OpenRaiser/Intern-Atlas huggingface dataset:
R2MR: Review and Rewrite Modality for Recommendation
Authors: Gu Tang, Jinghe Wang, Xiaoying Gan, Bin Lu, Ze Zhao, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Venue: KDD
Year: 2025

With the explosive growth of online multimodal content, multimodal recommender systems(MRSs) have brought significant benefits to multimedia platforms. As MRSs evolve, many studies incorporate advanced technologies like graph neural networks(GNNs) and self-supervised learning(SSL), achieving remarkable results. However, these efforts still suffer from the quality disparity problem. It refers to the mixture of high and low quality across items' multiple modalities, owing to disparities in construction costs or design levels. These low-quality modalities often lack crucial details or introduce noise to the depiction of item, leading to insufficient or polluted item representation. Therefore, we propose a novel framework R2MR: Review and Rewrite Modality for Recommendation to tackle this issue. Specifically, R2MR is composed of two key components: Modality Reviewer and Modality Rewriter. The Modality Reviewer introduces a Consensus Review Mechanism. It performs perspective decomposition based on user representations and learns the consensus quality scores for modalities from diverse perspectives of multiple users. The Modality Rewriter proposes a Latent Mapping Model, which improves the quality of inferior modalities by learning various mapping patterns from high-quality modalities. Comprehensive experiments across three benchmark datasets reveal that R2MR substantially outperforms state-of-the-art methods, achieving an average improvement of 9.20%. The implementations are available at https://github.com/gutang-97/R2MR.
Lossless Set Compression by Binary Tree Coding
Authors: Longtao Tang, Ying Zhou, Yu Yang
Venue: KDD
Year: 2025

A set of categorical items is a common data structure used in various applications, such as customer orders in online shopping. With the rise of big data and e-commerce, there is an increasing need to store such a large number of set data efficiently. This paper presents a novel lossless compression method that uses binary trees for set compression. The compressed code is generated by performing a depth-first search for items in the set on the binary tree. We prove that finding the optimal tree for minimizing the total code length for a given collection of sets is NP-hard, and the problem even remains NP-hard when we only need to assign items to the leaf nodes of a given tree. A heuristic algorithm is then proposed for building trees. To further improve the compression efficiency, we incorporate the entropy coding method into the coding process. Moreover, we reveal the underlying probabilistic expression of the binary tree model and present the conditional independence structure of the model. To validate the effectiveness of the proposed method, empirical studies are conducted on two real-world online e-commerce datasets. The results show that the proposed coding method significantly reduces the storage space required for set data and the tree structure for compressing sets can help identify conditional independent items.
LLM4Tag: Automatic Tagging System for Information Retrieval via Large Language Models
Authors: Ruiming Tang, Chenxu Zhu, Bo Chen, Weipeng Zhang, Menghui Zhu, Xinyi Dai, Huifeng Guo
Venue: KDD
Year: 2025

Tagging systems play an essential role in various information retrieval applications such as search engines and recommender systems. Recently, Large Language Models (LLMs) have been applied in tagging systems due to their extensive world knowledge, semantic understanding, and reasoning capabilities. Despite achieving remarkable performance, existing methods still have limitations, including difficulties in retrieving relevant candidate tags comprehensively, challenges in adapting to emerging domain-specific knowledge, and the lack of reliable tag confidence quantification. To address these three limitations above, we propose an automatic tagging system LLM4Tag. First, a graph-based tag recall module is designed to effectively and comprehensively construct a small-scale highly relevant candidate tag set. Subsequently, a knowledge-enhanced tag generation module is employed to generate accurate tags with long-term and short-term knowledge injection. Finally, a tag confidence calibration module is introduced to generate reliable tag confidence scores. Extensive experiments over three large-scale industrial datasets show that LLM4Tag significantly outperforms the state-of-the-art baselines and LLM4Tag has been deployed online for content tagging to serve hundreds of millions of users.
Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search
Authors: Hengzhu Tang, Zefeng Zhang, Zhiping Li, Zhenyu Zhang, Xing Wu, Li Gao, Suqi Cheng, Dawei Yin
Venue: KDD
Year: 2025

Video Quality Assessment (VQA) is a crucial component of broadscale video retrieval systems. Its goal is to accurately identify various quality issues in videos, thereby encouraging the video retrieval system to prioritize high-quality videos. In large-scale industrial video retrieval systems, we formulate the characteristics of low-quality videos into four categories: visual-related low-level quality problems such as mosaics and black boxes, textual-related low-level quality problems caused by video title and Optical Character Recognition (OCR) content, as well as semantic-level frame incoherence and frame-text mismatch caused by emerging AI-generated videos. These kinds of low-quality videos, which are widely present in industrial environments, have been overlooked in academic research before, and accurately identifying them is very challenging. In this paper, we introduce a Multi-Branch Collaborative learning Network (MBCN) to tackle the above issues. We carefully design four assessment branches for MBCN to adapt to the above four kinds of issues for industrial video retrieval systems. After obtaining independent scores for each branch, we perform a weighted aggregation of the various branches to dynamically address video quality issues in different scenarios with a squeeze-and-excitation mechanism. Finally, we integrate point-wise and pair-wise optimization objectives to ensure the predicted scores are stable and fall into a reasonable range. To demonstrate the effectiveness of our proposed MBCN, we conduct extensive offline and online experiments in a world-level video search engine. The experimental results show that due to the powerful ability of MBCN to identify video quality issues, the ranking ability of the video retrieval system has been significantly improved. We also conduct a series of detailed experimental analyses to verify that all four evaluation branches play a positive role. Besides that, for emerging low-quality AI-generated videos, the recognition accuracy of MBCN also improves significantly compared to the baseline.
LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Authors: Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
Venue: KDD
Year: 2025

Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are designed for general instances without considering specific problem properties and hard to accurately handle with complex elastic-plastic solids involving contact, loading and unloading. In this work, we take stretch bending, a popular metal fabrication technique, as our case study and introduce LaDEEP, a deep learning-based surrogate model for La rge De formation of Elastic-Plastic Solids. We encode the partitioned regions of the involved slender solids into a token sequence to maintain their essential order property. To characterize the physical process of the solid deformation, a two-stage Transformer-based module is designed to predict the deformation with the sequence of tokens as input. Empirically, LaDEEP achieves five magnitudes faster speed than finite element methods with a comparable accuracy, and gains 20.47% relative improvement on average compared to other deep learning baselines. We have also deployed our model into a real-world industrial production system, and it has shown remarkable performance in both accuracy and efficiency. Code is available at https://github.com/therontau0054/LaDEEP.
Breeding-aware Revenue Maximization for NFT Viral Marketing on Social Networks
Authors: Ya-Wen Teng, De-Nian Yang, Yishuo Shi, Guang-Siang Lee, Wang-Chien Lee, Philip S. Yu, Ming-Syan Chen
Venue: KDD
Year: 2025

Non-fungible tokens (NFTs) have emerged as a transformative innovation in art and technology, relying heavily on social networks for promotion and revenue generation. The value of NFTs is profoundly influenced by their scarcity, rarity, and unique breeding mechanisms, which present novel challenges for viral marketing strategies. In this paper, we introduce a new research problem of NFT Revenue Maximization (NRM), which focuses on maximizing revenue from the perspective of NFT marketplaces by optimally selecting users for viral marketing campaigns (NFT airdrops) and determining the ideal quantities of NFTs to release. We prove the hardness of NRM and propose an approximation algorithm named Quantity and Offspring-Oriented Airdrops (QOOA). Our algorithm leverages the concepts of Scarcity-Conscious Revenue and Valuation-based Quantity Inequality to prune suboptimal airdrops and quantities at an early stage. To further enhance revenue through NFT breeding, QOOA identifies and incentivizes Rare Trait Collectors to acquire multiple NFTs with rare traits, facilitating the breeding of high-value offspring. Experimental results demonstrate that QOOA significantly outperforms baselines, achieving up to 3.8 times higher revenue in large-scale social networks.
MLDGG: Meta-Learning for Domain Generalization on Graphs
Authors: Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang, Yujie Lin, Dong Li
Venue: KDD
Year: 2025

Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testing and training distributions. However, existing methods often rely on static encoders directly applied to the target domain, constraining its flexible adaptability. In contrast to conventional methodologies, which concentrate on developing specific generalized models, our framework, MLDGG, endeavors to achieve adaptable generalization across diverse domains by integrating cross-multi-domain meta-learning with structure learning and semantic identification.Initially, it introduces a generalized structure learner to mitigate the adverse effects of task-unrelated edges, enhancing the comprehensiveness of representations learned by Graph Neural Networks (GNNs) while capturing shared structural information across domains.Subsequently, a representation learner is designed to disentangle domain-invariant semantic and domain-specific variation information in node embedding by leveraging causal reasoning for semantic identification, further enhancing generalization. In the context of meta-learning, meta-parameters for both learners are optimized to facilitate knowledge transfer and enable effective adaptation to graphs through fine-tuning within the target domains, where target graphs are inaccessible during training.Our empirical results demonstrate that MLDGG surpasses baseline methods, showcasing the effectiveness in three different distribution shift settings.
GROOT: Effective Design of Biological Sequences with Limited Experimental Data
Authors: Thanh V. T. Tran, Nhat Khang Ngo, Viet Anh Nguyen, Truong Son Hy
Venue: KDD
Year: 2025

Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from available data and using a surrogate model fΦ to guide optimization algorithms toward optimal outputs. However, existing methods struggle when labeled data is limited, as training fΦ with few labeled data points can lead to subpar outputs, offering no advantage over the training data itself. We address this challenge by introducing GROOT, a GRaph-based Latent SmOOThing for Biological Sequence Optimization. In particular, GROOT generates pseudo-labels for neighbors sampled around the training latent embeddings. These pseudo-labels are then refined and smoothed by Label Propagation. Additionally, we theoretically and empirically justify our approach, demonstrate GROOT's ability to extrapolate to regions beyond the training set while maintaining reliability within an upper bound of their expected distances from the training regions. We evaluate GROOT on various biological sequence design tasks, including protein optimization (GFP and AAV) and three tasks with exact oracles from Design-Bench. The results demonstrate that GROOT equalizes and surpasses existing methods without requiring access to black-box oracles or vast amounts of labeled data, highlighting its practicality and effectiveness. We release our code at https://github.com/Fsoft-AIC/GROOT.
ASTNet: Asynchronous Spatio-Temporal Network for Large-Scale Chemical Sensor Forecasting
Authors: Shihao Tu, Yang Yang, Wenyue Ding, Yicheng Lu, Qingkai Ren, Yupeng Zhang, Yin Zhang
Venue: KDD
Year: 2025

The chemical industry is faced with the urgent challenge of effectively harnessing the vast amounts of time-series data generated by thousands of sensors, which is essential for forecasting chemical states, achieving accurate real-time control of production processes. Traditional forecasting methods suffer from high computational latency and struggle with the complexity of spatiotemporal dependencies. As a result, modeling this data becomes challenging. This paper introduces a novel approach, referred to as ASTNet, designed to address these challenges. ASTNet integrates an asynchronous spatiotemporal modeling framework that combines temporal and spatial encoders, enabling concurrent learning of temporal and spatial dependencies while reducing computational latency. Additionally, it introduces a gated graph fusion mechanism that adaptively combines static (meta) and evolving (dynamic) sensor graphs, enhancing the handling of heterogeneous sensor data and spatial correlations. Extensive experiments on three real-world chemical sensor datasets demonstrate that ASTNet outperforms SOTA methods in terms of both prediction accuracy and computational efficiency, making ASTNet successfully deployed in chemical engineering industrial scenarios.
Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language Models
Authors: Shangqing Tu, Zhuoran Pan, Wenxuan Wang, Zhexin Zhang, Yuliang Sun, Jifan Yu, Hongning Wang, Lei Hou, Juanzi Li
Venue: KDD
Year: 2025

Large language models (LLMs) have been increasingly applied to various domains, which triggers increasing concerns about LLMs' safety on specialized domains, e.g. medicine. Despite prior explorations on general jailbreaking attacks, there are two challenges for applying existing attacks on testing the domain-specific safety of LLMs: (1) Lack of professional knowledge-driven attacks(2) Insufficient coverage of domain knowledge. To bridge this gap, we propose a new task, knowledge-to-jailbreak, which aims to generate jailbreaking attacks from domain knowledge, requiring both attack effectiveness and knowledge relevance. We collect a large-scale dataset with 12,974 knowledge-jailbreak pairs and fine-tune a large language model as jailbreak-generator, to produce domain knowledge-specific jailbreaks. Experiments on 13 domains and 8 target LLMs demonstrate the effectiveness of jailbreak-generator in generating jailbreaks that are both threatening to the target LLMs and relevant to the given knowledge. We also apply our method to an out-of-domain knowledge base, showing that jailbreak-generator can generate jailbreaks that are comparable in harmfulness to those crafted by human experts. Data and code are available at: https://github.com/THU-KEG/Knowledge-to-Jailbreak/.
Cross-Species Insights: Transforming Drug Efficacy from Rats to Humans Using Tissue-Specific Generative Models
Authors: Sally Turutov, Kira Radinsky
Venue: KDD
Year: 2025

Within the realm of drug development, the transition from successful animal trials to human clinical efficacy remains a daunting challenge. While initial outcomes may appear promising in animal studies, ensuring similar effectiveness in humans, especially across specific target tissues, presents a significant obstacle. To address this pressing concern, we introduce a novel generative model tailored to optimize molecules that have demonstrated efficacy in rats for enhanced performance in specific human tissues. Central to our solution is the transformer architecture, enhanced with intricate mechanisms such as molecule self-attention within the encoder and a novel dedicated tissue-specific generator. Intuitively, by learning to generate molecules simultaneously from multiple tissues, the generative model enhances its ability to perform the necessary adaptations from rats to humans. Through rigorous empirical evaluation across various tissues, our model consistently exhibits remarkable efficacy compared to existing methods. We anticipate that this model has the potential to minimize the requirement for lengthy and inconclusive trials, thereby streamlining the drug development process.
Causality - Exploiting Multi-Modal Data
Authors: Caroline Uhler
Venue: KDD
Year: 2025

Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. Representation learning has become a key driver of deep learning applications, since it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of multi-modal and interventional data (in medicine, advertisement, education, etc.). However, these datasets are still miniscule compared to the action spaces of interest in these applications (e.g. interventions can take on continuous values like the dose of a drug or can be combinatorial as in combinatorial drug therapies). In this talk, we will present a statistical and computational framework for causal representation learning from multi-modal data and its application towards optimal intervention design.
How Well Calibrated are Extreme Multi-label Classifiers? An Empirical Analysis
Authors: Nasib Ullah, Erik Schultheis, Jinbin Zhang, Rohit Babbar
Venue: KDD
Year: 2025

Extreme multilabel classification (XMLC) problems occur in settings such as related product recommendation, large-scale document tagging, or ad prediction, and are characterized by a label space that can span millions of possible labels. There are two implicit tasks that the classifier performs: Evaluating each potential label for its expected worth, and then selecting the best candidates. For the latter task, only the relative order of scores matters, and this is what is captured by the standard evaluation procedure in the XMLC literature. However, in many practical applications, it is important to have a good estimate of the actual probability of a label being relevant, e.g., to decide whether to pay the fee to be allowed to display the corresponding ad. To judge whether an extreme classifier is indeed suited to this task, one can look, for example, to whether it returns calibrated probabilities, which has hitherto not been done in this field. Therefore, this paper aims to establish the current status quo of calibration in XMLC by providing a systematic evaluation, comprising nine models from four different model families across seven benchmark datasets. As naive application of Expected Calibration Error (ECE) leads to meaningless results in long-tailed XMC datasets, we instead introduce the notion of calibration@k (e.g., ECE@k), which focusses on the top-k probability mass, offering a more appropriate measure for evaluating probability calibration in XMLC scenarios. While we find that different models can exhibit widely varying reliability plots, we also show that post-training calibration via a computationally efficient isotonic regression method enhances model calibration without sacrificing prediction accuracy. Thus, the practitioner can choose the model family based on accuracy considerations, and leave calibration to isotonic regression.
TAG2M- A Task-Agnostic Knowledge Distillation Framework for Distilling GNN to MLP
Authors: Ram Ganesh V, Ayush Singh, Aditi Rai, Harsh Pal, Deepanshu Bagotia, Akshay Sethi, Aakarsh Malhotra, Sayan Ranu
Venue: KDD
Year: 2025

Graph Neural Networks (Gnns) have achieved remarkable success in various downstream tasks, such as node classification and link prediction. Yet, efficiently deploying Gnns remains a challenge due to their computational complexity. Graph knowledge distillation aims to address this by transferring task-specific structural knowledge from teacher Gnns to lightweight student Gnns or Multi-Layer Perceptrons (MLPs). Despite its promise, existing distillation approaches suffer from several limitations: (i) they require extensive task-specific supervision(ii) they must be retrained separately for each downstream task, and (iii) they often struggle in heterophilous settings. To overcome these challenges, we propose TAG2M, a Task-Agnostic Gnn-to-MLP distillation framework designed for efficient and accurate few-shot inference. TAG2M introduces several novel strategies, including a self-supervised contrastive loss that captures topological information solely from node attributes. Additionally, it leverages Lipschitz embeddings to encode positional information with provable distortion bounds, ensuring robust representation learning. To further enhance adaptability for few-shot inference, TAG2M incorporates a learnable prompt head, which facilitates rapid task adaptation even in label-scarce settings. Unlike prior methods, TAG2M generalizes well across both homophilous and heterophilous datasets while delivering a significant computational advantage, achieving up to a 20X -200X speed-up. Extensive evaluations on 11 public datasets demonstrate its superior accuracy across diverse tasks, including node classification, link prediction, and node regression, outperforming state-of-the-art approaches.