OpenRaiser/Intern-Atlas huggingface dataset:
Wedjat: Detecting Sophisticated Evasion Attacks via Real-time Causal Analysis
Authors: Li Gao, Chuanpu Fu, Xinhao Deng, Ke Xu, Qi Li
Venue: KDD
Year: 2025

Traffic encryption has been widely adopted to protect the confidentiality and integrity of Internet traffic. However, attackers can also abuse such mechanism to deliver malicious traffic. Particularly, existing methods detecting encrypted malicious traffic are not robust against evasion attacks that manipulate traffic to obfuscate traffic features. Robust detection against evasion attacks remains an open problem. To the end, we develop Wedjat, which utilizes a causal network to model benign packet interactions among relevant flows, such that it recognizes abnormal causality that represents malicious traffic and disrupted causality incurred by evasion attacks. We extensively evaluate Wedjat with millions of flows collected from a real-world enterprise. The experimental results demonstrate that Wedjat achieves an accuracy of 0.957 F1-score when detecting various advanced attacks. Notably, five sophisticated evasion attacks, which have successfully evaded all existing methods, are accurately detected by Wedjat with over 0.915 F1. It demonstrates that Wedjat achieves exceptional robustness against evasions. Meanwhile, Wed- jat maintains an outstanding detection latency, i.e., it can predict each packet in less than 0.125 seconds.
SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation
Authors: Hongfan Gao, Wangmeng Shen, Xiangfei Qiu, Ronghui Xu, Bin Yang, Jilin Hu
Venue: KDD
Year: 2025

Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability for uncertainty estimation and denoising diffusion probabilistic models (DDPMs) have achieved great success in probabilistic time series imputation tasks with its power to model complex distributions. However, current DDPM-based probabilistic time series imputation methodologies are confronted with two types of challenges: 1) The backbone modules of the denoising parts are not capable of achieving sequence modeling with low time complexity. 2) The architecture of denoising modules can not handle the dependencies in the time series data effectively. To address the first challenge, we explore the potential of state space model, namely Mamba, as the backbone denoising module for DDPMs. To tackle the second challenge, we carefully devise several SSM-based blocks for time series data modeling. Experimental results demonstrate that our approach can achieve state-of-the-art time series imputation results on multiple real-world datasets. Our datasets and code are available at https://github.com/decisionintelligence/SSD-TS/
Multi-Task Combinatorial Bandits for Budget Allocation
Authors: Lin Ge, Yang Xu, Jianing Chu, David Cramer, Fuhong Li, Kelly Paulson, Rui Song
Venue: KDD
Year: 2025

Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year. A crucial challenge for marketing managers is determining the optimal allocation of limited budgets across various ad lines in each campaign to maximize cumulative returns, especially given the huge uncertainty in return outcomes. In this paper, we propose to formulate budget allocation as a multi-task combinatorial bandit problem and introduce a novel online budget allocation system. The proposed system: i) integrates a Bayesian hierarchical model to intelligently utilize the metadata of campaigns and ad lines and budget size, ensuring efficient information sharing; ii) provides the flexibility to incorporate diverse modeling techniques such as Linear Regression, Gaussian Processes, and Neural Networks, catering to diverse environmental complexities; and iii) employs the Thompson sampling technique to strike a balance between exploration and exploitation. Through extensive empirical evaluations with both synthetic data and real-world data from Amazon campaigns, our system demonstrates robustness and adaptability, effectively maximizing the overall cumulative returns. A Python implementation of the proposed procedure is available at https://anonymous.4open.science/r/MCMAB.
Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval
Authors: Abdellah Ghassel, Ian Robinson, Gabriel Tanase, Hal Cooper, Bryan Thompson, Zhen Han, Vassilis N. Ioannidis, Soji Adeshina, Huzefa Rangwala
Venue: KDD
Year: 2025

Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, yet it still falters when answers must be pieced together across semantically distant documents. We close this gap with the Hierarchical Lexical Graph (HLG), a three-tier index that (i) traces every atomic proposition to its source(ii) clusters propositions into latent topics, and (iii) links entities and relations to expose cross-document paths. On top of HLG we build two complementary, plug-and-play retrievers: StatementGraphRAG, which performs fine-grained entity-aware beam search over propositions for high-precision factoid questions, and TopicGraphRAG, which selects coarse topics before expanding along entity links to supply broad yet relevant context for exploratory queries. Additionally, existing benchmarks lack the complexity required to rigorously evaluate multi-hop summarization systems, often focusing on single-document queries or limited datasets. To address this, we introduce a synthetic dataset generation pipeline that curates realistic, multi-document question-answer pairs, enabling robust evaluation of multi-hop retrieval systems. Extensive experiments across five datasets demonstrate that our methods outperform naive chunk-based RAG, achieving an average relative improvement of 23.1% in retrieval recall and correctness. Open-source Python library is available at https://github.com/awslabs/graphrag-toolkit.
TEMPER: Capturing Consistent and Fluctuating TEMPoral User Behaviour for EtheReum Phishing Scam Detection
Authors: Medhasree Ghosh, Chirag Dinesh Jain, Raju Halder, Joydeep Chandra
Venue: KDD
Year: 2025

Phishing scams on the Ethereum network have become a serious threat, especially with the influx of new users into the cryptocurrency market. Current detection methods are mainly focused on long-term consistent transaction patterns with smooth temporal dynamics. However, these methods often struggle to differentiate between phishing and non-phishing users, whose behaviours may appear deceptively similar. Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. TEMPER effectively generates distinguishable user embeddings, enabling the accurate identification of phishing users. Unlike previous approaches, TEMPER mitigates data leakage through a novel sequential transaction sampling algorithm and addresses network sparsity with short-term temporal learning. Through extensive experimentation on three real-world Ethereum datasets, TEMPER demonstrates its efficacy by achieving a 3-4% improvement in the F1-Score compared to existing baseline models, representing a significant advancement in Ethereum phishing user detection.
Detecting Interpretable Subgroup Drifts
Authors: Flavio Giobergia, Eliana Pastor, Luca de Alfaro, Elena Baralis
Venue: KDD
Year: 2025

The ability to detect and adapt to changes in data distributions is crucial to maintain the accuracy and reliability of machine learning models. Detection is generally approached by observing the drift of model performance from a global point of view. However, drifts occurring in (fine-grained) data subgroups may go unnoticed when monitoring global drift.We take a different perspective, and introduce methods for observing drift at the finer granularity of subgroups. Relevant data subgroups are identified during training and monitored efficiently throughout the model's life. Performance drifts in any subgroup are detected, quantified and characterized so as to provide an interpretable summary of the model behavior over time. Experimental results confirm that our subgroup-level drift analysis identifies drifts that do not show at the (coarser) global dataset level. The proposed approach provides a valuable tool for monitoring model performance in dynamic real-world applications, offering insights into the evolving nature of data and ultimately contributing to more robust and adaptive models.
Improving Learning Efficacy on Duolingo via Generative AI and the Learner Feedback Loop
Authors: Natalie S. Glance
Venue: KDD
Year: 2025

One of the most challenging aspects of learning a foreign language is learning to converse in that language. While large language models (LLMs) have made it feasible to build generative AI chatbots that simulate conversation, creating an AI-powered language tutor that is both pedagogically effective and engaging for learners requires solving a host of additional problems. At Duolingo, we have developed an AI conversational tutor embedded in our platform, featuring a character named Lily, who helps users practice real-world conversations in their target language. In building this system, we tackled multiple challenges: adapting dialogue to each learner's proficiency level, sustaining personalized and coherent interactions across sessions, maintaining consistent character-driven personality, and designing a structure that supports both guided and learner-initiated topics. Our solution integrates a three-party conversational architecture, a persistent memory mechanism to retain prior interactions, and real-time conversation evaluation to dynamically adjust to the learner's input. This talk will highlight how generative AI, when coupled with rigorous feedback loops and thoughtful design, can significantly enhance the language learning experience.
Benchmarking Fraud Detectors on Private Graph Data
Authors: Alexander Goldberg, Giulia Fanti, Nihar B. Shah, Steven Wu
Venue: KDD
Year: 2025

We introduce the novel problem of benchmarking fraud detectors on private graph-structured data. Currently, many types of fraud are managed in part by automated detection algorithms that operate over graphs. We consider the scenario where a data holder wishes to outsource development of fraud detectors to third parties (e.g., vendors or researchers). The third parties submit their fraud detectors to the data holder, who evaluates these algorithms on a private dataset and then publicly communicates the results. We propose a realistic privacy attack on this system that allows an adversary to de-anonymize individuals' data based only on the evaluation results. In simulations of a privacy-sensitive benchmark for facial recognition algorithms by the National Institute of Standards and Technology (NIST), our attack achieves near perfect accuracy in identifying whether individuals' data is present in a private dataset, with a True Positive Rate of 0.98 at a False Positive Rate of 0.00. We then study how to benchmark algorithms while satisfying a formal differential privacy (DP) guarantee. We empirically evaluate two classes of solutions: subsample-and-aggregate and DP synthetic graph data. We demonstrate through extensive experiments that current approaches fail to provide utility when guaranteeing DP. Our results indicate that the error arising from DP trades off between bias from distorting graph structure and variance from adding random noise. Current methods lie on different points along this bias-variance trade-off, but more complex methods tend to require high-variance noise addition, undermining utility.
Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation
Authors: Peiliang Gong, Yucheng Wang, Min Wu, Zhenghua Chen, Xiaoli Li, Daoqiang Zhang
Venue: KDD
Year: 2025

Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained model from an annotated source domain to an unlabelled target domain without accessing the source data, thereby preserving data privacy. While existing SFDA methods have proven effective in reducing reliance on source data, they struggle to perform well on multivariate time series (MTS) due to their failure to consider the intrinsic spatial correlations inherent in MTS data. These spatial correlations are crucial for accurately representing MTS data and preserving invariant information across domains. To address this challenge, we propose Temporal Restoration and Spatial Rewiring (TERSE), a novel and concise SFDA method tailored for MTS data. Specifically, TERSE comprises a customized spatial-temporal feature encoder designed to capture the underlying spatial-temporal characteristics, coupled with both temporal restoration and spatial rewiring tasks to reinstate latent representations of the temporally masked time series and the spatially masked correlated structures. During the target adaptation phase, the target encoder is guided to produce spatially and temporally consistent features with the source domain by leveraging the source pre-trained temporal restoration and spatial rewiring networks. Therefore, TERSE can effectively model and transfer spatial-temporal dependencies across domains, facilitating implicit feature alignment. In addition, as the first approach to simultaneously consider spatial-temporal consistency in MTS-SFDA, TERSE can also be integrated as a versatile plug-and-play module into established SFDA methods. Extensive experiments on three real-world time series datasets demonstrate the effectiveness and versatility of our approach. Our code is available at https://github.com/Tokenmw/TERSE-master.
Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
Authors: Peiliang Gong, Mohamed Ragab, Min Wu, Zhenghua Chen, Yongyi Su, Xiaoli Li, Daoqiang Zhang
Venue: KDD
Year: 2025

Test-time adaptation aims to adapt pre-trained deep neural networks using solely online unlabelled test data during inference. Although TTA has shown promise in visual applications, its potential in time series contexts remains largely unexplored. Existing TTA methods, originally designed for visual tasks, may not effectively handle the complex temporal dynamics of real-world time series data, resulting in suboptimal adaptation performance. To address this gap, we propose Augmented Contrastive Clustering with Uncertainty-aware Prototyping (ACCUP), a straightforward yet effective TTA method for time series data. Initially, our approach employs augmentation ensemble on the time series data to capture diverse temporal information and variations, incorporating uncertainty-aware prototypes to distill essential characteristics. Additionally, we introduce an entropy comparison scheme to selectively acquire more confident predictions, enhancing the reliability of pseudo labels. Furthermore, we utilize augmented contrastive clustering to enhance feature discriminability and mitigate error accumulation from noisy pseudo labels, promoting cohesive clustering within the same class while facilitating clear separation between different classes. Extensive experiments conducted on three real-world time series datasets demonstrate the effectiveness and generalization potential of the proposed method, advancing the underexplored realm of TTA for time series data. Our code is available at https://github.com/Tokenmw/ACCUP-main.
Pairwise Sample Complexity for Fair Active Ranking with Cascaded Norm Objectives
Authors: Sruthi Gorantla, Sara Ahmadian
Venue: KDD
Year: 2025

Ranking systems based on pairwise comparisons are fundamental in decision-making applications, yet fairness concerns remain largely unaddressed, particularly in active ranking frameworks. We propose a novel fairness-aware active ranking approach that adaptively queries pairwise preferences to construct rankings that are both probably approximately correct (PAC) and fair. We propose a flexible cascaded norm-based objective that balances error distribution within and across socially salient groups, providing a unified framework to mitigate systemic disparities. Adopting our objective function allows us to explore fundamental fairness concepts like equal or proportionate errors within a unified framework. We develop both group-blind and group-aware algorithms and derive their sample complexity bounds. Empirical evaluations on real-world datasets, including COMPAS and German Credit, demonstrate the efficiency of our approach, reducing sample complexity while achieving fairer rankings. Our findings offer theoretical insights and practical methods to enhance fairness in active ranking systems, improving their reliability in hiring, recommendations, and other applications.
TSMO 2025: Two-sided Marketplace Optimization: Search, Discovery, Matching, Pricing & Growth
Authors: Mihajlo Grbovic, Vladan Radosavljevic, Amit Goyal, Rui Song, Minmin Chen, Zhiwei Qin, Katerina Iliakopoulou-Zanos, Thanasis Noulas, Hongtu Zhu, Fabrizio Silvestri
Venue: KDD
Year: 2025

In recent years, two-sided marketplaces have emerged as viable business models in many real-world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing the supply and demand of a specific good. Examples of industries include but are not limited to accommodation (Airbnb, Booking.com), video content (YouTube, Instagram, TikTok), ridesharing (Uber, Lyft), online shops (Etsy, Ebay, Facebook Marketplace), music (Spotify, Amazon), app stores (Apple App Store, Google App Store) or job sites (LinkedIn). The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content, or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube, Instagram, or Uber, the platforms operate by outsourcing the service they provide to their users, whether they are hosts, content creators or drivers, and have to develop their models considering their needs and goals.
VFLAIR-LLM: A Comprehensive Framework and Benchmark for Split Learning of LLMs
Authors: Zixuan Gu, Qiufeng Fan, Long Sun, Yang Liu, Xiaojun Ye
Venue: KDD
Year: 2025

With the advancement of Large Language Models (LLMs), LLM applications have expanded into a growing number of fields. However, users with data privacy concerns face limitations in directly utilizing LLM APIs, while private deployments incur significant computational demands. This creates a substantial challenge in achieving secure LLM adaptation under constrained local resources. To address this issue, collaborative learning methods, such as Split Learning (SL), offer a resource-efficient and privacy-preserving solution for adapting LLMs to private domains. In this study, we introduce VFLAIR-LLM (available at https://github.com/FLAIR-THU/VFLAIR-LLM), an extensible and lightweight split learning framework for LLMs, enabling privacy-preserving LLM inference and fine-tuning in resource-constrained environments. Our library provides two LLM partition settings, supporting three task types and 18 datasets. In addition, we provide standard modules for implementing and evaluating attacks and defenses. We benchmark 5 attacks and 9 defenses under various Split Learning for LLM(SL-LLM) settings, offering concrete insights and recommendations on the choice of model partition configurations, defense strategies, and relevant hyperparameters for real-world applications.
Adaptive Domain Inference Attack with Concept Hierarchy
Authors: Yuechun Gu, Jiajie He, Keke Chen
Venue: KDD
Year: 2025

With increasingly deployed deep neural networks in sensitive application domains, such as healthcare and security, it's essential to understand what kind of sensitive information can be inferred from these models. Most known model-targeted attacks assume attackers have learned the application domain or training data distribution to ensure successful attacks. Can removing the domain information from model APIs protect models from these attacks? This paper studies this critical problem. Unfortunately, even with minimal knowledge, i.e., accessing the model as an unnamed function without leaking the meaning of input and output, the proposed adaptive domain inference attack (ADI) can still successfully estimate relevant subsets of training data. We show that the extracted relevant data can significantly improve, for instance, the performance of model-inversion attacks. Specifically, the ADI method utilizes the concept hierarchy extracted from the public and private datasets that the attacker can access and applies a novel algorithm to adaptively tune the likelihood of leaf concepts in the hierarchy showing up in the unseen training data. For comparison, we also designed a straightforward hypothesis-testing-based attack -- LDI. The ADI attack not only extracts partial training data at the concept level but also converges fastest and requires the fewest target-model accesses among all candidate methods. Our code is available at https://anonymous.4open.science/r/KDD-362D.
Revisiting Cognition in Neural Cognitive Diagnosis
Authors: Hengnian Gu, Guoqian Luo, Xiaoxiao Dong, Shulin Li, Dongdai Zhou
Venue: KDD
Year: 2025

Cognitive diagnosis is a fundamental task in intelligent education, aiming to measure students' proficiency on knowledge concepts based on practice data. Traditional methods utilize a broadly-defined latent trait θ to represent knowledge proficiency with some cognitive factors like skill or ability. However, existing methods simplify this to a narrowly-defined latent trait θ, which focuses only on knowledge or treats these cognitive factors as implicit features inferred from data. They fail to explicitly model these cognitive factors, resulting in limited performance and interpretability. To this end, we revisit essence of cognition in Educational Psychology Theory and propose a novel Cognition-aware Cognitive Diagnosis (CCD) model, where we first introduce the Cognition factor as a bridge into the long-standing three-basic-factors (Student, Exercise, Knowledge concept) paradigm. CCD has two main parts: cognition representations and a two-stage diagnostic process. In the first part, we explicitly model cognitive process (CP) dimensions from Bloom's Taxonomy of Educational Objectives, leading to two innovative concepts proposed: the student's Subjective Cognitive Ability (SCA) and the exercise's Objective Cognitive Attribute (OCA), derived by regulating the CP through S-K and E-K interactions, respectively. Then, the SCA and OCA are formed into a new cognition-aware latent trait θ. In the second part, we employ a basic interaction function and a slip and guess influence function, inputting our new θ, a continuous Q-matrix (generated by a siamese PLMs), and other features to obtain the ideal result, followed by feeding it into the slip and guess influence function to obtain the actual result. Extensive experiments on real-world datasets demonstrates the superior effectiveness and good interpretability.