiang, Jianxin Wang
Venue: KDD
Year: 2025

Distributed deep learning has been widely deployed in data centers to provide various services such as image classification and speech recognition. To reduce the training time, Top-k compression has become one of the most popular solutions used to shrink the data volume of gradients. Nevertheless, we observe that existing Top-k compression solutions are inefficient when used for large-scale distributed training due to gradient build-up, missing of Top-k gradients, and high compression overhead at the end hosts. To address these problems, we propose SwitchTop-k, which improves the accuracy of selecting Top-k values while ensuring a high compression rate and zero compression overhead. Specifically, SwitchTop-k offloads the Top-k compression from the end hosts to the programmable switches, thus alleviating the gradient build-up and compression overhead. Meanwhile, we propose a sketch-based solution to achieve high accuracy in selecting global Top-k gradients. We also co-design switch logic and end host logic to improve communication efficiency of uncompressed traffic. Finally, we implement SwitchTop-k on Intel Tofino switches and integrate it with Pytorch. The test results show that SwitchTop-k reduces iteration time by up to 91% compared with existing compression algorithms.
The 14th International Workshop on Urban Computing
Authors: Yuxuan Liang, Yu Zheng, Chuishi Meng, Yanhua Li, Jieping Ye, Philip S. Yu, Ouri Wolfson
Venue: KDD
Year: 2025

The swift advancement of urbanization has resulted in the growth of numerous large cities, which have enhanced the lives of many individuals but have also created significant challenges, such as air pollution, higher energy consumption, and traffic congestion. Addressing these issues was nearly unfeasible in the past due to the intricate and ever-changing nature of urban environments. Today, however, advancements in sensing technologies and extensive computing infrastructures have generated vast amounts of big data related to urban areas, including information on human mobility, air quality, traffic patterns, and geographic data. Inspired by the potential for creating smarter cities, we developed a vision for urban computing that seeks to harness insights from diverse and extensive data collected in urban settings, using this valuable information to tackle the critical problems our cities currently encounter.
Large Language Model Enhanced Recommender Systems: Methods, Applications and Trends
Authors: Qidong Liu, Xiangyu Zhao, Yuhao Wang, Yejing Wang, Zijian Zhang, Yuqi Sun, Xiang Li, Maolin Wang, Pengyue Jia, Chong Chen, Wei Huang, Feng Tian
Venue: KDD
Year: 2025

Due to exceptional reasoning and understanding abilities, the Large Language Model (LLM) has revolutionized the pattern of many fields, including recommender systems (RS). There has been a handful of research that focuses on empowering the RS by LLM. Recently, considering the latency and memory costs in real-world applications, LLM-Enhanced RS (LLMERS) is highlighted. This direction pushes the LLM into the online system with a large step by eliminating the utilization of LLM during inference. As a cutting-edge field, there is a clear need for a comprehensive survey to summarize this direction. In this survey, we systematically investigate the most up-to-date works of LLM-enhanced RS to boost this direction. Based on the component of an RS model that the LLM aims to augment, the basic taxonomy includes Knowledge Enhancement, Interaction Enhancement and Model Enhancement. Additionally, we identify several promising research directions. To facilitate access to the surveyed papers, we release a repository.
Test-Time Training with Diversified Local Aggregation Consistency for Mortality Prediction using Clinical Time Series
Authors: Jingwen Xu, Fei Lyu, Pong C. Yuen
Venue: KDD
Year: 2025

Mortality prediction is necessary for patients in the Intensive Care Unit (ICU). Clinical time series provide essential insights for making accurate predictions. However, existing prediction models often struggle with domain shifts when applied across different domains. Privacy concern hinders model calibration due to the forbidden data sharing across domains. Test-Time Training (TTT) has been increasingly researched to tackle the above issues by updating a source model to each single target sample before inference. While massive vision-based TTT methods are proposed, deploying TTT in clinical time series still faces the unique challenge of temporal imbalance: Time points tend to cluster around specific periods in some patients. Neglecting the temporal imbalance in TTT can make the model biased toward dense local pattern, resulting in unsatisfactory prediction. To overcome this challenge, we propose a novel Test-Time Training method with Diversified Local Aggregation Consistency (DLAC-TTT). During the test-time update, DLAC-TTT focuses on the distinct temporal distributions within each patient, enforcing their local diversity and global consistency through aggregation. In this way, it can mitigate the over-reliance on specific local patterns and well integrate diverse local patterns for global learning. Extensive experiments show that DLAC-TTT can boost the generalization performance across real-world clinical datasets from different medical institutes.
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis
Authors: Jiaqi Wang, Ziyi Yin, Quanzeng You, Lingjuan Lyu, Fenglong Ma
Venue: KDD
Year: 2025

Geographic health disparities pose a pressing global challenge, particularly in underserved regions of low- and middle-income nations. Addressing this issue requires a collaborative approach to enhance healthcare quality, leveraging support from medically more developed areas. Federated learning emerges as a promising tool for this purpose. However, the scarcity of medical data and limited computation resources in underserved regions make collaborative training of powerful machine learning models challenging. Furthermore, there exists an asymmetrical reciprocity between underserved and developed regions. To overcome these challenges, we propose a novel cross-silo federated learning framework, named FedHelp, aimed at alleviating geographic health disparities and fortifying the diagnostic capabilities of underserved regions. Specifically, FedHelp leverages foundational model knowledge via one-time API access to guide the learning process of underserved small clients, addressing the challenge of insufficient data. Additionally, we introduce a novel asymmetric dual knowledge distillation module to manage the issue of asymmetric reciprocity, facilitating the exchange of necessary knowledge between developed large clients and underserved small clients. We validate the effectiveness and utility of FedHelp through extensive experiments on both medical image classification and segmentation tasks. The experimental results demonstrate significant performance improvement compared to state-of-the-art baselines, particularly benefiting clients in underserved regions.
Breaking the Top-K Barrier: Advancing Top-K Ranking Metrics Optimization in Recommender Systems
Authors: Weiqin Yang, Jiawei Chen, Shengjia Zhang, Peng Wu, Yuegang Sun, Yan Feng, Chun Chen, Can Wang
Venue: KDD
Year: 2025

In the realm of recommender systems (RS), Top-K ranking metrics such as NDCG@K are the gold standard for evaluating recommendation performance. However, during the training of recommendation models, optimizing NDCG@K poses significant challenges due to its inherent discontinuous nature and the intricate Top-K truncation. Recent efforts to optimize NDCG@K have either overlooked the Top-K truncation or suffered from high computational costs and training instability. To overcome these limitations, we propose SoftmaxLoss@K (SL@K), a novel recommendation loss tailored for NDCG@K optimization. Specifically, we integrate the quantile technique to handle Top-K truncation and derive a smooth upper bound for optimizing NDCG@K to address discontinuity. The resulting SL@K loss has several desirable properties, including theoretical guarantees, ease of implementation, computational efficiency, gradient stability, and noise robustness. Extensive experiments on four real-world datasets and three recommendation backbones demonstrate that SL@K outperforms existing losses with a notable average improvement of 6.03%. The code is available at https://github.com/Tiny-Snow/IR-Benchmark.
A Scalable and Efficient Signal Integration System for Job Matching
Authors: Ping Liu, Rajat Arora, Xiao Shi, Benjamin Le, Qianqi Shen, Jianqiang Shen, Chengming Jiang, Nikita Zhiltsov, Priya Bannur, Yidan Zhu, Liming Dong, Haichao Wei, Qi Guo, Luke Simon, Liangjie Hong, Wenjing Zhang
Venue: KDD
Year: 2025

LinkedIn, one of the world's largest platforms for professional networking and job seeking, encounters various modeling challenges in building recommendation systems for its job matching product, including cold-start, filter bubbles, and biases affecting candidate-job matching. To address these, we developed the STAR (Signal integration for Talent And Recruiters) system, leveraging the combined strengths of Large Language Models (LLMs) and Graph Neural Networks (GNNs). LLMs excel at understanding textual data, such as member profiles and job postings, while GNNs capture intricate relationships and mitigate cold-start issues through network effects. STAR integrates diverse signals by uniting LLM and GNN capabilities with industrial-scale paradigms including adaptive sampling and version management. It provides an end-to-end solution for developing and deploying embeddings in large-scale recommender systems. Our key contributions include a robust methodology for building embeddings in industrial applications, a scalable GNN-LLM integration for high-performing recommendations, and practical insights for real-world model deployment.