OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Black-Box Adversarial Attacks on Discrete Sequential Data via Bilevel Bayesian Optimization in Hybrid Spaces
Authors: Tianxing Man, Xingchen Li, Zhaogeng Liu, Haozhen Zhang, Bin Gu, Yi Chang
Venue: KDD
Year: 2025

Black-box attacks have emerged as a significant threat to deep neural networks. This challenge is particularly difficult in discrete sequential data compared to continuous data. Recently, the Blockwise Bayesian Attack (BBA) leveraging discrete Bayesian optimization with an adapted RBF kernel has gained prominence as a cutting-edge solution. However, it relies solely on alignment information (i.e., positional differences) within the RBF kernel, which may not fully capture the information (such as statistical, structural, and semantic information) inherent in discrete sequential data and potentially lacks the desired inductive bias necessary to approximate the target function accurately. To overcome this limitation, this paper proposes a novel bilevel Bayesian optimization approach to adaptively learn a hybrid space that better captures the similarity between discrete sequences. Specifically, we introduce a multi-kernel mechanism that incorporates multiple types of information, creating a more comprehensive similarity measure. Moreover, we develop a bilevel Bayesian optimization algorithm, where the outer-level objective determines the optimal weights of the multiple kernels, while the inner-level objective identifies the optimal adversarial sequence. Extensive experiments conducted on discrete sequential data demonstrate that our approach ensures secure multi-kernel selection and achieves a higher attack success rate with only a few additional queries, compared to BBA and other traditional optimization strategies.
IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping
Authors: Nibir Chandra Mandal, Oishee Bintey Hoque, Abhijin Adiga, Samarth Swarup, Mandy L. Wilson, Lu Feng, Yangfeng Ji, Miaomiao Zhang, Geoffrey C. Fox, Madhav V. Marathe
Venue: KDD
Year: 2025

We introduce IrrMap, the first large-scale dataset (1.1 million patches) for irrigation method mapping across regions. IrrMap consists of multi-resolution satellite imagery from LandSat and Sentinel, along with key auxiliary data such as crop type, land use, and vegetation indices. The dataset spans 1,668,899 farms and 11,443,492 acres across multiple western U.S. states from 2013 to 2023, providing a rich and diverse foundation for irrigation analysis and ensuring geospatial alignment and quality control. The dataset is ML-ready, with standardized 224×224 GeoTIFF patches, the multiple input modalities, carefully chosen train-test-split data, and accompanying dataloaders for seamless deep learning model training and benchmarking in irrigation mapping. The dataset is also accompanied by a complete pipeline for dataset generation, enabling researchers to extend IrrMap to new regions for irrigation data collection or adapt it with minimal effort for other similar applications in agricultural and geospatial analysis. We also analyze the irrigation method distribution across crop groups, spatial irrigation patterns (using Shannon diversity indices), and irrigated area variations for both LandSat and Sentinel, providing insights into regional and resolution-based differences. To promote further exploration, we openly release IrrMap, along with the derived datasets, benchmark models, and pipeline code, through a GitHub repository: https://github.com/Nibir088/IrrMap and Data repository: https://huggingface.co/Nibir/IrrMap, providing comprehensive documentation and implementation details.
The Surprising Victory of NLP: From Philosophy to Agentic Language Models
Authors: Christopher D. Manning
Venue: KDD
Year: 2025

Language Models have been around for decades but have suddenly taken the world by storm. In a surprising third act for anyone doing Natural Language Processing (NLP) in the 70s, 80s, 90s, or 2000s, in much of the popular media, artificial intelligence is now synonymous with language models. In this talk, I want to take a look backward at where language models came from and why they were so slow to emerge, a look inward to give a few thoughts on meaning, intelligence, and what language models understand and know, and a look forward at some topics of recent research: the possibilities for progress with new versions of Universal Transformers and using Large Language Models (LLMs) to build intelligent language-using agents for the digital world. I will argue that material beyond language is not necessary to having meaning and understanding, but it is very useful in most cases, and that composability, adaptability, and learning are vital to intelligence. Rather than simply continuing to build ever-larger LLMs from huge passive collections of text data, we need to explore developing better neural architectures and agents that can learn through interactions. I will discuss how web agents can learn through interactions and how such an interaction-first learning approach can work very effectively in web environments with relatively small language models.
MicroAdapt: Self-Evolutionary Dynamic Modeling Algorithms for Time-evolving Data Streams
Authors: Yasuko Matsubara, Yasushi Sakurai
Venue: KDD
Year: 2025

Recently, we have been inundated with dynamic, time-evolving activity data streams generated by various individual IoT/sensor devices (e.g., healthcare devices and factory equipment). What would be the ideal mechanism for adaptively summarizing and modeling individual and local activities directly on small IoT devices with limited computing resources? In this paper, we present MicroAdapt, which comprises scalable and effective algorithms for modeling and forecasting time-evolving data streams. Our proposed method has the following advantages: (a) Adaptivity: it continuously monitors the latest time-series patterns in data streams, recognizes any sudden changes, updates model parameters, and enables real-time, long-range forecasting; (b) Any-time processing: it operates on a large collection of data streams, requiring a constant time (i.e., O(1)) for model updates and future predictions, at any point in time; (c) Lightweight computing: it does not require high-performance computing machines equipped with powerful GPUs, such as those used in deep learning, and can run on lightweight computers or even ''edge'' computing devices, such as Raspberry Pi. Intuitively, our method is inspired by the concept of an evolutionary adaptation mechanism of microorganisms, and thus it enables adaptive, any-time, lightweight model estimation and summarization of non-stationary data streams whose time-series patterns are constantly changing over time. We conduct extensive experiments on real datasets and demonstrate that MicroAdapt effectively captures important time-series patterns in the data streams, makes long-range forecasts, and consistently outperforms the existing state-of-the-art methods (e.g., TSMixer) as regards accuracy and execution speed. MicroAdapt improves accuracy by about 60% for MSE and 30% for MAE, while reducing computation time by up to six orders of magnitude (more than 100,000 times). We also applied our analytics to experiments implemented on Raspberry Pi with real IoT data streams, thus demonstrating the practicality and effectiveness of our approach. In fact, our proposed method required less than 1.95GB of memory and consumed less than 1.69W of power.
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction
Authors: João Mattos, Zexi Huang, Mert Kosan, Ambuj K. Singh, Arlei Silva
Venue: KDD
Year: 2025

Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from the set of disconnected pairs. Graph Neural Networks (GNNs) have become the predominant framework for link prediction. GNN-based methods treat link prediction as a binary classification problem and handle the extreme class imbalance---real graphs are very sparse---by sampling (uniformly at random) a balanced number of disconnected pairs not only for training but also for evaluation. However, we show that the reported performance of GNNs for link prediction in the balanced setting does not translate to the more realistic imbalanced setting and that simpler topology-based approaches are often better at handling sparsity. These findings motivate Gelato, a similarity-based link-prediction method that applies (1) graph learning based on node attributes to enhance a topological heuristic, (2) a ranking loss for addressing class imbalance, and (3) a negative sampling scheme that efficiently selects hard training pairs via graph partitioning. Experiments show that Gelato outperforms existing GNN-based alternatives.
MEDS: Building Models and Tools in a Reproducible Health AI Ecosystem
Authors: Matthew B. A. McDermott, Justin Xu, Teya S. Bergamaschi, Hyewon Jeong, Simon A. Lee, Nassim Oufattole, Patrick Rockenschaub, Kamile Stankeviciute, Ethan Steinberg, Jimeng Sun, Robin Van De Water, Michael Wornow, John Wu, Zhenbang Wu
Venue: KDD
Year: 2025

Health AI suffers from a systemic reproducibility crisis that irreparably hinders research in this space across academia and industry. To combat this and empower researchers in the health AI space, we propose a comprehensive interactive tutorial introducing the ''Medical Event Data Standard'' (MEDS) and its growing open-source ecosystem. Working in MEDS allows you to more easily build AI models over public or private longitudinal EHR datasets and to readily benchmark existing, published models against contributions on local datasets and tasks. MEDS simplifies the construction of AI models on longitudinal Electronic Health Record (EHR) datasets and enables straightforward benchmarking against established models. Reflecting its growing adoption, MEDS is utilized at over 15 institutions across 8 countries, features 7+ open-source tools, supports 10+ published models, and provides publicly available Extract-Transform-Load (ETL) pipelines for major public EHR datasets. A KDD tutorial offering practical experience with MEDS will significantly enhance reproducibility and comparability in health AI research. In this tutorial, we will teach attendees how to (1) transform datasets into the MEDS format(2) pre-process MEDS data for modeling needs(3) build highly effective, efficient, AI models for diverse predictive tasks on their datasets, and (4) contribute their results to MEDS-DEV, a decentralized benchmark enabling robust evaluation against meaningful baselines. Participants will engage in collaborative, minimal-dependency Jupyter notebook exercises, guided through each step by structured instruction and practical coding sessions. Attendees will leave equipped with practical knowledge to build reproducible, state-of-the-art AI models within the MEDS ecosystem.
GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems
Authors: Tiehua Mei, Hengrui Chen, Peng Yu, Jiaqing Liang, Deqing Yang
Venue: KDD
Year: 2025

Although large language models (LLMs) have shown great potential in recommender systems, the prohibitive computational costs for fine-tuning LLMs on entire datasets hinder their successful deployment in real-world scenarios. To develop affordable and effective LLM-based recommender systems, we focus on the task of coreset selection which identifies a small subset of fine-tuning data to optimize the test loss, thereby facilitating efficient LLMs' fine-tuning. Although there exist some intuitive solutions of subset selection, including distribution-based and importance-based approaches, they often lead to suboptimal performance due to the misalignment with downstream fine-tuning objectives or weak generalization ability caused by individual-level sample selection. To overcome these challenges, we propose GORACS, which is a novel Group-level Optimal tRAnsport-guided Coreset Selection framework for LLM-based recommender systems. GORACS is designed based on two key principles for coreset selection: 1) selecting the subsets that minimize the test loss to align with fine-tuning objectives, and 2) enhancing model generalization through group-level data selection. Corresponding to these two principles, GORACS has two key components: 1) a Proxy Optimization Objective (POO) leveraging optimal transport and gradient information to bound the intractable test loss, thus reducing computational costs by avoiding repeated LLM retraining, and 2) a two-stage Initialization-Then-Refinement Algorithm (ITRA) for efficient group-level selection. Our extensive experiments across diverse recommendation datasets and tasks validate that GORACS significantly reduces fine-tuning costs of LLMs while achieving superior performance over the state-of-the-art baselines and full data training.
Charting Pathways to AI-Enabled Healthcare
Authors: Nina Mishra
Venue: KDD
Year: 2025

AI shows promising potential to improve patient health outcomes, and the accelerating pace of technological advancement suggests health AI may be approaching a transformative threshold. Despite this promise, widespread implementation remains elusive. What barriers persist, and how might we chart a viable path forward? This talk examines two critical pathways to realizing AI's potential in healthcare: how can we enable large-scale access to clinical data and how can we evolve AI to earn the trust of medical professionals? The first challenge - lack of access to clinical data - is well-recognized but persistent. Despite recent increases in publicly available healthcare data, we still lack the volume and diversity needed to ensure accuracy across demographic groups and medical conditions. In other AI domains such as text generation, the breakthrough to reliable performance came through massive-scale training datasets. Healthcare requires a similar scale, yet patient privacy rightfully restricts data access. This presentation will explore methods for generating synthetic patient records that maintain privacy while providing the necessary training scale. The second pathway involves evolving AI to build trust within the clinical community. Medical AI must offer transparency in its decision-making processes. For instance, when answering whether ''Alice takes blood thinners'', an AI system must provide supporting evidence rather than a simple yes/no response. Two approaches will be presented that address this need: fact-verification systems for clinical claims in structured data and semantic highlighting for unstructured text. For predictive scenarios such as ''Will Alice need ventilator support in the next 48 hours?'', I will demonstrate how predictions coupled with counterfactual explanations enhance clinical trust. In addition, AI can earn provider trust by solving problems clinicians lack time to address, such as translating radiology reports into patient-friendly explanations. I will showcase methodologies that effectively bridge this communication gap. Clearing these pathways is essential to equip healthcare providers with AI tools that enable more accurate, efficient decision-making. Despite the challenges, there are compelling reasons for optimism that these barriers can be overcome, bringing us closer to truly AI-enabled healthcare.
Evaluation and Benchmarking of LLM Agents: A Survey
Authors: Mahmoud Mohammadi, Yipeng Li, Jane Lo, Wendy Yip
Venue: KDD
Year: 2025

The rise of LLM-based agents has opened new frontiers in AI applications, yet evaluating these agents remains a complex and underdeveloped area. This survey provides an in-depth overview of the emerging field of LLM agent evaluation, introducing a two-dimensional taxonomy that organizes existing work along (1) evaluation objectives-what to evaluate, such as agent behavior, capabilities, reliability, and safety-and (2) evaluation process-how to evaluate, including interaction modes, datasets and benchmarks, metric computation methods, and tooling. In addition to taxonomy, we highlight enterprise-specific challenges, such as role-based access to data, the need for reliability guarantees, dynamic and long-horizon interactions, and compliance, which are often overlooked in current research. We also identify the future research directions, including holistic, more realistic, and scalable evaluation. This work aims to bring clarity to the fragmented landscape of agent evaluation and provide a framework for systematic assessment, enabling researchers and practitioners to evaluate LLM agents for real-world deployment.
Roadside Multi-LiDAR Data Fusion for Enhanced Traffic Safety
Authors: Md. Parvez Mollah, Biplob Debnath, Murugan Sankaradas, Srimat Chakradhar, Abdullah Mueen
Venue: KDD
Year: 2025

Roadside LiDAR (Light Detection and Ranging) sensors promise safer and faster traffic management and vehicular operations. However, occlusion and small view angles are significant challenges to widespread use of roadside LiDARs. We consider fusing data from multiple LiDARs at a traffic intersection to better estimate traffic parameters than one can estimate from a single LiDAR. The key challenge is to calibrate multiple LiDARs both in time and space. The problem is more complex when heterogeneous sensors differ in resolution and are positioned arbitrarily on a traffic intersection. We propose a calibration technique to fuse multiple LiDARs. We show that our technique works on various data granularity and enables real-time analytics for roadside traffic monitoring. We evaluate on a large number of simulated traffic scenarios and show that fusion improves accuracy of vehicle counting and near-collision detection. We apply our algorithm on real traffic data and demonstrate utility in classifying vehicles and detecting occluded traffic participants.
Compressive Meta-Learning
Authors: Daniel Mas Montserrat, David Bonet, Maria Perera, Xavier Giró-i-Nieto, Alexander G. Ioannidis
Venue: KDD
Year: 2025

The rapid expansion in the size of new datasets has created a need for fast and efficient parameter-learning techniques. Compressive learning is a framework that enables efficient processing by using random, nonlinear features to project large-scale databases onto compact, information-preserving representations whose dimensionality is independent of the number of samples and can be easily stored, transferred, and processed. These database-level summaries are then used to decode parameters of interest from the underlying data distribution without requiring access to the original samples, offering an efficient and privacy-friendly learning framework. However, both the encoding and decoding techniques are typically randomized and data-independent, failing to exploit the underlying structure of the data. In this work, we propose a framework that meta-learns both the encoding and decoding stages of compressive learning methods by using neural networks that provide faster and more accurate systems than the current state-of-the-art approaches. To demonstrate the potential of the presented Compressive Meta-Learning framework, we explore multiple applications--including neural network-based compressive PCA, compressive ridge regression, compressive k-means, and autoencoders.
Optimising Budget Management via Primal-Dual Approximation with Constrained Polynomial Weights Update
Authors: Dmitrii Moor, Per Berglund, Hannes Karlbom, Zhenwen Dai, Kyle Kretschman, Mounia Lalmas
Venue: KDD
Year: 2025

Budget management is an essential capability in many online applications, including running advertising campaigns in sponsored search and allocating promotional content in recommender systems (RS). Most existing approaches to optimising budget spendings rely on improving worst-case approximation guarantees in the respective online knapsack packing problem or leveraging online learning techniques to improve an average system performance. However, worst-case approaches often underperform in practice, as extreme scenarios are uncommon, while online learning methods may lack robustness in highly non-stationary environments. In our work, we bridge these two approaches by developing an online budget pacing algorithm that preserves worst-case guarantees while improving the average allocative efficiency of budget management systems. Specifically, we frame the optimal budget allocation problem within an online learning perspective, where strategies chosen by the online learner are constrained by a primal-dual approximation algorithm defining the environment. We identify a feedback loop between the online learner's strategy and environment's response and propose a method to break this loop, optimising average allocative efficiency without compromising worst-case guarantees. To evaluate our approach, we conduct extensive offline experiments using both synthetic and real-world data, covering 320M promotional impressions across 20 different markets of a major industrial RS. Our results demonstrate that our method significantly enhances total allocated value and ROI across all markets.
TGDataset: Collecting and Exploring the Largest Telegram Channels Dataset
Authors: Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini
Venue: KDD
Year: 2025

Telegram is a widely adopted instant messaging platform. It has become worldwide popular because of its emphasis on privacy and its social network features such as channels-virtual rooms in which only the admins can post and broadcast messages to all the subscribers. Channels are used to deliver live updates (e.g., weather alerts) and content to a large audience (e.g., COVID-19 announcements) but unfortunately also to disseminate radical ideologies and coordinate attacks such as the Capitol Hill riot. This paper introduces the TGDataset, the most extensive publicly available collection of Telegram channels, comprising 120,979 channels and over 400 million messages. We outline the data collection process and provide a comprehensive overview of the data set. Using language detection, we identify the predominant languages within the dataset. We then focus on English channels, employing topic modeling to analyze the subjects they cover. Finally, we discuss some use cases in which our dataset can be instrumental in understanding the Telegram ecosystem and studying the diffusion of questionable news. Alongside the raw dataset, we release the scripts used in our analysis, as well as a list of channels associated with a novel conspiracy theory known as Sabmyk.