OpenRaiser/Intern-Atlas huggingface dataset:
GNN-SKAN: Advancing Molecular Representation Learning with SwallowKAN
Authors: Ruifeng Li, Mingqian Li, Wei Liu, Hongyang Chen
Venue: KDD
Year: 2025

Effective molecular representation learning is crucial for advancing molecular property prediction and drug design. Mainstream molecular representation learning methods rely on Graph Neural Networks (GNNs) to model complex molecular structures. However, these GNN-based approaches struggle with three significant issues: insufficient annotations, molecular diversity, and architectural limitations such as over-squashing, which leads to the loss of critical structural details. To address these issues, we introduce a new class of GNNs that integrates the Kolmogorov-Arnold Networks (KANs), known for their robust data-fitting capabilities and high accuracy in small-scale AI + Science tasks. By incorporating KANs into GNNs, our model enhances the representation of molecular structures. We further advance this approach with a variant called SwallowKAN (SKAN), which employs adaptive Radial Basis Functions (RBFs) as the core of the non-linear neurons. This innovation improves both computational efficiency and adaptability to diverse molecular structures. Building on the strengths of SKAN, we propose a new class of GNNs, GNN-SKAN, and its augmented variant, GNN-SKAN+, which incorporates a SKAN-based classifier to further boost the performance. Experiments across 6 classification datasets, 6 regression datasets, and 4 few-shot learning datasets demonstrate that our approach achieves new state-of-the-art performance in terms of accuracy and computational cost.
Contrastive Learning for Inventory Add Prediction at Fliggy
Authors: Manwei Li, Detao Lv, Yao Yu, Zihao Jiao
Venue: KDD
Year: 2025

Online Travel Platforms (OTPs) serve as crucial bridges between hotels and users, hotel staff can synchronize room inventory information with OTPs through manual and auto modes. In the manual mode, the hotel staff must manually maintain the inventory information on the OTPs. This mode often leads to the "inventory synchronization delay'' phenomenon where OTPs show no availability while hotels still have available rooms, seriously affecting the competitiveness of OTPs and hotel sales. To address this issue, Fliggy uses inventory add prediction (IAP) to determine whether to add an inventory for the sold-out room type. However, in practice, accurate modeling of IAP faces significant challenges due to the data sparsity. In this paper, we propose a Contrastive Learning framework for Inventory Add Prediction at Fliggy (CL4IAP), which consists of the Joint Pay-Accept Prediction Module, the Data Augmentation Module, and the Contrastive Learning Module. Specifically, the Joint Pay-Accept Prediction Module aims to predict the likelihood of generating an order and the hotel acceptance after adding an inventory. It also includes a specially designed correlation enhancement component that facilitates the expert prediction network's learning through knowledge transfer based on inter-task correlation. In the Data Augmentation Module, we design three novel data augmentation strategies for the first time based on the correlation and importance of features. In the Contrastive Learning Module, we design instance-level and cluster-level contrastive losses, which aim to minimize the distance between positive sample pairs and mitigate the negative impact of false negative sample pairs, respectively. Both offline and online experiments demonstrate the effectiveness of CL4IAP, and CL4IAP has been successfully deployed on Fliggy.
Learn to Refine: Synergistic Multi-Agent Path Optimization for Lifelong Conflict-Free Navigation of Autonomous Vehicles
Authors: Junjun Li, Zeyuan Ma, Ting Huang, Yue-Jiao Gong
Venue: KDD
Year: 2025

Lifelong Multi-Agent Path Finding (LMAPF) focuses on planning conflict-free paths for agents, like autonomous vehicles, that are continuously assigned new tasks. The synergy of search-based and learning-based methods holds promise for striking a balance in-between effectiveness and efficiency but still faces several challenges such as inferior initial paths, weak search-learning synergy and low sample utilization rate. To address these issues, this paper proposes a new synergized LMAPF approach, named Synergistic Multi-Agent Path Optimization (SMAPO), which consists of two tightly-coupled phases: Primordial Planning and Decision Refinement. In the Primordial Planning phase, we introduce a novel load-balanced A* algorithm that integrates planned and perceived congestion costs, which enhances initial solution quality by evenly distributing spatiotemporal traffic loads, thereby mitigating potential conflicts. In the Decision Refinement phase, we propose a novel Encoder-Decoder based neural network to learn a collaborative optimization policy through multi-agent reinforcement learning. In addition, we leverage dual transformations to augment trajectory samples during online learning, enhancing both the sample utilization rate and overall learning stability. Extensive experiments reveal that our SMAPO is superior to the state-of-the-art baselines in effectiveness, efficiency, and generalization capability. Source code is available at https://github.com/ByteUser-blues/SMAPO.
Reasoning-Enhanced Object-Centric Learning for Videos
Authors: Jian Li, Pu Ren, Yang Liu, Hao Sun
Venue: KDD
Year: 2025

Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learning systems toward the physical world. Recently, slot-based video models have demonstrated remarkable proficiency in segmenting and tracking objects, but they overlook the importance of the effective reasoning module. In the real world, reasoning and predictive abilities play a crucial role in human perception and object tracking; in particular, these abilities are closely related to human intuitive physics. Inspired by this, we designed a novel reasoning module called the Slot-based Time-Space Transformer with Memory buffer (STATM) to enhance the model's perception ability in complex scenes. The memory buffer primarily serves as storage for slot information from upstream modules, the Slot-based Time-Space Transformer makes predictions through slot-based spatiotemporal attention computations and fusion. Our experimental results on various datasets indicate that the STATM module can significantly enhance the capabilities of multiple state-of-the-art object-centric learning models for video. Moreover, as a predictive model, the STATM module also performs well in downstream prediction and Visual Question Answering (VQA) tasks. We will release our codes and data at https://github.com/intell-sci-comput/STATM.
SlotPi: Physics-informed Object-centric Reasoning Models
Authors: Jian Li, Han Wan, Ning Lin, Yu-Liang Zhan, Ruizhi Chengze, Haining Wang, Yi Zhang, Hongsheng Liu, Zidong Wang, Fan Yu, Hao Sun
Venue: KDD
Year: 2025

Understanding and reasoning about dynamics governed by physical laws through visual observation, akin to human capabilities in the real world, poses significant challenges. Currently, object-centric dynamic simulation methods, which emulate human behavior, have achieved notable progress but overlook two critical aspects: 1) the integration of physical knowledge into models. Humans gain physical insights by observing the world and apply this knowledge to accurately reason about various dynamic scenarios; 2) the validation of model adaptability across diverse scenarios. Real-world dynamics, especially those involving fluids and objects, demand models that not only capture object interactions but also simulate fluid flow characteristics. To address these gaps, we introduce SlotPi, a slot-based physics-informed object-centric reasoning model. SlotPi integrates a physical module based on Hamiltonian principles with a spatio-temporal prediction module for dynamic forecasting. Our experiments highlight the model's strengths in tasks such as prediction and Visual Question Answering (VQA) on benchmark and fluid datasets. Furthermore, we have created a real-world dataset encompassing object interactions, fluid dynamics, and fluid-object interactions, on which we validated our model's capabilities. The model's robust performance across all datasets underscores its strong adaptability, laying a foundation for developing more advanced world models.
FusionSAM: Visual Multi-Modal Learning with Segment Anything Model
Authors: Daixun Li, Weiying Xie, Mingxiang Cao, Yunke Wang, Yusi Zhang, Leyuan Fang, Yunsong Li, Chang Xu
Venue: KDD
Year: 2025

Multimodal image fusion and semantic segmentation are critical for autonomous driving. Despite advancements, current models often struggle with segmenting densely packed elements due to a lack of comprehensive fusion features for guidance during training. While the Segment Anything Model (SAM) allows precise control during fine-tuning through its flexible prompting encoder, its potential remains largely unexplored in the context of multimodal segmentation for natural images. In this paper, we introduce SAM into multimodal image segmentation for the first time, proposing a novel framework that combines Latent Space Token Generation (LSTG) and Fusion Mask Prompting (FMP) modules. This approach transforms the training methodology for multimodal segmentation from a traditional black-box approach to a controllable, prompt-based mechanism. Specifically, we obtain latent space features for both modalities through vector quantization and embed them into a cross-attention-based inter-domain fusion module to establish long-range dependencies between modalities. We then use these comprehensive fusion features as prompts to guide precise pixel-level segmentation. Extensive experiments on multiple public datasets demonstrate that our method significantly outperforms SAM and SAM2 in multimodal autonomous driving scenarios, achieving an average improvement of 4.1% over the state-of-the-art method in segmentation mIoU, and the performance is also optimized in other multi-modal visual scenes.
RankElectra: Semi-supervised Pre-training of Learning-to-Rank Electra for Web-scale Search
Authors: Yuchen Li, Haoyi Xiong, Yongqi Zhang, Jiang Bian, Tianhao Peng, Xuhong Li, Shuaiqiang Wang, Linghe Kong, Dawei Yin
Venue: KDD
Year: 2025

While representation learning has been used to boost the performance of Learning-to-Rank (LTR) models through distilling key features for webpage ranking, the weak supervision signals extracted from users' sparse click-through data lead to inadequate representation of query-webpage pairs for ranking score prediction. Recent studies in generative LTR pre-training demonstrate the feasibility of incorporating reconstruction loss for enhanced ranking score prediction. However, LTR is afterall a regression task and it might be reasonable to find an alternate route that pre-trains LTR models with discriminative losses. Following the success of Electra in representation learning for natural language processing (NLP), this work proposes RankElectra that pre-trains the LTR model as a discriminator module inside a generative learning framework. Specifically, RankElectra first structures sparsely-annotated query-webpage pairs into a bipartite graph, with query and webpage feature vectors as node types and ranking scores as the connecting edges, and then leverages positive and negative extension strategies to densify the graph by link predictions. Later, this work proposes a novel Electra module that pre-trains the LTR model as a discriminator module for node reconstruction tasks, where node features of selected edges would be randomly masked and reconstructed by a generator, and the discriminator learns to classify whether the reconstructed features are the original or replaced as well as perform correct ranking. Finally, the pre-trained discriminator module, rather than the generator, would be fine-tuned on the labeled graph. We carried out extensive offline and online evaluations using the real-world web traffic of Baidu search engine. The results show that RankElectra could significantly boost the ranking performance of Baidu Search compared with numbers of competitor systems.
ESCFD: Probabilistic Flow Diffusion Model for Accelerated High-Quality Single-Cell RNA-seq Data Synthesis
Authors: Shuangyin Li, Jihua Yang, Yixuan Wang, Shimin Di, Lei Chen
Venue: KDD
Year: 2025

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for studying complex biological systems and diseases with high resolution. However, generating sufficient high quality scRNA-seq samples for accurate downstream analyses and ensuring reproducibility remains a challenge. Previous methods often produce low-quality samples or a limited number of useful specific cell subpopulations. To address this issue, we propose a novel Efficient Single-Cell RNA-seq Flow Diffusion (named ESCFD) method based on the Diffusion Model, capable of synthesizing large-scale, high-quality scRNA-seq samples and generating either ''holistic'' or specific cellular subpopulations within a single framework. A pre-guidance mechanism is designed for synthesizing specific cellular subpopulations of high quality, and a post-guidance mechanism was designed to accelerate the sampling of scRNA-seq samples. The ESCFD can efficiently synthesize large-scale and high-quality scRNA-seq samples for various downstream tasks, surpassing previous work with faster sampling speed. Experimental results demonstrate that the ESCFD model achieves state-of-the-art performance in cell classification and cell distribution similarity when evaluated on three scRNA-seq benchmarks, and verified the effectiveness of accelerated sampling. Visualization experiments also reveal the ESCFD's ability to synthesize specific cellular subpopulations. This innovative approach has the potential to significantly advance our understanding of complex biological systems and accelerate the development of novel therapeutic interventions and personalized medicine strategies.
TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model
Authors: Weixian Waylon Li, Yftah Ziser, Yifei Xie, Shay B. Cohen, Tiejun Ma
Venue: KDD
Year: 2025

Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub-optimal global rankings. Conversely, deep learning based listwise methods, while aiming to optimise entire lists, require complex tuning and yield only marginal improvements over robust pairwise models. To overcome these limitations, we introduce Travelling Salesman Problem Rank (TSPRank), a hybrid pairwise-listwise ranking method. TSPRank reframes the ranking problem as a Travelling Salesman Problem (TSP), a well-known combinatorial optimisation challenge that has been extensively studied for its numerous solution algorithms and applications. This approach enables the modelling of pairwise relationships and leverages combinatorial optimisation to determine the listwise ranking. TSPRank can be directly integrated as an additional component into embeddings generated by existing backbone models to enhance ranking performance. Our extensive experiments across three backbone models on diverse tasks, including stock ranking, information retrieval, and historical events ordering, demonstrate that TSPRank significantly outperforms both pure pairwise and listwise methods. Our qualitative analysis reveals that TSPRank's main advantage over existing methods is its ability to harness global information better while ranking. TSPRank's robustness and superior performance across different domains highlight its potential as a versatile and effective LETOR solution.
Imputation via Domain Adaptation: Rethinking Variable Subset Forecasting from Knowledge Transfer
Authors: Runchang Liang, Qi Hao, Yue Gao, Kunpeng Liu, Lu Jiang, Pengyang Wang, Minghao Yin
Venue: KDD
Year: 2025

Multivariate time series forecasting in practical deployment faces a critical challenge termed Variable Subset Forecasting (VSF), where certain variables accessible during training are entirely missing during inference. This creates a stark discrepancy between the training (source domain with full variables) and inference (target domain with partial variables) environments, disrupting cross-variable dependencies and fragmenting global temporal patterns. Existing imputation methods, limited to transferring local knowledge (e.g., temporal neighbors or pairwise correlations), fail to capture essential global dynamics, leading to severe performance degradation under distribution shifts. To address these challenges, we redefine VSF as a cross-domain knowledge transfer problem and propose VIDA, a framework that systematically transfers Variable Invariant knowledge from complete to partial observations through Domain Adaptation. Key to our approach is (1) Global time-frequency joint representation learning, which encodes temporal dynamics via dilated convolutions and captures low-frequency spectral consistency using Fourier neural operators, and (2) Sinkhorn-regularized distribution alignment to bridge non-overlapping feature supports across domains via optimal transport. Unlike imputation-first methods, VIDA enforces task-driven consistency by jointly optimizing predictions on reconstructed and original data, ensuring the transferred knowledge directly enhances forecasting robustness. Extensive experiments across four real-world datasets show that VIDA outperforms state-of-the-art imputation methods by 25% on average with partially observed variables. This work establishes a new paradigm for variable-missing scenarios by unifying imputation and forecasting through principled knowledge transfer.
Hexagon-Net: Heterogeneous Cross-View Aligned Graph Attention Networks for Implied Volatility Surface Prediction
Authors: Kaiwei Liang, Ruirui Liu, Huichou Huang, Johannes Ruf, Peilin Zhao, Qingyao Wu
Venue: KDD
Year: 2025

Implied Volatility Surface (IVS) prediction is critical for options hedging, portfolio management, and risk control. These applications encounter significant challenges, including imbalanced data distributions and inherent uncertainties in forecasting. Recent advances relying on deep learning have led to significant progress in addressing these issues. However, several key problems have not yet been solved: (i) Moneyness and maturities of traded options change over time. Therefore, proper spatio-temporal alignment is an essential prerequisite for the downstream forecasting exercise. (ii) Different regions of the IVS are unevenly informed because of liquidity constraints and therefore should not be modeled uniformly. (iii) The complex interconnections among data points in the IVS from various perspectives-such as the well-known 'smirk' patterns across dimensions-are neither explicitly addressed nor effectively captured by existing models. To address these issues, we propose a novel end-to-end heterogeneous cross(x)-view aligned graph attention network (Hexagon-Net), which aligns historical IVS data, learns distinctive IVS patterns, propagates predictive information, and forecasts future IVS movements simultaneously. Extensive experiments on stock index options datasets demonstrate that Hexagon-Net significantly and consistently outperforms the previous approaches in IVS modeling and deep learning. Additionally, we present further experiments-such as ablation studies, sensitivity analyses, and alternative configurations-to explore the reasons behind its superior performance.
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
Authors: Daojun Liang
Venue: KDD
Year: 2025

Traditional regression and prediction tasks often only provide deterministic point estimates. To estimate the distribution or uncertainty of the response variable, traditional methods either assume that the posterior distribution of samples follows a Gaussian process or require thousands of forward passes for sample generation. We propose a novel approach called DistPred for regression and forecasting tasks, which overcomes the limitations of existing methods while remaining simple and powerful. Specifically, we transform proper scoring rules that measure the discrepancy between the predicted distribution and the target distribution into a differentiable discrete form and use it as a loss function to train the model end-to-end. This allows the model to sample numerous samples in a single forward pass to estimate the potential distribution of the response variable. We have compared our method with several existing approaches on multiple datasets and achieved state-of-the-art performance. Additionally, our method significantly improves computational efficiency. For example, compared to state-of-the-art models, DistPred has a 180x faster inference speed.
Feature Reconstruction for Anomaly Detection on Directed Multigraphs: A Preprocessing Framework for GNNs
Authors: Sicheng Liang, Qinlin Xie, Jingqi Feng, Yiwen Yue, Hongyi Li, Jiawei Ye, Jie Wu
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have achieved significant success in anomaly detection across various domains. However, existing GNN approaches face notable challenges when applied to directed multigraphs where edge attributes serve as the sole source of information. This limitation complicates the learning of meaningful node representations and the accurate detection of anomalies, particularly in transaction networks. Moreover, existing methods typically suffer from dimensionality explosion and suboptimal use of structural and semantic information, leading to reduced detection performance. To address these challenges, we propose Feature Reconstruction for Anomaly Detection on Directed Multigraphs (FRAD-DM), a novel preprocessing framework tailored to enhance graph representations in node feature-absent scenarios. FRAD-DM employs advanced edge feature derivation techniques, temporal and structural subgraph analysis for node representation generation, and a reinforcement learning-based adaptive feature selection mechanism. This comprehensive framework optimizes feature spaces by balancing informativeness, task relevance, and computational efficiency, ensuring robust and scalable anomaly detection. Extensive experiments on real-world datasets under barely supervised settings demonstrate the effectiveness of FRAD-DM. By integrating it into various GNN architectures for node-level and edge-level anomaly detection tasks, negative-class F1-scores improve by 5.44% to 20.98%. These results highlight its capability to address the challenges of directed multigraphs in practical applications.
Adaptive Conformal Prediction Intervals for Invariant Learning
Authors: Shuxin Liang, Yihan Xiao, Linglong Kong, Wenlu Tang
Venue: KDD
Year: 2025

Distribution shifts between training and testing datasets can significantly affect the effectiveness of machine learning models, as the typical assumption of data homogeneity often does not hold in practical applications. To address this challenge, previous literature explores invariant representation learning methods, including but not limited to domain adversarial learning, feature alignment, and invariant risk minimization. In this paper, we introduce a novel method for generating distribution-free prediction regions, which effectively quantify the uncertainty of predictions in different environments. Our methodology incorporates a weighted conformity score, tailored to the environment of each test sample, to construct adaptive conformal intervals. The proposed method is computationally efficient and can be implemented by subsampling. We demonstrate how these intervals maintain a consistent conditional coverage under some conditions. The numerical experiments, including simulations and real data analysis, show that our method is applicable and robust in dealing with distribution shifts.