OpenRaiser/Intern-Atlas huggingface dataset:
Exploring the Potential of Foundation Models as Reliable AI Contact Centers
Authors: Hoyoon Byun, Minhoi Park, Seolah Kim, EunBi Kim, Kyungwoo Song
Venue: KDD
Year: 2025

The emergence of foundation models with high generalization performance increases expectations for their application in AI Contact Centers (AICC) services. However, research on AICC services based on foundation models remains insufficient, particularly in the context of non-English languages. To bridge this gap, we construct a Korean phone call-based customer service dataset designed to evaluate the feasibility and fairness of foundation model-based AICC systems. Our utterance-level audio dataset incorporates diverse speaker attributes such as accent, gender, and age, ensuring comprehensive assessments across various demographic factors. Furthermore, we provide dialect-aware scripts to enhance linguistic diversity and realism in AICC agent evaluation. Building upon this dataset, we propose a customized prompt engineering approach to optimize foundation models for AICC services. Our system prompt improves the natural flow of conversation while ensuring accurate information generation by LLMs. In addition, we address the limitations of conventional human-based evaluations, which are costly and susceptible to bias, by introducing an automatic hierarchical evaluation metric. This metric leverages LLMs to systematically assess AI agent responses, considering both overall conversation quality and factual accuracy. Finally, using our fine-grained Korean speech dataset, we conduct an experimental fairness evaluation of AI agents in AICC services. We also demonstrate that our proposed evaluation metric exhibits a higher correlation with human judgments compared to previous predominant metrics. Our work contributes to the development of more reliable, fair, and effective AICC services by providing a high-quality dataset and robust evaluation framework. The code and materials are available at https://anonymous.4open.science/r/fomo4aicc-EC45.
HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback
Authors: Xufeng Cai, Ziwei Guan, Lei Yuan, Ali Selman Aydin, Tengyu Xu, Boying Liu, Wenbo Ren, Renkai Xiang, Songyi He, Haichuan Yang, Serena Li, Mingze Gao, Yue Weng, Ji Liu
Venue: KDD
Year: 2025

Modern recommendation systems can be broadly divided into two key stages: the ranking stage, where the system predicts various user engagements (e.g., click-through rate, like rate, follow rate, watch time), and the value model stage, which aggregates these predictive scores through a function (e.g., a linear combination defined by a weight vector) to measure the value of each content by a single numerical score. Both stages play roughly equally important roles in real industrial systems; however, how to optimize the model weights for the second stage still lacks systematic study. This paper focuses on optimizing the second stage through auto-tuning technology. Although general auto-tuning systems and solutions - both from established production practices and open-source solutions - can address this problem, they typically require weeks or even months to identify a feasible solution. Such prolonged tuning processes are unacceptable in production environments for recommendation systems, as suboptimal value models can severely degrade user experience. An effective auto-tuning solution is required to identify a viable model within 2-3 days, rather than the extended timelines typically associated with existing approaches. In this paper, we introduce a practical auto-tuning system named HyperZero that addresses these time constraints while effectively solving the unique challenges inherent in modern recommendation systems. Moreover, this framework has the potential to be expanded to broader tuning tasks within recommendation systems.
How to use Graph Data in the Wild to Help Graph Anomaly Detection?
Authors: Yuxuan Cao, Jiarong Xu, Chen Zhao, Jiaan Wang, Carl Ji Yang, Chunping Wang, Yang Yang
Venue: KDD
Year: 2025

In recent years, graph anomaly detection has gained considerable attention and has found extensive applications in various domains such as social, financial, and communication networks. However, anomalies in graph-structured data present unique challenges, including label scarcity, ill-defined anomalies, and varying anomaly types, making supervised or semi-supervised methods unreliable. Researchers often adopt unsupervised approaches to address these challenges, assuming that anomalies deviate significantly from the normal data distribution. Yet, when the available data is insufficient, capturing the normal distribution accurately and comprehensively becomes difficult. To overcome this limitation, we propose to utilize external graph data (i.e., graph data in the wild) to help anomaly detection tasks. This naturally raises the question: How can we use external data to help graph anomaly detection task? To answer this question, we propose a novel framework Wild-GAD. Our framework is built upon a unified database, UniWildGraph, which comprises a large and diverse collection of graph data with broad domain coverage, ample data volume, and a unified feature space. We further develop selection criteria based on representativity and diversity to identify the most suitable external data for each anomaly detection task. Extensive experiments on six real-world test datasets demonstrate the effectiveness of Wild-GAD. Compared to the baseline methods, our framework has an average 18% AUCROC and 32% AUCPR improvement over the best-competing methods.
Maturity Framework for Enhancing Machine Learning Quality
Authors: Angelantonio Castelli, Georgios Christos Chouliaras, Dmitri Goldenberg
Venue: KDD
Year: 2025

With the rapid integration of Machine Learning (ML) in business applications and processes, it is crucial to ensure the quality, reliability and reproducibility of such systems. We suggest a methodical approach towards ML system quality assessment and introduce a structured Maturity framework for governance of ML. We emphasize the importance of quality in ML and the need for rigorous assessment, driven by issues in ML governance and gaps in existing frameworks. Our primary contribution is a comprehensive open-sourced quality assessment method, validated with empirical evidence, accompanied by a systematic maturity framework tailored to ML systems. Drawing from applied experience at Booking.com, we discuss challenges and lessons learned during large-scale adoption within organizations. The study presents empirical findings, highlighting quality improvement trends and showcasing business outcomes. The maturity framework for ML systems, aims to become a valuable resource to reshape industry standards and enable a structural approach to improve ML maturity in any organization.
Safe Online Bid Optimization with Return on Investment and Budget Constraints
Authors: Matteo Castiglioni, Alessandro Nuara, Giulia Romano, Giorgio Spadaro, Francesco Trovò, Nicola Gatti
Venue: KDD
Year: 2025

In online marketing, the advertisers aim to balance achieving high volumes and high profitability. The companies' business units address this tradeoff by maximizing the volumes while guaranteeing a minimum Return On Investment (ROI) level. Such a task can be naturally modeled as a combinatorial optimization problem subject to ROI and budget constraints that can be solved online. In this picture, the learner's uncertainty over the constraints' parameters plays a crucial role since the algorithms' exploration choices might lead to their violation during the entire learning process. Such violations represent a major obstacle to adopting online techniques in real-world applications. Thus, controlling the algorithms' exploration during learning is paramount to making humans trust online learning tools. This paper studies the nature of both optimization and learning problems. In particular, we show that the learning problem is inapproximable within any factor (unless P = NP) and provide a pseudo-polynomial-time algorithm to solve its discretized version. Subsequently, we prove that no online learning algorithm can violate the (ROI or budget) constraints a sublinear number of times during the learning process while guaranteeing a sublinear regret. We provide the GCB algorithm that guarantees sublinear regret at the cost of a linear number of constraint violations and GCBsafe that guarantees w.h.p.a constant upper bound on the number of constraint violations at the cost of a linear regret. Moreover, we designed GCBsafe(ψ, φ), which guarantees both sublinear regret and safety w.h.p. at the cost of accepting tolerances ψ and φ in the satisfaction of the ROI and budget constraints, respectively. Finally, we provide experimental results to compare the regret and constraint violations of GCB, GCBsafe, and GCBsafe(ψ, φ).
Evaluating and Generating Query Workloads for High Dimensional Vector Similarity Search
Authors: Matteo Ceccarello, Alexandra Levchenko, Ioana Ileana, Themis Palpanas
Venue: KDD
Year: 2025

Similarity search lies at the heart of many modern applications, ranging from databases to deep learning to data series analysis. As such, a vast effort has been invested in developing algorithms, data structures and implementations to speed up this crucial subroutine. To empirically validate these approaches, several benchmarking efforts have been initiated covering a wide array of datasets. In this paper, we observe that usually little control is exercised on the hardness of the workloads with which methods are tested and compared. To address this issue, we first evaluate several query hardness measures with respect to their ability to capture the empirical hardness of a query, i.e. the effort invested by an index data structure to provide an answer. Then, we propose two methods, deemed Hephaestus-Annealing and Hephaestus-Gradient, for synthesizing query workloads so that they meet a user-specified hardness target. Both methods allow to produce workloads with the desired hardness: we find that Hephaestus-Gradient is faster, while Hephaestus-Annealing makes fewer assumptions on the target hardness measure. The resulting workloads can be used to gain insights into the behavior of similarity search algorithms.
UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization
Authors: Haoye Chai, Shiyuan Zhang, Xiaoqian Qi, Baohua Qiu, Yong Li
Venue: KDD
Year: 2025

Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. It involves multiple tasks, including long-term prediction, short-term prediction, and generation tasks that do not rely on historical data. By leveraging the different types of mobile network data generated from these tasks, operators can perform a variety of network optimizations and planning activities, such as base station (BS) deployment, resource allocation, energy optimization, etc. However, existing models are often designed for specific tasks and trained with specialized data, and there is a lack of universal models for traffic forecasting across different urban environments. In this paper, we propose a Universal model for Mobile traffic forecasting (UoMo), aiming to handle diverse forecasting tasks of short/long-term predictions and distribution generation across multiple cities to support network planning and optimization. UoMo combines diffusion models and transformers, where various spatio-temporal masks are proposed to enable UoMo to learn intrinsic features of different tasks, and a contrastive learning strategy is developed to capture the correlations between mobile traffic and urban contexts, thereby improving its transfer learning capability. Extensive evaluations on 9 real-world datasets demonstrate that UoMo outperforms current models in various forecasting tasks and zero/few-shot learning. It shows an average accuracy improvement of 27.85%, 18.57%, and 15.6% in long-term prediction, short-term prediction, and generation tasks, respectively, showcasing its strong forecasting capability. We deploy UoMo on China Mobile's JiuTian platform, leveraging the predicted mobile data to optimize live networks. This optimization includes BS deployment, resulting in a 25.3% increase in served users, and BS sleep control, which reduces equipment depreciation by 40.7%. The source code is available online: https://github.com/tsinghua-fib-lab/UoMo.
GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations
Authors: Junze Chen, Cheng Yang, Shujie Li, Zhiqiang Zhang, Yawen Li, Junping Du, Chuan Shi
Venue: KDD
Year: 2025

Large language models (LLMs) have demonstrated their strong capabilities in various domains, and have been recently integrated for graph analysis as graph language models (GLMs). With LLMs as the predictor, some GLMs can interpret unseen tasks described by natural language, and learn from a few examples in the prompts without parameter tuning, known as in-context learning (ICL). Another subset of GLMs utilizes abundant training labels to enhance model performance, known as instruction tuning. However, we argue that ICL on graphs has effectiveness issues due to fixed parameters and efficiency issues due to long context. Meanwhile, the large amount of labeled data required for instruction tuning can be difficult to obtain in real-world scenarios. To this end, we aim to introduce an extra parameter adaptation stage that can efficiently tailor GLMs to an unseen graph and task with only a few labeled examples, in exchange for better prediction accuracy and faster inference speed. For implementation, in this paper we propose GraphLAMA method, with its model backbone and learning schemes specialized for efficient tuning and inference. Specifically, for the model backbone, we use a graph neural network (GNN) with several well-designed components (e.g., hop encodings, gating modules) to transform nodes into the representation space of LLM tokens. Task instructions can then be represented as a mixture of node and language tokens. In the pre-training stage, all model parameters except for the LLM will be trained with different tasks (i.e., node matching, node classification, and link prediction) to capture general knowledge. In the adaptation stage, only a few pre-trained parameters will be updated based on few-shot examples. Extensive experiments on few/zero-shot node classification and summary generation show that our proposed GraphLAMA achieves state-of-the-art (SOTA) performance with 4.91% absolute improvement in accuracy. Compared with ICL, our inference speed can be 10 times faster under 5-shot setting. Our code is available on GitHub at https://github.com/BUPT-GAMMA/GraphLAMA.
Locally Balancing Signed Graphs
Authors: Weizhe Chen, Wentao Li, Min Gao, Dong Wen, Maolin Cai, Wei Wang
Venue: KDD
Year: 2025

Signed graphs capture both positive and negative relationships between entities, with balance being a fundamental concept. In these graphs, a vertex is considered balanced if all cycles it belongs to contain an even number of negative edges. On the other hand, unbalanced vertices often experience cognitive dissonance and emotional disturbance, motivating efforts to modify the graph to achieve balance for these vertices. Yet, most existing research emphasizes global balance, focusing on lengthy cycles that represent distant interactions. In contrast, this paper shifts the focus to local balance, where a vertex is deemed balanced when the triangles (length-three cycles) it participates in are positive, reflecting more immediate relationships. Building on this, we introduce the Locally Balancing Signed Graph (LBS) problem, which aims to maximize the number of locally balanced vertices through graph modification. Despite the NP-hard nature of the LBS problem and the absence of properties such as monotonicity and submodularity, our novel greedy method effectively addresses these challenges. We further enhance our method with dynamic computation and pruning techniques. Extensive experiments show the efficacy of our greedy method in solving the LBS problem and underscore the substantial runtime reductions achieved through our optimization techniques.
MSHTrans: Multi-Scale Hypergraph Transformer with Time-Series Decomposition for Temporal Anomaly Detection
Authors: Zhaoliang Chen, Zhihao Wu, William K. Cheung, Hong-Ning Dai, Byron Choi, Jiming Liu
Venue: KDD
Year: 2025

Time series anomaly detection has garnered significant research attention due to growing demands for temporal data monitoring across diverse domains. Despite the rapid advent of unsupervised anomaly detection models, existing approaches face two critical challenges in understanding the mechanisms of reconstruction-based models when handling diverse temporal dependencies: (1) the insufficient exploration of complex inter-timestamp relationships encompassing both short-term and long-term dependencies, and (2) the lack of integrated frameworks for jointly learning short-term patterns and long-term temporal characteristics. To address these challenges, we propose the novel Multi-Scale Hypergraph Transformer (MSHTrans), which leverages the capacity of hypergraphs for modeling multi-order temporal dependencies. Particularly, our method employs multi-scale downsampling to derive complementary fine-grained and coarse-grained representations, integrated with trainable hypergraph neural networks that can adaptively learn inter-timestamp relationships. The framework further integrates time series decomposition to systematically extract periodic and trend components from multi-granular features, thereby enhancing long-term dependency modeling. Through synergistic integration of learned short-term patterns and long-term temporal structures, the model achieves comprehensive time series reconstruction for effective anomaly detection. Extensive experiments demonstrate that MSHTrans outperforms state-of-the-art competitors with an average performance improvement of 8.21% (without point adjustment) and 3.52% (with point adjustment).
Multi-Agent Proactive Information Seeking with Adaptive LLM Orchestration for Non-Factoid Question Answering
Authors: Xinran Chen, Yuchen Li, Hengyi Cai, Zhuoran Ma, Xuanang Chen, Haoyi Xiong, Shuaiqiang Wang, Ben He, Le Sun, Dawei Yin
Venue: KDD
Year: 2025

The proliferation of complex non-factoid questions in modern information seeking (IS) systems exposes critical limitations in conventional Retrieval-Augmented Generation (RAG) approaches, particularly their static search strategies and the lack of systematic multi-source information integration capabilities. Facing these limitations, we present PASS (Proactive Agent-driven Search System), a novel multi-agent framework that operationalizes human-like proactive search strategies through five specialized agents: Revealer for intent analysis, Navigator for search planning, Seeker/Reader for adaptive retrieval, and Writer for response synthesis, systematically expanding the search space through iterative query refinement and multi-perspective knowledge integration. Crucially, our framework demonstrates remarkable adaptability to mid-sized LLMs, demonstrating its scalability in resource-constrained environments. To comprehensively assess the effectiveness of the proposed framework, we carry out extensive experiments on both mid-sized and proprietary large-scale LLMs, evaluating response quality for complex non-factoid questions using a newly introduced nugget-based assessment. Experimental results from offline nugget-based evaluation and online A/B Tests confirm substantial improvements in answer quality, advancing proactive information seeking methodologies and offering practical pathways for democratizing complex reasoning capabilities to resource-constrained environments.
Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
Authors: Mengjie Chen, Ming Zhang, Cunquan Qu
Venue: KDD
Year: 2025

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse drug reactions with significant implications for patient safety and healthcare outcomes. While graph-based methods have achieved strong predictive performance, most approaches treat drug pairs independently, overlooking the complex, context-dependent interactions unique to drug pairs. Additionally, these models struggle to integrate biological interaction networks and molecular-level structures to provide meaningful mechanistic insights. In this study, we propose MolecBioNet, a novel graph-based framework that integrates molecular and biomedical knowledge for robust and interpretable DDI prediction. By modeling drug pairs as unified entities, MolecBioNet captures both macro-level biological interactions and micro-level molecular influences, offering a comprehensive perspective on DDIs. The framework extracts local subgraphs from biomedical knowledge graphs and constructs hierarchical interaction graphs from molecular representations, leveraging classical graph neural network methods to learn multi-scale representations of drug pairs. To enhance accuracy and interpretability, MolecBioNet introduces two domain-specific pooling strategies: context-aware subgraph pooling (CASPool), which emphasizes biologically relevant entities, and attention-guided influence pooling (AGIPool), which prioritizes influential molecular substructures. The framework further employs mutual information minimization regularization to enhance information diversity during embedding fusion. Experimental results demonstrate that MolecBioNet outperforms state-of-the-art methods in DDI prediction, while ablation studies and embedding visualizations further validate the advantages of unified drug pair modeling and multi-scale knowledge integration. Moreover, the pooling strategies yield interpretable insights into the underlying interaction mechanisms, highlighting MolecBioNet's potential as a robust and transparent framework for predictive modeling in support of drug safety assessment.
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach
Authors: Chao Chen, Tian Zhou, Yanjun Zhao, Hui Liu, Rong Jin, Liang Sun
Venue: KDD
Year: 2025

Spatio-temporal forecasting is crucial in various fields and requires a careful balance between identifying subtle patterns and filtering out noise. Vector quantization (VQ) appears well-suited for this purpose, as it quantizes input vectors into a set of codebook vectors or patterns. Although VQ has shown promise in various computer vision tasks, it surprisingly falls short in enhancing the accuracy of spatio-temporal forecasting. We attribute this to two main issues: inaccurate optimization due to non-differentiability and limited representation power caused by hard-VQ structure. To tackle these challenges, we introduce Differentiable Sparse Soft-Vector Quantization (SVQ), the first VQ method to enhance spatio-temporal forecasting. SVQ balances detail preservation with noise reduction, offering full differentiability and a solid foundation in sparse regression. The method employs a two-layer MLP and an extensive codebook to streamline the sparse regression process, significantly cutting computational costs while simplifying training and improving performance. Empirical studies on five spatio-temporal benchmark datasets show SVQ achieves state-of-the-art results, including a 7.9% improvement on the WeatherBench-S temperature dataset and an average mean absolute error reduction of 9.4% in video prediction benchmarks (Human3.6M, KTH, and KittiCaltech), along with a 17.3% enhancement in image quality (measured by LPIPS).