OpenRaiser/Intern-Atlas huggingface dataset:
SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction
Authors: Changchang Yin, Shihan Fu, Bingsheng Yao, Thai-Hoang Pham, Weidan Cao, Dakuo Wang, Jeffrey M. Caterino, Ping Zhang
Venue: KDD
Year: 2025

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.
Self-Tuning Self-Supervised Image Anomaly Detection
Authors: Jaemin Yoo, Lingxiao Zhao, Leman Akoglu
Venue: KDD
Year: 2025

Self-supervised learning (SSL) has emerged as a promising paradigm that presents supervisory signals to real-world problems, bypassing the extensive cost of manual labeling. Consequently, self-supervised anomaly detection (SSAD) has seen a recent surge of interest, since SSL is especially attractive for unsupervised tasks. However, recent works have reported that the choice of a data augmentation function has significant impact on the accuracy of SSAD, posing augmentation search as an essential but nontrivial problem due to lack of labeled validation data. In this paper, we introduce ST-SSAD, the first unsupervised approach to end-to-end augmentation tuning for SSAD. To this end, our work presents two key contributions. The first is a new unsupervised validation loss that quantifies the alignment between augmented training data and unlabeled validation data. The second is new differentiable augmentation functions, allowing data augmentation hyperparameter(s) to be tuned in an end-to-end manner. Experiments on two testbeds with semantic class anomalies and subtle industrial defects show that ST-SSAD gives significant performance gains over existing works. All our code and testbeds are available at https://github.com/jaeminyoo/ST-SSAD.
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Authors: Tianrun Yu, Jiaqi Wang, Haoyu Wang, Mingquan Lin, Han Liu, Nelson S. Yee, Fenglong Ma
Venue: KDD
Year: 2025

Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate shift. We provide a theoretical analysis of this setting, which motivates the design of FedAKD (Federated Asynchronous Knowledge Distillation) - a simple yet effective approach that balances accurate prediction with collaborative fairness. FedAKD consists of client and server updates. In the client update, we introduce a novel asynchronous knowledge distillation strategy based on our preliminary analysis, which reveals that while correctly predicted samples exhibit similar feature distributions across clients, incorrectly predicted samples show significant variability. This suggests that imbalanced covariate shift primarily arises from misclassified samples. Leveraging this insight, our approach first applies traditional knowledge distillation to update client models while keeping the global model fixed. Next, we select the correctly predicted high-confidence samples and update the global model using these samples, while keeping the client models fixed. The server update simply aggregates all client models. We further provide a theoretical proof of FedAKD's convergence. Experimental results on both public datasets (FashionMNIST and CIFAR10) and a real-world Electronic Health Records (EHR) dataset demonstrate that FedAKD significantly improves collaborative fairness, enhances predictive accuracy, and fosters client participation, even under highly heterogeneous data distributions.
Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates
Authors: Chengqing Yu, Fei Wang, Chuanguang Yang, Zezhi Shao, Tao Sun, Tangwen Qian, Wei Wei, Zhulin An, Yongjun Xu
Venue: KDD
Year: 2025

Multivariate Time Series Forecasting (MTSF) involves predicting future values of multiple interrelated time series. Recently, deep learning-based MTSF models have gained significant attention for their promising ability to mine semantics (global and local information) within MTS data. However, these models are pervasively susceptible to missing values caused by malfunctioning data collectors. These missing values not only disrupt the semantics of MTS, but their distribution also changes over time. Nevertheless, existing models lack robustness to such issues, leading to suboptimal forecasting performance. To this end, in this paper, we propose Multi-View Representation Learning (Merlin), which can help existing models achieve semantic alignment between incomplete observations with different missing rates and complete observations in MTS. Specifically, Merlin consists of two key modules: offline knowledge distillation and multi-view contrastive learning. The former utilizes a teacher model to guide a student model in mining semantics from incomplete observations, similar to those obtainable from complete observations. The latter improves the student model's robustness by learning from positive/negative data pairs constructed from incomplete observations with different missing rates, ensuring semantic alignment across different missing rates. Therefore, Merlin is capable of effectively enhancing the robustness of existing models against unfixed missing rates while preserving forecasting accuracy. Experiments on four real-world datasets demonstrate the superiority of Merlin.
Instruction Semantics Enhanced Dual-Flow Graph Model for GPU Error Resilience Prediction
Authors: Pengfei Yu, Jingjing Gu, Dazhong Shen, Xin Dong, Yang Liu, Hui Xiong
Venue: KDD
Year: 2025

As GPUs are widely deployed in High Performance Computing systems, it is critical to ensure that these systems can perform reliably. To improve system reliability, researchers estimate the error resilience of GPU programs by understanding resilience characteristics or modeling error propagation. However, features indicative of resilience rely on manual extraction from simulations of numerous faults, and error propagation analysis cannot target fine-grained bit-level faults. To address those problems, this paper introduces a novel paradigm, namely InstrDGM, for efficiently predicting GPU error resilience. Specifically, InstrDGM first fine-tunes a large language model using extensive sequences of GPU assembly instructions for extracting the semantic representation of instructions automatically. Meanwhile, we consider the propagation of bit-level faults during instruction execution and data transfer processes, and leverage graph neural networks to capture their distinct error propagation patterns. Then, the fault embeddings extracted from these error propagation patterns are integrated for error resilience prediction. Additionally, this paper releases a new dataset for GPU error resilience assessment, containing 1.2 million fault samples. Finally, extensive experiments show that InstrDGM significantly outperforms existing methods.
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
Authors: Haiyang Yu, Tian Xie, Jiaping Gui, Pengyang Wang, Pengzhou Cheng, Ping Yi, Yue Wu
Venue: KDD
Year: 2025

Over the past few years, the emergence of backdoor attacks has presented significant challenges to deep learning systems, allowing attackers to insert backdoors into neural networks. When data with a trigger is processed by a backdoor model, it can lead to mispredictions targeted by attackers, whereas normal data yields regular results. The scope of backdoor attacks is expanding beyond computer vision and encroaching into areas such as natural language processing and speech recognition. Nevertheless, existing backdoor defense methods are typically tailored to specific data modalities, restricting their application in multimodal contexts. While multimodal learning proves highly applicable in facial recognition, sentiment analysis, action recognition, visual question answering, the security of these models remains a crucial concern. Specifically, there are no existing backdoor benchmarks targeting multimodal applications or related tasks. In order to facilitate the research in multimodal backdoor, we introduce BackdoorMBTI, the first backdoor learning toolkit and benchmark designed for multimodal evaluation across three representative modalities from eleven commonly used datasets. BackdoorMBTI provides a systematic backdoor learning pipeline, encompassing data processing, data poisoning, backdoor training, and evaluation. The generated poison datasets and backdoor models enable detailed evaluation of backdoor defenses. Given the diversity of modalities, BackdoorMBTI facilitates systematic evaluation across different data types. Furthermore, BackdoorMBTI offers a standardized approach to handling practical factors in backdoor learning, such as issues related to data quality and erroneous labels. We anticipate that BackdoorMBTI will expedite future research in backdoor defense methods within a multimodal context. Code is available at https://github.com/SJTUHaiyangYu/BackdoorMBTI.
Hardness-aware Privileged Features Distillation with Latent Alignment for CVR Prediction
Authors: Huining Yuan, Wenpeng Zhang, Zijie Hao, Zengde Deng
Venue: KDD
Year: 2025

In computational advertising, predicting the post-click conversion rate (CVR) using deep neural networks (DNNs) benefits a lot from privileged features, which can be collected for offline training but are unavailable during online serving. To utilize these privileged signals, privileged features distillation (PFD) methods incorporate a teacher model with privileged features to guide the CVR model. However, existing PFD approaches fail to put more emphasis on poorly predicted instances where the teacher's guidance is most crucial, and thus suffer from overconfidence on ''easy'' instances. In this work, we propose Hardness-aware Privileged Features Distillation (HA-PFD) for enhancing CVR prediction in real-world advertising recommender systems. We specifically design focal-style distillation losses that adaptively adjust the weight of each instance based on its ''hardness''. This method prioritizes poorly predicted instances during the distillation process, resulting in improved ranking performance and better model calibration. Additionally, we incorporate latent-level distillation into the PFD framework for the first time, which facilitates the student's representation learning through a straightforward layer alignment approach. We also propose a method for selecting privileged features based on their relevance to the conversion label. We conduct extensive offline experiments on large-scale, real-world datasets and online experiments on Douyin, a short video platform with billions of live users. In the offline evaluation, HA-PFD exhibits competitive performance and superior model calibration compared to existing state-of-the-art methods. In the online experiments, HA-PFD significantly improves advertiser value and conversions. Now we have deployed HA-PFD as the main online serving model on our short video platform.
Boosting Explainability through Selective Rationalization in Pre-trained Language Models
Authors: Libing Yuan, Shuaibo Hu, Kui Yu, Le Wu
Venue: KDD
Year: 2025

The widespread application of pre-trained language models (PLMs) in natural language processing (NLP) has led to increasing concerns about their explainability. Selective rationalization is a self-explanatory framework that selects human-intelligible input subsets as rationales for predictions. Recent studies have shown that applying existing rationalization frameworks to PLMs will result in severe degeneration and failure problems, producing sub-optimal or meaningless rationales. Such failures severely damage trust in rationalization methods and constrain the application of rationalization techniques on PLMs. In this paper, we find that the homogeneity of tokens in the sentences produced by PLMs is the primary contributor to these problems. To address these challenges, we propose a method named Pre-trained Language Model's Rationalization (PLMR), which splits PLMs into a generator and a predictor to deal with NLP tasks while providing interpretable rationales. The generator in PLMR also alleviates homogeneity by pruning irrelevant tokens, while the predictor uses full-text information to standardize predictions. Experiments conducted on two widely used datasets across multiple PLMs demonstrate the effectiveness of the proposed method PLMR in addressing the challenge of applying selective rationalization to PLMs. Codes: https://github.com/ylb777/PLMR.
A Structure-aware Invariant Learning Framework for Node-level Graph OOD Generalization
Authors: Ruiwen Yuan, Yongqiang Tang, Wensheng Zhang
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have been proven effective in modeling graph data, mostly depending on the in-distribution assumption. While in the out-of-distribution (OOD) scenarios, especially for the more challenging node-level task, the feature and structure distribution shifts between training and test nodes lead to performance degradation. To improve node-level OOD generalization, typical approaches introduce graph augmentation to enrich the training environments and conduct invariant learning to learn stable representations across various augmented environments. However, their graph augmentations emphasize diversity but neglect the preservation of invariant patterns which are fundamental to invariant learning. Moreover, most of them simply conduct the classic invariant learning objective but lack the consideration of the graph-specific structure information. Therefore, to mitigate their weakness, we propose a Structure-aware Invariant learning framework for Node-level Graph OOD generalization (SING). Specifically, we develop the invariance constraint regularization terms during the optimization of augmentations. Additionally, we define the structure embedding to elucidate the structural property and design the structure embedding alignment loss to optimize the augmentations and the invariant representations. By introducing the structure information, we further integrate the unique structural property into invariant learning, thereby boosting the invariant message-passing GNNs. The extensive experiments on the transductive GOOD benchmark and the inductive datasets empirically validate our superior OOD generalization performance to baselines.
Preference-Optimized Retrieval and Ranking for Efficient Multimodal Recommendation
Authors: Zhenrui Yue, Huimin Zeng, Yueqi Wang, Julian J. McAuley, Dong Wang
Venue: KDD
Year: 2025

Large multimodal models (LMMs) exhibit enhanced capabilities in understanding and generating both textual and visual content. By leveraging item metadata, LMMs are also applied for recommendation and demonstrate improvements across diverse scenarios. However, the majority of existing methods explore static item attributes without considering additional contextual information (e.g., price, brand). Moreover, overlooking the interaction between the retrieval and ranking stages may lead to suboptimal solutions for fine-grained recommendations. In this work, we introduce PRIME: preference-optimized retrieval and ranking for efficient multimodal recommendation. PRIME operates in two stages: (i) a lightweight retriever identifies potential candidate items; (ii) an LMM learns to rank the retrieved candidates with detailed user history and multimodal features (e.g., text and image attributes). These features are incorporated into a carefully designed prompt, facilitating fine-grained transition patterns for user preference understanding. To optimize the inference efficiency of PRIME, we introduce verbalizer-based inference, which computes ranking scores for all candidate items in a single forward pass. Furthermore, we employ the LMM ranker to provide feedback on sampled candidate sets, enabling online preference optimization that refines the retriever model and improves the alignment between retrieval and ranking. As a result, PRIME can capture subtle user intentions and efficiently rank candidate items with minimal inference costs. Extensive experiments show the effectiveness and efficiency of PRIME, which consistently achieves superior performance over baseline methods.
Oldie but Goodie: Re-illuminating Label Propagation on Graphs with Partially Observed Features
Authors: Sukwon Yun, Xin Liu, Yunhak Oh, Junseok Lee, Tianlong Chen, Tsuyoshi Murata, Chanyoung Park
Venue: KDD
Year: 2025

In real-world graphs, we often encounter missing feature situations where a few or the majority of node features, e.g., sensitive information, are missed. In such scenarios, directly utilizing Graph Neural Networks (GNNs) would yield sub-optimal results in downstream tasks such as node classification. Despite the emergence of a few GNN-based methods attempting to mitigate its missing situation, when only a few features are available, they rather perform worse than traditional structure-based models. To this end, we propose a novel framework that further illuminates the potential of classical Label Propagation (Oldie), taking advantage of Feature Propagation, especially when only a partial feature is available. Now called by GOODIE, it takes a hybrid approach to obtain embeddings from the Label Propagation branch and Feature Propagation branch. To do so, we first design a GNN-based decoder that enables the Label Propagation branch to output hidden embeddings that align with those of the FP branch. Then, GOODIE automatically captures the significance of structure and feature information thanks to the newly designed Structure-Feature Attention. Followed by a novel Pseudo-Label contrastive learning that differentiates the contribution of each positive pair within pseudo-labels originating from the LP branch, GOODIE outputs the final prediction for the unlabeled nodes. Through extensive experiments, we demonstrate that our proposed model, GOODIE, outperforms the existing state-of-the-art methods not only when only a few features are available but also in abundantly available situations. Source code of GOODIE is available at: https://github.com/SukwonYun/GOODIE.
Robustness Evaluation of Graph-based News Detection Using Network Structural Information
Authors: Xianghua Zeng, Hao Peng, Angsheng Li
Venue: KDD
Year: 2025

Although Graph Neural Networks (GNNs) have shown promising potential in fake news detection, they remain highly vulnerable to adversarial manipulations within social networks. Existing methods primarily establish connections between malicious accounts and individual target news to investigate the vulnerability of graph-based detectors, while they neglect the structural relationships surrounding targets, limiting their effectiveness in robustness evaluation. In this work, we propose a novel Structural Information principles-guided Adversarial Attack Framework, namely SI2AF, which effectively challenges graph-based detectors and further probes their detection robustness. Specifically, structural entropy is introduced to quantify the dynamic uncertainty in social engagements and identify hierarchical communities that encompass all user accounts and news posts. An influence metric is presented to measure each account's probability of engaging in random interactions, facilitating the design of multiple agents that manage distinct malicious accounts. For each target news, three attack strategies are developed through multi-agent collaboration within the associated subgraph to optimize evasion against black-box detectors. By incorporating the adversarial manipulations generated by SI2AF, we enrich the original network structure and refine graph-based detectors to improve their robustness against adversarial attacks. Extensive evaluations demonstrate that SI2AF significantly outperforms state-of-the-art baselines in attack effectiveness with an average improvement of 16.71%, and enhances GNN-based detection robustness by 41.54% on average.
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding
Authors: Tong Zeng, Longfeng Wu, Liang Shi, Dawei Zhou, Feng Guo
Venue: KDD
Year: 2025

Vision Large Language Models (VLLMs) have demonstrated impressive capabilities in general visual tasks such as image captioning and visual question answering. However, their effectiveness in specialized, safety-critical domains like autonomous driving remains largely unexplored. Autonomous driving systems require sophisticated scene understanding in complex environments, yet existing multimodal benchmarks primarily focus on normal driving conditions, failing to adequately assess VLLMs' performance in safety-critical scenarios. To address this, we introduce DVBench-a pioneering benchmark designed to evaluate the performance of VLLMs in understanding safety-critical driving videos. Built around a hierarchical ability taxonomy that aligns with widely adopted frameworks for describing driving scenarios used in assessing highly automated driving systems, DVBench features 10,000 multiple-choice questions with human-annotated ground-truth answers, enabling a comprehensive evaluation of VLLMs' capabilities in perception and reasoning. Experiments on 14 state-of-the-art VLLMs, ranging from 0.5B to 72B parameters, reveal significant performance gaps, with no model achieving over 40% accuracy, highlighting critical limitations in understanding complex driving scenarios. To probe adaptability, we fine-tuned selected models using domain-specific data from DVBench, achieving accuracy gains ranging from 5.24 to 10.94 percentage points, with relative improvements of up to 43.59%. This improvement underscores the necessity of targeted adaptation to bridge the gap between general-purpose vision-language models and mission-critical driving applications. DVBench establishes an essential evaluation framework and research roadmap for developing VLLMs that meet the safety and robustness requirements for real-world autonomous systems. We released the benchmark toolbox and the fine-tuned model at: https://github.com/tong-zeng/DVBench.git.