paper_name stringlengths 4 421 | paper_url stringlengths 21 200 | paper_authors listlengths 0 125 | paper_abstract stringlengths 0 43.4k | paper_code stringlengths 1 149 | conf stringlengths 6 18 |
|---|---|---|---|---|---|
When to Trust Whom: A Context-Aware Graph Routing Mechanism for Information Diffusion Prediction | https://doi.org/10.1145/3770855.3818163 | [
"Zihan Feng",
"Yajun Yang",
"Rui Wu",
"Xin Huang",
"Hong Gao",
"Qinghua Hu"
] | Information diffusion prediction forecasts future participants from an observed cascade prefix, enabling proactive intervention in applications such as viral marketing and misinformation mitigation. Most existing models leverage two data sources: the global social graph (exposure/trust pathways) and cascade-induced int... | # | KDD2026 |
Bounded-Abstention Pairwise Learning to Rank | https://doi.org/10.1145/3770855.3817918 | [
"Antonio Ferrara",
"Andrea Pugnana",
"Francesco Bonchi",
"Salvatore Ruggieri"
] | Ranking systems influence decision-making in high-stakes domains like health, education, and employment, where they can have substantial economic and social impacts. This makes the integration of safety mechanisms essential. One such mechanism is abstention, which enables algorithmic decision-making systems to defer un... | # | KDD2026 |
ResDIF: A Residual Disentanglement Framework for Interpretable Financial Time Series Forecasting via Spectrally-Enhanced Temporal Encoding | https://doi.org/10.1145/3770855.3818080 | [
"Chengwei Fu",
"Gang Xiao",
"Yuchao Zhang",
"Yuhang Sun",
"Jiange Li",
"Yue Deng"
] | Financial time series forecasting tasks, specifically stock trend prediction and market attribution, are essential for quantitative investment and risk control. However, these tasks suffer from low signal-to-noise ratios and non-stationarity, stemming from the coupling of hierarchical drivers: market trends, sector rot... | # | KDD2026 |
Lifting the Veil of Non-Stationarity in Financial Market | https://doi.org/10.1145/3770855.3817881 | [
"Vincent Fu",
"Xinxin Xu",
"Xuanmeng Zhang",
"Weichen Xu",
"Ruilong Ren",
"Bowen Deng",
"Xinyu Zhao",
"Jian Cao",
"Xixin Cao"
] | Financial asset price movement prediction is inherently challenging due to the non-stationary nature of financial markets, where data distributions shift over time. Existing methods often assume that the market is stationary, which limits their applicability. To address this, we propose the Market-State Jump Diffusion ... | # | KDD2026 |
Exact k-Center Clustering on Graphs for Small k | https://doi.org/10.1145/3770855.3818102 | [
"Stefan Funke",
"Sabine Storandt"
] | k-center clustering on graphs is widely used in data mining tasks such as prototype selection, facility placement, and dataset summarization. Despite its importance, practitioners rely almost entirely on approximation algorithms or heuristics due to the perceived impracticality of exact computation. We show that exact ... | # | KDD2026 |
Label-consistent Clustering for Evolving Data | https://doi.org/10.1145/3770855.3817933 | [
"Ameet Gadekar",
"Aristides Gionis",
"Thibault Marette"
] | Data analysis often involves an iterative process, where solutions must be continuously refined in response to new data. Typically, as new data becomes available, an existing solution must be updated to incorporate the latest information. In addition to seeking a high-quality solution for the data-analysis task, it is ... | # | KDD2026 |
Observationally Informed Adaptive Causal Experimental Design | https://doi.org/10.1145/3770855.3817762 | [
"Erdun Gao",
"Liang Zhang",
"Jake Fawkes",
"Aoqi Zuo",
"Wenqin Liu",
"Haoxuan Li",
"Mingming Gong",
"Dino Sejdinovic"
] | Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is typically used only for retrospective fusion, and remains discarded in prospective trial design due to bias concerns. We argue that this ''tab... | # | KDD2026 |
Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting | https://doi.org/10.1145/3770855.3818066 | [
"Mengzhou Gao",
"Huangqian Yu",
"Pengfei Jiao"
] | Time series in real-world applications are often generated by nonlinear dynamical systems, making accurate forecasting challenging. Existing approaches that explicitly model system dynamics typically rely on linear assumptions or Koopman-based linearizations, which may inadequately capture complex nonlinear behaviors a... | # | KDD2026 |
Mapping LLM Capability Frontiers via Formalized and Calibrated Probes | https://doi.org/10.1145/3770855.3818029 | [
"Tianxi Gao",
"Yufan Cai",
"Yusi Yuan",
"Jin Song Dong"
] | Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emphasize task-level accuracy, often conflating pattern matching with reasoning capability. We present X-RAY, an eXplainable Reasoning Analysis sYstem that maps the LLM reasoni... | # | KDD2026 |
On Optimizing Route-Responsive Urban Tree Placement in City-Scale Pedestrian Networks | https://doi.org/10.1145/3770855.3817687 | [
"Tianyou Gao",
"Takayuki Ito"
] | Urban tree siting is widely studied in environmental science and urban planning, yet is comparatively less explored from an algorithmic optimization perspective. We introduce a modeling framework for shade-aware tree siting and formulate the Route-responsive Urban Tree Optimization (RUTO) problem, which selects k plant... | # | KDD2026 |
R-Select: A Robust Multi-Metric Data Selection Approach for Fine-Tuning Large Language Models | https://doi.org/10.1145/3770855.3817656 | [
"Xin Gao",
"Xiaoyang Wang",
"Yun Zhu",
"Zheng Liu",
"Conghui He",
"Lijun Wu"
] | The transition from architecture-centric scaling to data-centric refinement has established high-quality data as a critical determinant of Large Language Model performance, particularly for complex reasoning and instruction following. However, effective data selection remains a persistent bottleneck: simple heuristic f... | # | KDD2026 |
ReSOT: Re-balance Semantic ID with Optimal Transport for Generative Recommendation | https://doi.org/10.1145/3770855.3817834 | [
"Renwu Geng",
"Yiming Xu",
"Fengxin Li",
"Fan Wang",
"Xiang Liu",
"Jun Wang",
"Chaochao Chen"
] | Generative recommendation (GR) reformulates sequential recommendation as an autoregressive generation problem, where items are represented as discrete semantic IDs. However, learning effective item tokenization is critical yet remains challenging. Most existing methods optimize tokenization in a point-wise or heuristic... | https://github.com/grw-zju/ReSOT | KDD2026 |
Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach | https://doi.org/10.1145/3770855.3818086 | [
"Fatemeh Ghaffari",
"Siddarth Sitaraman",
"Xutong Liu",
"Xuchuang Wang",
"Mohammad Hajiesmaili"
] | Online learning to rank (ØLTR ) studies how to recommend a short ranked list of items from a large pool and improves future rankings based on user clicks. This setting is commonly modeled as cascading bandits, where the objective is to maximize the likelihood that the user clicks on at least one of the presented items ... | # | KDD2026 |
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty | https://doi.org/10.1145/3770855.3818087 | [
"Riku Green",
"Zahraa S. Abdallah",
"Telmo de Menezes e Silva Filho"
] | Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a sufficient target. We show that this can be misleading under conditional uncertainty, where the conditional expectation becomes unrepresentative o... | # | KDD2026 |
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies | https://doi.org/10.1145/3770855.3817890 | [
"Ekaterina Grishina",
"Stepan L. Kuznetsov",
"Askar Tsyganov",
"Ilya Ivanov",
"Daria Korovaitceva",
"Margarita Rusanova",
"Uliana Parkina",
"Alexander Derevyagin",
"Evgeny Frolov",
"Sergey Samsonov",
"Anton Lysenko"
] | The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comparison between algorithms. Naive aggregation of performance metrics (e.g., avera... | # | KDD2026 |
HyMAGE: Semantic-Aware Dynamic Hypergraph Generation | https://doi.org/10.1145/3770855.3817897 | [
"Bingqiao Gu",
"Jiale Zeng",
"Nuoran Zhou",
"Xingqin Qi",
"Dong Li"
] | Understanding hypergraph evolution is essential for revealing high-order interaction patterns and generating credible synthetic data when real interaction records are scarce. Existing models suffer from two key limitations: (1) they rely on global topological heuristics that treat nodes as passive entities, yielding po... | # | KDD2026 |
When Compilation Breaks Your GNN: A Numerical Stability Perspective | https://doi.org/10.1145/3770855.3817799 | [
"Jiawei Gu",
"Zechao Li"
] | Deep learning compilers like torch.compile accelerate GNNs through operator fusion and computation reordering, yet often introduce numerical instability that causes models to diverge under hyperparameters that work in eager mode. We trace this fragility to the interaction between compiler optimizations and GNN structur... | # | KDD2026 |
The Hidden Fragility of GNNs: How Graph Structure Amplifies Numerical Errors | https://doi.org/10.1145/3770855.3818182 | [
"Jiawei Gu",
"Ziyue Qiao"
] | Graph Neural Networks exhibit a puzzling numerical fragility under mixed-precision training, failing significantly more often than MLPs or CNNs. This failure is inherently tied to graph structure, with heterophilic graphs and high-degree nodes being particularly vulnerable. We identify the root cause as catastrophic ca... | # | KDD2026 |
PROBE: VLM-Guided Discrete Structural Reconfiguration for Customized and Efficient Video Retrieval | https://doi.org/10.1145/3770855.3818016 | [
"Yiyang Gu",
"Kaili Liu",
"Tao Zhe",
"Binqi Chen",
"Jiayue Fan",
"Junwei Yang",
"Zequn Liu",
"Zhiping Xiao",
"Chong Chen",
"Xiao Luo",
"Xian-Sheng Hua",
"Ming Zhang"
] | Existing self-supervised video hashing methods achieve high efficiency by encoding videos into compact binary representations, but they typically rely on a fixed global similarity geometry that enforces a single notion of similarity across all queries. In many real-world retrieval scenarios, however, the same videos ma... | https://github.com/liamgu06/PROBE | KDD2026 |
Advancing Graph Few-Shot Learning via In-Context Learning | https://doi.org/10.1145/3770855.3817797 | [
"Renchu Guan",
"Yajun Wang",
"Chunli Guo",
"Bowen Cao",
"Fausto Giunchiglia",
"Wei Pang",
"Yonghao Liu",
"Xiaoyue Feng"
] | Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning. However, existing methods often face two key limitations. First, the predominant graph few-shot learning paradigm relies on supervised tasks, failing to leverage the ... | # | KDD2026 |
StablePFN: Stable Prediction with Causal-Aware Tabular Foundation Model | https://doi.org/10.1145/3770855.3818030 | [
"Zhengkang Guan",
"Yikang Chen",
"Haoyuan Qian",
"Kairong Han",
"Peng Cui",
"Fei Wu",
"Kun Kuang"
] | Pre-trained tabular prediction models based on Prior-Data Fitted Networks (PFNs), such as TabPFN and LimiX, have achieved remarkable progress in supervised learning, demonstrating immense potential across real-world scenarios and diverse downstream tasks. However, a critical question remains systematically unexplored: ... | # | KDD2026 |
From Rows to Reasoning: A Retrieval-Augmented Multimodal Framework for Spreadsheet Understanding | https://doi.org/10.1145/3770855.3817962 | [
"Anmol Gulati",
"Sahil Sen",
"Waqar Sarguroh",
"Kevin Paul"
] | Large Language Models (LLMs) struggle to reason over large-scale enterprise spreadsheets containing thousands of numeric rows, multiple linked sheets, and embedded visual content such as charts and receipts. Prior state-of-the-art spreadsheet reasoning approaches typically rely on single-sheet compression or full-conte... | # | KDD2026 |
FedFST: Mitigating Spectral Catastrophic Forgetting in Federated Graph Continual Learning | https://doi.org/10.1145/3770855.3817730 | [
"Hanyao Guo",
"Zihan Tan",
"Wenke Huang",
"Bin Yang",
"Mang Ye"
] | Federated Graph Learning (FGL) enables privacy-preserving GNN training over distributed graph data, yet dynamic task streams in Federated Graph Continual Learning (FGCL) inevitably lead to catastrophic forgetting. From a spectral perspective, this forgetting manifests as two fundamental challenges: high-frequency incon... | https://github.com/YunQi572/FedFST.git | KDD2026 |
Why Retrieval-Augmented Generation Fails: A Graph Perspective | https://doi.org/10.1145/3770855.3818101 | [
"Kai Guo",
"Xinnan Dai",
"Zhibo Zhang",
"Nuohan Lin",
"Shenglai Zeng",
"Jie Ren",
"Haoyu Han",
"Jiliang Tang"
] | Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in retrieved evidence. However, RAG systems still produce incorrect answers in many cases. Why RAG fails despite having access to external information remains poorly understood... | https://github.com/KaiGuo20/Circuit_RAG | KDD2026 |
Hierarchical Residual Policy Optimization for Generative Recommendations | https://doi.org/10.1145/3770855.3818206 | [
"Kaifeng Guo",
"Yiming Yang",
"Jingtong Gao",
"Guolei Zeng",
"Fukang Yang",
"Yukang Liang",
"Peng Jiang",
"Qingpeng Cai",
"Xiangyu Zhao"
] | Generative recommenders select items by autoregressively decoding semantic identifiers (SIDs), whose token positions induce a coarse-to-fine hierarchy over the item space. In practice, SID decoders are trained via supervised next-token prediction, which imitates logged trajectories rather than directly optimizing downs... | # | KDD2026 |
Embedding-Space Orthogonal Decomposition for Robust Social Recommendation | https://doi.org/10.1145/3770855.3817864 | [
"Rongfeng Guo",
"Yinxuan Huang",
"Wei Chen",
"Mingyang Zhou",
"Yusen Wu",
"Yangchen Zeng",
"Han Chen",
"Hao Liao"
] | Graph-based social recommendation leverages both the interaction graph and the social graph to model user preferences, especially under sparse feedback. However, users' intricate social behaviors may introduce mismatched social ties that contaminate user representations and harm the models' robustness. The majority of ... | # | KDD2026 |
Alignment-Free Multi-Modality Large-Small Model Bidirectional Collaboration with Missing Modality | https://doi.org/10.1145/3770855.3817904 | [
"Wei Guo",
"Jiale Mao",
"Yiqi Tong",
"Chuyu Fang",
"Xiao Zhang",
"Yikun Ban",
"Zhaojun Hu",
"Yiyang Duan",
"Fuzhen Zhuang"
] | Different from existing single-modality large-small model collaborations, multi-modality large-small model collaboration is a key but under-explored paradigm where cloud-side multi-modality large model (MM-LM) collaborates with edge-side small models (SMs) to achieve bidirectional task improvements. Nevertheless, this ... | # | KDD2026 |
Federated Nonlinear Causal Discovery via Divide-and-Conquer Learning | https://doi.org/10.1145/3770855.3818073 | [
"Xianjie Guo",
"Shuai Yang",
"Lin Ma",
"Xi Cheng",
"Jie Fu",
"Han Yu"
] | Federated causal discovery aims to learn causal structures from distributed data without sharing raw samples. Existing federated nonlinear methods adopt a monolithic global strategy that optimizes the entire graph simultaneously, suffering from catastrophic error propagation: a single misidentified edge cascades throug... | https://github.com/Xianjie-Guo/DC-FNCD | KDD2026 |
Generalizing GNNs with Tokenized Mixture of Experts | https://doi.org/10.1145/3770855.3817952 | [
"Xiaoguang Guo",
"Zehong Wang",
"Jiazheng Li",
"Shawn Spitzel",
"Qi Yang",
"Kaize Ding",
"Jundong Li",
"Chuxu Zhang"
] | Deployed graph neural networks (GNNs) operate as frozen snapshots, yet must simultaneously fit clean data, generalize under distribution shifts, and remain stable against input perturbations---three goals that are difficult to satisfy at once with a single fixed model. We first show theoretically that a single fixed in... | https://github.com/GXG-CS/STEM-GNN | KDD2026 |
Text-Guided Visual Representation Learning for Robust Multimodal E-Commerce Recommendation | https://doi.org/10.1145/3770855.3818176 | [
"Yufei Guo",
"Jing Ma",
"Yixuan Dong",
"Tianlu Zhang",
"Shijie Yang",
"Yanlong Zang",
"Weijie Ding",
"Pinghua Gong",
"Jungong Han"
] | Multimodal item embeddings are crucial for e-commerce item-to-item (I2I) retrieval, yet real-world product images often contain promotional overlays and background clutter that inject spurious visual cues and degrade retrieval robustness. This issue is particularly pronounced in MLRM-style pipelines, where a frozen vis... | # | KDD2026 |
S2GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation | https://doi.org/10.1145/3770855.3817720 | [
"Zihao Guo",
"Jian Wang",
"Ruxin Zhou",
"Youhua Liu",
"Jiawei Guo",
"Jun Zhao",
"Xiaoxiao Xu",
"Yongqi Liu",
"Kaiqiao Zhan"
] | Generative Recommendation (GR) has emerged as a transformative paradigm with its end-to-end generation advantages. However, existing GR methods primarily focus on direct semantic ID (SID) generation from interaction sequences, failing to activate deeper reasoning capabilities analogous to those in large language models... | # | KDD2026 |
FedTail-DT: A Dual-Teacher Framework for Long-Tailed Heterogeneous FL with CLIP Prototypes and Adaptive Aggregation | https://doi.org/10.1145/3770855.3817728 | [
"Zijie Guo",
"Jinghua Zhu",
"Gang Du",
"Kejia Zhang"
] | Federated learning enables decentralized model training while preserving data privacy, showing significant potential in sensitive fields such as healthcare and finance. However, real-world client data often exhibits both heterogeneity and long-tailed label distributions, leading to class bias and knowledge gaps that li... | # | KDD2026 |
Out-of-Distribution Robust Explainer for Graph Neural Networks | https://doi.org/10.1145/3770855.3817721 | [
"Geonhee Han",
"Heesoo Jung",
"Hyunju Kang",
"Hogun Park"
] | Graph Neural Networks (GNNs) have become widely used for analyzing graph-structured data, motivating post-hoc explanation methods for interpreting pre-trained GNNs. However, most existing explainers have primarily been designed and evaluated in settings where the inference graph is structurally or distributionally simi... | https://github.com/gunhee8178/ORExplainer | KDD2026 |
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning | https://doi.org/10.1145/3770855.3817801 | [
"Xiaosong Han",
"Ke Chen",
"Xindi Dai",
"Di Liang",
"Minlong Peng",
"Wei Pang",
"Fausto Giunchiglia",
"Xiaoyue Feng",
"Yonghao Liu",
"Renchu Guan"
] | In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. However, indiscriminately mixing tasks can dilute task specialization, while sequential fine-tuning (full-parameter or low rank adaptation) of... | # | KDD2026 |
SketchBuilder: Learning-Augmented Proactive Sketch Construction for Heavy Hitter Detection in Data Streams | https://doi.org/10.1145/3770855.3817652 | [
"Yifan Han",
"Yang Du",
"Yu-E. Sun",
"He Huang",
"Xiaocan Wu"
] | Heavy hitter detection is a fundamental problem in data stream processing with broad applications. State-of-the-art self-adjusting sketches adapt to skewed streams by repeatedly adjusting local memory structures at runtime. However, they largely overlook global sketch-level collisions that persistently map multiple hea... | # | KDD2026 |
TTMC: Brain-Inspired Test-Time Memory Calibration with Orthogonal Projection for Online Continual Learning | https://doi.org/10.1145/3770855.3818072 | [
"Yuyang Han",
"Ziyu Li",
"Diwei Su",
"Shihao Zhang",
"Qing Li",
"Zhiying Long",
"Xia Wu"
] | Existing Online Continual Learning (OCL) methods, particularly those based on Parameter-Efficient Fine-Tuning (PEFT), predominantly operate under a Static Inference Assumption, which means freezing model parameters immediately after training. This paradigm ignores the inevitable covariate shift in non-stationary test s... | https://github.com/hanyuyang99/TTMC.git | KDD2026 |
SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems | https://doi.org/10.1145/3770855.3817807 | [
"Haochang Hao",
"Yifan Xu",
"Xinzhuo Li",
"Yingqiang Ge",
"Lu Cheng"
] | Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an underexplored vulnerability in which recommendation outputs may negatively impact users by violating personalized safety constraints, when individualized safety sensitivities—such ... | # | KDD2026 |
Recall-Aware Early Termination in Approximate Nearest Neighbor Search | https://doi.org/10.1145/3770855.3817686 | [
"Shuang Hao",
"Xinxin Li",
"Wei Zhang"
] | Approximate nearest neighbor search (ANNS) is a fundamental operation in large-scale vector retrieval systems, where achieving high recall under strict latency constraints is essential. Existing ANNS approaches typically control recall using fixed search parameters, such as a predefined candidate neighbor set (CNS) siz... | https://github.com/lxxabb/Recall-Aware-Early-Termination-in-Approximate-Nearest-Neighbor-Search | KDD2026 |
Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling | https://doi.org/10.1145/3770855.3818068 | [
"Xixuan Hao",
"Yutian Jiang",
"Jiabo Liu",
"Yihang Yang",
"Guangyin Jin",
"Song Gao",
"Yuxuan Liang"
] | Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring. Existing methods typically formulate this task as multimodal representation learning, fusing heterogeneous urban data—e.g., satellite imagery, p... | # | KDD2026 |
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection | https://doi.org/10.1145/3770855.3818062 | [
"Hui He",
"Hezhe Qiao",
"Yutong Chen",
"Kun Yi",
"Guansong Pang"
] | Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstream tasks (\eg, forecasting). Consequently, they are often ineffective for inherently unsupervised downstream tasks—such as time series anomal... | https://github.com/mala-lab/TimeRadar | KDD2026 |
FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting | https://doi.org/10.1145/3770855.3818005 | [
"Peng He",
"Yao Liu",
"Yanglei Gan",
"Run Lin",
"Yuxiang Cai",
"Qiao Liu"
] | While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes. Recent advancements h... | # | KDD2026 |
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection | https://doi.org/10.1145/3770855.3817967 | [
"Xinrui He",
"Mengting Ai",
"Junting Wang",
"Curtiss B. Cook",
"Jingrui He"
] | Clinical care often relies on key laboratory indicators, yet real-world patient visits are sparse and tests are ordered irregularly, leading to pervasive missingness. While many imputation methods improve average accuracy, they provide limited guidance on which imputed values are reliable enough for high-stakes downstr... | https://github.com/Xinrui17/SafeImpute | KDD2026 |
Reasoning over Semantic IDs Enhances Generative Recommendation | https://doi.org/10.1145/3770855.3818122 | [
"Yingzhi He",
"Yan Sun",
"Junfei Tan",
"Yuxin Chen",
"Xiaoyu Kong",
"Chunxu Shen",
"Xiang Wang",
"An Zhang",
"Tat-Seng Chua"
] | Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SI... | https://github.com/HappyPointer/SIDReasoner | KDD2026 |
Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models | https://doi.org/10.1145/3770855.3817805 | [
"Yuting He",
"Chenyu You",
"Shuo Li"
] | Multi-modality medical vision (MV) foundation models (FM) are fundamentally challenged by pronounced Non-IID feature statistics across heterogeneous imaging modalities. Monolithic self-supervised optimization on such data induces conflicting gradients, driving representations to collapse toward modality-dominant shortc... | https://github.com/YutingHe-list/DEX | KDD2026 |
Invariant-Stratified Propagation for Expressive Graph Neural Networks | https://doi.org/10.1145/3770855.3818116 | [
"Asela Hevapathige",
"Ahad N. Zehmakan",
"Asiri Wijesinghe",
"Saman K. Halgamuge"
] | Graph Neural Networks (GNNs) face fundamental limitations in expressivity and capturing structural heterogeneity. Standard message-passing architectures are constrained by the 1-dimensional Weisfeiler-Leman (1-WL) test, unable to distinguish graphs beyond degree sequences, and aggregate information uniformly from neigh... | https://github.com/Aselahp/ISP-GNN | KDD2026 |
GaussTrap: Stealthy Backdoor Attacks on 3D Gaussian Splatting for Targeted Scene Misperception | https://doi.org/10.1145/3770855.3817947 | [
"Jiaxin Hong",
"Sixu Chen",
"Shuoyang Sun",
"Hongyao Yu",
"Hao Fang",
"Yuxin Peng",
"Bin Chen",
"Jiawei Li"
] | 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for real-time scene representation and novel view synthesis, gaining traction in safety-critical applications such as autonomous driving and AR/VR systems. However, the security vulnerabilities of 3DGS remain largely unexplored. In this paper, we ... | https://github.com/acang425/GaussTrap | KDD2026 |
SUDER: Self-Improving Unified Large Multimodal Models for Understanding and Generation with Dual Self-rewards | https://doi.org/10.1145/3770855.3817654 | [
"Jixiang Hong",
"Yiran Zhang",
"Guanzhong Wang",
"Yi Liu",
"Ji-Rong Wen",
"Rui Yan"
] | Building upon large language models (LLMs), recent large multimodal models (LMMs) unify cross-modal understanding and generation into a single framework. However, LMMs still struggle to achieve accurate vision-language alignment, prone to generating text responses contradicting the visual input or failing to follow the... | # | KDD2026 |
Implicit Fine-tuning via Context Engineering: A Curriculum Learning Framework for Multimodal Entity Alignment | https://doi.org/10.1145/3770855.3817732 | [
"Yunpeng Hong",
"Chenyang Bu",
"Di Wu",
"Yi He",
"Xindong Wu"
] | Multimodal Entity Alignment (MMEA) aims to identify equivalent entities across different modalities. While existing methods enhance MMEA performance through black-box context engineering strategies, their reliance on LLM parameter capacity and lack of theoretical interpretability remain unresolved. To this end, we firs... | https://github.com/DMiC-Lab-HFUT/PTFEA | KDD2026 |
ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement | https://doi.org/10.1145/3770855.3818164 | [
"Zijin Hong",
"Hao Chen",
"Zheng Yuan",
"Qinggang Zhang",
"Luyao Zhuang",
"Qing Liao",
"Feiran Huang",
"Yangqiu Song",
"Xiao Huang"
] | Despite the remarkable performance of large language models (LLMs) in text-to-SQL, correctly producing SQL queries remains challenging during initial generation. The SQL refinement task is subsequently introduced to correct syntactic and semantic errors. However, existing paradigms face two major limitations: (i) self-... | # | KDD2026 |
Core-based Hierarchies for Efficient GraphRAG | https://doi.org/10.1145/3770855.3818007 | [
"Jakir Hossain",
"Ahmet Erdem Sariyüce"
] | Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge. However, existing vector-based methods often fail on global sensemaking tasks that require reasoning across many documents. GraphRAG addresses this by organizing documents into a knowledge graph with hierarchical co... | # | KDD2026 |
EvoFEND: Dual Memory-Driven Self-Evolving Fake News Detection | https://doi.org/10.1145/3770855.3817673 | [
"Beizhe Hu",
"Qiang Sheng",
"Hao Mi",
"Jiaying Wu",
"Zhengjia Wang",
"Yuanlong Yu",
"Danding Wang",
"Xuming Hu",
"Juan Cao"
] | Real-world fake news is inherently dynamic: evidence within an event accumulates and conflicts over time, while deceptive tactics shift across events. However, most prior work formulates detection as a static, one-shot classification problem over fixed snapshots. This mismatch ignores the lifecycle of news and leaves d... | # | KDD2026 |
Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks | https://doi.org/10.1145/3770855.3817693 | [
"Haoji Hu",
"Huaqing Mao",
"Yijun Lin",
"Xiaowei Jia",
"Jinwei Zhou",
"Minoh Jeong",
"Yao-Yi Chiang"
] | Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust way to quantify dependence between heterogeneous data types, especially between continuous time series and discrete temporal event sequences. Existing approaches rely on ad... | https://github.com/HaojiHu/Multimodal-Temporal-Data-Quantification | KDD2026 |
Purify and Generalize: Efficient Dual-End Adapters for Sequential Recommendation | https://doi.org/10.1145/3770855.3818208 | [
"Juntao Hu",
"Wei Zhou",
"Huayi Shen",
"Junhao Wen",
"Hongyu Zhang"
] | Despite rapid advances in backbone architectures for sequential recommendation, existing approaches still suffer from two challenges. First, user behavior sequences contain noisy interactions that corrupt input representations and mislead the modeling process. Second, models often converge to sharp minima that overfit ... | # | KDD2026 |
VeriHGN: Heterogeneous Graph-Based Congestion Prediction for Chip Layout Verification | https://doi.org/10.1145/3770855.3818099 | [
"Runbang Hu",
"Bo Fang",
"Bingzhe Li",
"Yuede Ji"
] | As Very Large Scale Integration (VLSI) designs continue to scale in size and complexity, layout verification has become a central challenge in modern Electronic Design Automation (EDA) workflows. In practice, congestion can only be accurately identified after detailed routing, making traditional verification both time-... | # | KDD2026 |
DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark | https://doi.org/10.1145/3770855.3817680 | [
"Ruofan Hu",
"Menghui Zhu",
"Jieming Zhu",
"Bo Chen",
"Shengyang Xu",
"Minjie Hong",
"Xiaoda Yang",
"Sashuai Zhou",
"Li Tang",
"Tao Jin",
"Zhou Zhao"
] | Multimodal documents contain diverse elements, such as tables, figures, and layouts, which can complicate retrieval tasks. While current approaches typically combine dense visual embedding models with supervised rerankers to achieve high-precision retrieval, they face inherent limitations. First, the coarse-grained nat... | # | KDD2026 |
Neural Stick-Breaking for Cascade Forensics | https://doi.org/10.1145/3770855.3817623 | [
"Shanfeng Hu"
] | Characterising transmission laws in epidemic cascades is essential for outbreak forensics, yet neural models with finite support fail to capture heavy-tailed phenomena like superspreading. We propose the Neural Stick-Breaking (NSB) process, a neuro-symbolic-spectral duality that learns infinite-support offspring distri... | https://github.com/shanfenghu/nsb | KDD2026 |
Test-Time Search for Automated GFM Fine-Tuning | https://doi.org/10.1145/3770855.3817853 | [
"Wenji Hu",
"Xianan Wang",
"Chunyu Wei",
"Senhao Liu",
"Kuien Liu",
"Yunhai Wang",
"Yueguo Chen"
] | Graph Foundation Models (GFMs) have emerged as a powerful paradigm for learning transferable graph representations, yet adapting them to downstream tasks requires navigating an exponentially large decision space, traditionally demanding heavy expert effort. We propose GFMTuner, a framework that automates GFM fine-tunin... | https://github.com/GFMTuner/GFMTuner | KDD2026 |
Kairos: Time-Sensitive Scheduling for Ad-Oriented ML Workloads with Heterogeneous Time-Utility Functions | https://doi.org/10.1145/3770855.3818001 | [
"Xun Hu",
"Luyao Luo",
"Yu-e Sun",
"He Huang"
] | Digital advertising relies heavily on machine learning models for accurate recommendations, yet the training tasks for these models exhibit unique time-sensitive characteristics that are often overlooked by current scheduling systems. Unlike general-purpose ML workloads, recommendation tasks are highly time-sensitive b... | # | KDD2026 |
StaR: Stateful Dynamic-Graph Root Cause Analysis through Memory-Enhanced Causality Discovery | https://doi.org/10.1145/3770855.3817863 | [
"Haiyu Huang",
"Man Tik Ng",
"Jiewei Lyu",
"Yujie Huang",
"Guangba Yu",
"Yilun Wang",
"Michael R. Lyu"
] | Identifying the root causes of anomalies in complex multivariate time series systems (e.g., microservices) is critical for maintaining service reliability. Although causal discovery-based approaches have gained traction, existing methods suffer largely from two fundamental limitations: they assume static causal relatio... | # | KDD2026 |
Aquavit: Ascending Quantization for Communication-Efficient Vast-Scale Distributed Training | https://doi.org/10.1145/3770855.3818083 | [
"Hong Huang",
"Jiaxun Ye",
"Jinhai Yang",
"Wenjiao Feng",
"Zonghang Li",
"Ning Chen"
] | Training Large Foundation Models (LFMs), including Large Language Models and Vision-Language Models, on massive distributed GPU clusters is increasingly bottlenecked by communication overhead. While frameworks like ZeRO++ employ static quantization to reduce communication volume, they suffer from a rigid trade-off: agg... | # | KDD2026 |
H4G: Unlocking Faithful Inference for Zero-Shot Graph Learning in Hyperbolic Space | https://doi.org/10.1145/3770855.3817675 | [
"Heng Zhang",
"Jin Huang"
] | Text-attributed graphs are widely used across domains, offering rich opportunities for zero-shot learning via graph-text alignment. However, existing methods struggle with tasks requiring fine-grained pattern recognition, particularly on heterophilic graphs. Through empirical and theoretical analysis, we identify an ov... | # | KDD2026 |
CR-Aug: Community Risk-Guided Adaptive Augmentation for Semi-supervised Graph Anomaly Detection | https://doi.org/10.1145/3770855.3818003 | [
"Jing Huang",
"Yicun Liu",
"Zhixin Li",
"Yinan Jing",
"Hongfeng Chai"
] | Recently, semi-supervised graph anomaly detection (GAD) has garnered increasing attention under a challenging setting where only a limited number of normal nodes are labeled during training. To better exploit the limited normal supervision and compensate for the absence of real anomaly labels, existing methods often ad... | # | KDD2026 |
Parameterized Fair Resource Allocation under Diversity Constraints | https://doi.org/10.1145/3770855.3817920 | [
"Keke Huang",
"Yik Yu Ng",
"Laks V. S. Lakshmanan",
"Xiaokui Xiao"
] | Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness. Existing approaches typically enforce these cons... | # | KDD2026 |
PrePrompt: Predictive Prompting for Class Incremental Learning | https://doi.org/10.1145/3770855.3817682 | [
"Libo Huang",
"Xiangqi Li",
"Jiarui Zhao",
"Zhulin An",
"Chuanguang Yang",
"Boyu Diao",
"Fei Wang",
"Yan Zeng",
"Zhifeng Hao",
"Yongjun Xu"
] | Prompt-based learning has emerged as a promising paradigm for Class Incremental Learning (CIL), enabling pre-trained models to adapt efficiently to open-world scenarios. Existing methods often employ correlation-based strategies, where an image's feature serves as a query to retrieve the most relevant key prompts, with... | https://github.com/libo-huang/preprompt | KDD2026 |
Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation | https://doi.org/10.1145/3770855.3818198 | [
"Rizhuo Huang",
"Yifan Feng",
"Rundong Xue",
"Shihui Ying",
"Jun-Hai Yong",
"Chuan Shi",
"Shaoyi Du",
"Yue Gao"
] | Knowledge hypergraphs surpass traditional binary knowledge graphs by encapsulating complex n-ary atomic facts, providing a more comprehensive paradigm for semantic representation. However, constructing high-quality hypergraphs remains challenging due to the scenario gap : generic extractors struggle to generalize acros... | https://github.com/Rizrock/Hyper-KGGen | KDD2026 |
SEA-FGT: Frequency-Guided Transformer with Semantic Expert Augment for Time Series Anomaly Detection | https://doi.org/10.1145/3770855.3817823 | [
"Wei Huang",
"Zhihong Wang",
"Yanyong Huang",
"Jia Liu",
"Xiaocao Ouyang"
] | Multivariate Time Series (MTS) anomaly detection is fundamental to industrial monitoring and intelligent operations. However, practical deployment remains difficult, as real-world MTS exhibit complex inter-channel dependencies and anomaly-induced perturbations, which are often subtle in time domain. Moreover, the impli... | # | KDD2026 |
SCOPE: Cost-Efficient Model Selection for Compound AI Systems under Quality Constraints | https://doi.org/10.1145/3770855.3818067 | [
"Yiqian Huang",
"Shiqi Zhang",
"Tianyuan Jin",
"Xiaokui Xiao"
] | A compound AI system consists of multiple LLM modules, together handling complex and multi-step tasks that exceed the capabilities of a single model. Existing systems often use a single expensive LLM across all modules to improve the result quality of the whole system. However, this configuration incurs prohibitive cos... | # | KDD2026 |
Broken Memories: Detecting and Mitigating Memorization in Diffusion Models with Degraded Generations | https://doi.org/10.1145/3770855.3817770 | [
"Yuanmin Huang",
"Mi Zhang",
"Chen Chen",
"Feifei Li",
"Geng Hong",
"Xiaoyu You",
"Min Yang"
] | While diffusion models excel at generating high-quality images, their tendency to memorize training data poses significant privacy and copyright risks. In this work, we for the first time identify that memorization induces internal numerical instability, often manifesting as visually ``broken'' artifacts. Inspired by s... | # | KDD2026 |
Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets | https://doi.org/10.1145/3770855.3817677 | [
"Minyoung Hwang",
"Seokhyun Lee",
"Changhee Lee"
] | As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability. However, achieving this vital level of interpretability is particularly challenging when these DLMs operate as blac... | # | KDD2026 |
Extracting Explainable Temporal Features in Multivariate Time Series Classification Pipelines | https://doi.org/10.1145/3770855.3818014 | [
"Ido Ikar",
"Amit Somech"
] | Multivariate Time Series Classification (MTSC) is a central task in modern data analytics, with growing impact across domains such as healthcare, finance, and industrial monitoring. As MTSC models are increasingly used in real-world decision-making, the need for explainability has become critical. Existing solutions ei... | https://github.com/analysis-bots/EFFECTS | KDD2026 |
Diffusion-based Spatio-temporal Interpolation with Dynamic Sensor Sets | https://doi.org/10.1145/3770855.3817811 | [
"Mohammad Rafid Ul Islam",
"Prasad Tadepalli",
"Alan Fern"
] | We tackle spatio-temporal interpolation for virtual sensors in sparse, partially observed, and dynamically changing networks. We introduce DynaSTI, a diffusion-based generative framework that is fully inductive to unseen locations, trains directly on incomplete observations, and remains effective without retraining whe... | # | KDD2026 |
DuET: Dual-View Tensor-to-Topology Spectral Adapter for Enhancing Sparse Tensor Factorization | https://doi.org/10.1145/3770855.3817884 | [
"Jun-Gi Jang",
"Jingrui He",
"Andrew J. Margenot",
"Hanghang Tong"
] | Many real-world datasets, ranging from web-interaction logs to biomedical networks, can be represented as sparse tensors where most entries are unobserved. Tensor factorization (TF) learns latent representations and a predictor to estimate unobserved entries and has been widely applied to higher-order recommendation, b... | # | KDD2026 |
Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation | https://doi.org/10.1145/3770855.3818040 | [
"Kyunghoon Jeon",
"Youmin Ko",
"Woohwan Jung",
"Hyunjoon Kim"
] | While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge. Existing methods fail to capture disease severity, treatment responses, and nuanced clinical pr... | # | KDD2026 |
ProgNet: Program-Grounded Evidence Composition for Interpretable Graph Classification | https://doi.org/10.1145/3770855.3817844 | [
"Minseok Jeon",
"Seunghyun Park",
"Jun-Gi Jang"
] | We present ProgNet, a graph learning framework for interpretable graph classification that treats explanatory structures as first-class, reusable components of the prediction mechanism. Departing from existing methods that generate isolated, instance-specific explanations, ProgNet introduces a paradigm where reasoning ... | # | KDD2026 |
Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity | https://doi.org/10.1145/3770855.3818130 | [
"James Jewitt",
"Gopi Krishnan Rajbahadur",
"Hao Li",
"Bram Adams",
"Ahmed E. Hassan"
] | Permissive licenses like MIT, Apache-2.0, and BSD-3-Clause dominate open-source AI, signaling that artifacts like models, datasets, and code can be freely used, modified, and redistributed. However, these licenses carry mandatory requirements: include the full license text, provide a copyright notice, and preserve upst... | # | KDD2026 |
CoRGI: GNNs with Convolutional Residual Global Interactions for Lagrangian Simulation | https://doi.org/10.1145/3770855.3818147 | [
"Ethan Ji",
"Yuanzhou Chen",
"Arush Ramteke",
"Fang Sun",
"Tianrun Yu",
"Jai Parera",
"Peipei Ping",
"Wei Wang",
"Yizhou Sun"
] | Partial differential equations (PDEs) govern dynamical systems in hydrodynamics, where classical solvers face well-known difficulties with nonlinearity and computational cost. Lagrangian neural surrogates such as GNS and SEGNN learn directly from particle-based simulations, but local message passing alone is limited in... | # | KDD2026 |
Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation | https://doi.org/10.1145/3770855.3817945 | [
"Huwei Ji",
"Jiajie Su",
"Yuyuan Li",
"Xiaohua Feng",
"Chaochao Chen"
] | LLM-based Cross-Domain Sequential Recommendation (CDSR) leverages LLMs to enhance target performance via deep semantic reasoning, alleviating the dependency on overlapping users. Among LLM-based paradigms, model merging is particularly promising for multi-domain scenarios due to its superior scalability and flexibility... | # | KDD2026 |
Joint Global-Local Representations via Relation-Entity Pair Encoding for Hyper-Relational Knowledge Graphs | https://doi.org/10.1145/3770855.3818000 | [
"Sangjun Ji",
"Sangjune Kim",
"Youngho Lee",
"Bonyou Koo",
"Xiongnan Jin",
"Byungkook Oh"
] | Hyper-relational knowledge graphs (HKGs) extend knowledge graphs with qualifiers to represent complex n-ary facts. However, existing methods often (1) rely on relation-agnostic node aggregation that mixes heterogeneous multi-hop evidence, which can lead to entity-role ambiguity and representation collapse, and (2) fail... | https://github.com/Approxy02/GLoRE | KDD2026 |
SpotAgent: Grounding Visual Geo-localization in Large Vision-Language Models through Agentic Reasoning | https://doi.org/10.1145/3770855.3817948 | [
"Furong Jia",
"Ling Dai",
"Wenjin Deng",
"Fan Zhang",
"Chen Hu",
"Daxin Jiang",
"Yu Liu"
] | Large Vision-Language Models (LVLMs) have demonstrated strong reasoning capabilities in geo-localization, yet they often struggle in real-world scenarios where visual cues are sparse, long-tailed, and highly ambiguous. Previous approaches, bound by internal knowledge, often fail to provide verifiable results, yielding ... | # | KDD2026 |
Experimentation for Different Scheduling Policies on Queues: Mixed Differences-in-Q Estimators Based on Little's Law | https://doi.org/10.1145/3770855.3817634 | [
"Nanshan Jia",
"Ramesh Johari",
"Nian Si",
"Zeyu Zheng"
] | In data centers, tasks are dispatched to various servers to evenly distribute the workload. When a data center considers implementing a new scheduling algorithm, it typically conducts an A/B test prior to deployment to assess the real-world impact of this new method. However, a straightforward A/B test might be interfe... | # | KDD2026 |
On the Role of Anticausal Direction in LLM-based Data Synthesis | https://doi.org/10.1145/3770855.3817910 | [
"Bohan Jiang",
"Pingchuan Ma",
"Zhen Tan",
"Zhuoyu Shi",
"Fred Morstatter",
"Adrienne Raglin",
"Huan Liu"
] | Large Language Models (LLMs) are increasingly used to generate synthetic data. Most LLM-based data synthesis workflows are anticausal : the user injects Y into the prompt to enforce targeted generation of X (Y\rightarrow X). This anticausal direction seems contradictory to the natural direction of data synthesis in mac... | # | KDD2026 |
Spend Search Where It Pays: Value-Guided Structured Sampling and Optimization for Generative Recommendation | https://doi.org/10.1145/3770855.3817873 | [
"Jie Jiang",
"Yangru Huang",
"Zeyu Wang",
"Changping Wang",
"Yuling Xiong",
"Jun Zhang",
"Huan Yu"
] | Generative recommendation via autoregressive models has unified retrieval and ranking into a single conditional generation framework. However, fine-tuning these models with Reinforcement Learning (RL) often suffers from a fundamental probability-reward mismatch. Conventional likelihood-dominated decoding (e.g., beam se... | # | KDD2026 |
G²PRO: Gradient-guided Graph Prompt Optimization for LLM-based POI Recommendation | https://doi.org/10.1145/3770855.3818010 | [
"Nan Jiang",
"Haitao Yuan",
"Tianjun Wei",
"Yingpeng Du",
"Jianing Si",
"Minxiao Chen",
"Jie Zhang",
"Zhu Sun"
] | Large Language Models (LLMs) have shown strong potential for sequential reasoning, creating new opportunities for next Point-of-Interest (POI) recommendation. However, applying LLMs to POI prediction remains challenging due to the modality gap between textual semantics and continuous spatio-temporal signals. Existing r... | # | KDD2026 |
RIDGECUT: Learning Graph Partitioning with Rings and Wedges | https://doi.org/10.1145/3770855.3818103 | [
"Qize Jiang",
"Angelo Zangari",
"Linsey Pang",
"Alice Gatti",
"Mahima Aggarwal",
"Giovanna Vantini",
"Xiaosong Ma",
"Weiwei Sun",
"Sourav Medya",
"Sanjay Chawla"
] | Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorporating domain knowledge into RL frameworks for graph partitioning remains challenging, as existing approaches typically rely on unconstrain... | # | KDD2026 |
Advancing Multimodal Agent Reasoning with Long-Term Neuro-Symbolic Memory | https://doi.org/10.1145/3770855.3817643 | [
"Rongjie Jiang",
"Jianwei Wang",
"Gengda Zhao",
"Chengyang Luo",
"Kai Wang",
"Wenjie Zhang"
] | Recent advances in large language models have driven the emergence of intelligent agents operating in open-world, multimodal environments. To support long-term reasoning, such agents are typically equipped with external memory systems. However, most existing multimodal agent memories rely primarily on neural representa... | # | KDD2026 |
CoFE: Collaborative Feature Engineering via Semantically-Guided Exploration and Diagnostic-Driven Refinement | https://doi.org/10.1145/3770855.3817923 | [
"Weihao Jiang",
"Ziang Nan",
"Zhihui Shi",
"Ya Cong",
"Jun Xiao",
"Xiaoye Miao"
] | In domains such as finance, healthcare, and industry, feature engineering remains the key bottleneck limiting the performance of machine learning models on tabular data. While Automated Feature Engineering (AutoFE) aims to reduce this manual effort, existing approaches still suffer from distinct limitations: data-drive... | # | KDD2026 |
Beyond Language Processing: LLMs Rules-Injected Instruction Tuning for Traffic Prediction | https://doi.org/10.1145/3770855.3817854 | [
"Weihao Jiang",
"Huizhao Wang",
"Zhihui Hu",
"Wenyu Hu",
"Yao Fu",
"Jiang Zhu",
"Jun Xiao"
] | Large Language Models (LLMs) demonstrate strong capabilities in contextual integration and multi-step reasoning, which endow them with the potential to model heterogeneous traffic data. However, the knowledge acquired during LLM pre-training is primarily qualitative and broad, and does not provide the fine-grained, con... | # | KDD2026 |
Towards Robust Travel Time Estimation: An Out-of-Distribution Generalization Approach | https://doi.org/10.1145/3770855.3817802 | [
"Xiwen Jiang",
"Chuan Zhou",
"Xiaofeng Meng",
"Haoxuan Li"
] | Travel is becoming increasingly convenient with the development of the Internet. Travel Time Estimation (TTE) serves as a fundamental task for online traffic services. However, it faces a pressing challenge due to the volatile nature of traffic: the out-of-distribution (OOD) problem. In this paper, we investigate the O... | # | KDD2026 |
Offline Long-Term Causal Effect Estimation with Short-Term Experimental Data for Recommendation Systems | https://doi.org/10.1145/3770855.3817735 | [
"Dian Jin",
"Baohong Li",
"Yi He",
"Yingrong Wang",
"Qiu Rui",
"Dagui Chen",
"Keting Yin",
"Han Zhu",
"Kun Kuang"
] | In recommendation systems, evaluating a recommendation policy typically involves causal inference through either online A/B testing or offline estimation of historical data. Time and cost constraints lead to policy evaluation based on short-term rather than long-term metrics - yet long-term metrics like lifetime-value ... | # | KDD2026 |
Adaptive Exploration for Latent-State Bandits | https://doi.org/10.1145/3770855.3817882 | [
"Jikai Jin",
"Kenneth Hung",
"Sanath Kumar Krishnamurthy",
"Baoyi Shi",
"Congshan Zhang"
] | We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions. The optimal arm can change even though the learner observes only past actions and rewards. We propose algorithms that feed LinUCB with two summaries of the hidden state: a lagged action-reward pair a... | # | KDD2026 |
Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation | https://doi.org/10.1145/3770855.3817750 | [
"Yifan Jin",
"Qirui Ji",
"Bin Qin",
"Jiangmeng Li",
"Lixiang Liu",
"Fuchun Sun",
"Changwen Zheng"
] | Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference. However, the parameterized knowledge encoded within these models is insufficient to cope with distribution shifts, limiting their generalization ability. To m... | # | KDD2026 |
RekindleSketch: Time-Aware Detection of Recent Persistent Flows via Arrival-Driven Rewards | https://doi.org/10.1145/3770855.3817767 | [
"Xuyang Jing",
"Yingchao Dou",
"Jialin Dong",
"Zheng Yan",
"Yihan Zheng",
"Xiangyu Wang",
"Cong Wang",
"Yang Xiao"
] | Recent persistent flow, which refers to a flow that remains continuously active within the most recent R windows, is an important analytical object for recognizing the network situation and detecting anomalies. Existing methods struggle to effectively detect such flows due to prohibitive memory overhead or unstable acc... | # | KDD2026 |
VisionDES: Robust and Explainable Dynamic Vision Ensemble | https://doi.org/10.1145/3770855.3818121 | [
"Firuz Juraev",
"Mohammed Abuhamad",
"Shaker H. Ali El-Sappagh",
"Simon S. Woo",
"Tamer Abuhmed"
] | Dynamic Ensemble Selection (DES) is an adaptive ensemble learning paradigm that selects a subset of base classifiers specific to each test input, enabling more flexible predictions than static ensemble methods. Although successful in tabular settings, DES remains largely unexplored in robust vision applications. We int... | # | KDD2026 |
Multi-TAP: Multi-criteria Target Adaptive Persona Modeling for Cross-domain Recommendation | https://doi.org/10.1145/3770855.3818138 | [
"Daehee Kang",
"Yeon-Chang Lee"
] | Cross-domain recommendation (CDR) aims to alleviate data sparsity by transferring knowledge across domains, yet existing methods primarily rely on coarse-grained behavioral signals and often overlook intra-domain heterogeneity in user preferences. We propose øurs, a multi-criteria target-adaptive persona framework that... | https://github.com/archivehee/Multi-TAP | KDD2026 |
HyperGC: Learning Hypergraph Representations via Full Hyperedge Reconstruction and Contrastive Evaluation | https://doi.org/10.1145/3770855.3817691 | [
"David Yoon Suk Kang",
"So-Bin Jung",
"Sang-Wook Kim"
] | Hypergraph representation learning (HRL) is essential for modeling complex groupwise relationships in real-world data. However, existing generative self-supervised HRL methods suffer from two key limitations: (C1) partial hyperedge reconstruction fails to capture high-order formation semantics, and (C2) negative-sampli... | # | KDD2026 |
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching | https://doi.org/10.1145/3770855.3817963 | [
"Inwon Kang",
"Kavitha Srinivas",
"Nandana Mihindukulasooriya",
"Sola Shirai",
"Parikshit Ram",
"Horst Samulowitz",
"Oshani Seneviratne"
] | Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic semantics, they typically process tabular data as serialized text sequences of standalone column descriptions. This serialization discards criti... | # | KDD2026 |
One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets | https://doi.org/10.1145/3770855.3818142 | [
"Woosung Kang",
"Jiwon Jeong",
"Jonghyeok Shin",
"Jeongwhan Choi",
"Noseong Park"
] | Existing sequential recommendation models rely on dataset-specific training, where the learned parameters are fitted to the item catalog and the observed interaction distribution of the training data. This limits generalization to new domains, typically requiring retraining from scratch. In this work, we propose SRPFN,... | # | KDD2026 |
PYTHIA: Universal Question Answering over Knowledge Graphs | https://doi.org/10.1145/3770855.3817968 | [
"Sergios-Anestis Kefalidis",
"Konstantinos Plas",
"Manolis Koubarakis"
] | Knowledge graph question answering (KGQA) focuses on answering questions with data retrieved from knowledge graphs (KGs). Although Large Language Models (LLMs) have enabled the development of innovative KGQA systems, serious challenges remain. Approaches that utilize supervised finetuning are accurate and responsive, b... | # | KDD2026 |
State Machine Guided Multi-Relational Synthetic Data from Logs for Anomaly Detection | https://doi.org/10.1145/3770855.3818134 | [
"Aja Khanal",
"Apurva Narayan"
] | Software systems generate massive unstructured logs that record execution behavior, failures, and interactions across components, yet existing log anomaly detection methods treat these logs primarily as flat sequences of templates, overlooking the relational execution structure that governs how events co-occur and evol... | # | KDD2026 |
PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories | https://doi.org/10.1145/3770855.3818152 | [
"Hyunsung Kim",
"Kunhee Lee",
"Sangwoo Seo",
"Sang-Ki Ko",
"Jinsung Yoon",
"Chanyoung Park"
] | Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation. Although prior work has explored automatic event detection using player and ball trajectories, ball tracking also remains difficult to scale due to high infrastructural and operational costs. As a r... | https://github.com/hyunsungkim-ds/pathcrf.git | KDD2026 |
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