OpenRaiser/Intern-Atlas huggingface dataset:
Where Do LLMs Go Wrong? Diagnosing Automated Peer Review via Aspect-Guided Multi-Level Perturbation
Authors: Jiatao Li, Yanheng Li, Xinyu Hu, Mingqi Gao, Xiaojun Wan
Venue: CIKM
Year: 2025

Large Language Models (LLMs) are increasingly integrated into academic peer review, prompting debates between full automation and purely human evaluation. Emerging evidence suggests optimal peer review leverages both human expertise and AI capabilities, and several major conferences have already adopted AI-assisted reviewing practices. However, effectively integrating these reviewers requires an aspect-based understanding of LLM vulnerabilities, clearly identifying specific dimensions where AI is most prone to error. Prior studies broadly caution against LLM biases but lack precise, aspect-specific insights necessary for informed human-AI partnerships in peer-review processes. We propose an aspect-guided, multi-level perturbation framework to systematically diagnose LLM weaknesses in automated peer review. By introducing targeted perturbations across key review components (papers, reviews, rebuttals) and evaluating impacts along critical quality dimensions (contribution, soundness, presentation, tone, completeness), our framework functions as a diagnostic tool: deviations from expected rating shifts after perturbation directly reveal specific LLM vulnerabilities. Our empirical analyses uncover recurring weaknesses, including misclassification of methodological flaws, disproportionate influence of strong rejection recommendations, inadequate responses to incomplete or negatively toned rebuttals, and misinterpretation of incorrect critiques as rigorous evaluations. These vulnerabilities consistently persist across diverse prompting strategies and a broad set of widely-used LLMs (e.g., GPT-4o, Gemini 2.0, LLaMA 3). This diagnostic framework provides granular insights into LLM limitations, empowering conference organizers to establish pragmatic, aspect-specific guidelines and enabling balanced, informed, and robust peer-review practices.
The International Workshop on Spatio-Temporal Data Intelligence and Foundation Models
Authors: Hao Miao, Yan Zhao, Yuxuan Liang, Bin Yang, Kai Zheng, Christian S. Jensen
Venue: CIKM
Year: 2025

Spatio-temporal data intelligence, which includes sensing, managing, and mining large-scale data across space and time, plays a pivotal role in understanding complex systems in real-world applications, such as urban computing and smart cities. With the rapid evolution of foundation models and their growing potential to transform spatio-temporal analytics, we propose a comprehensive half-day workshop (with at least 5 accepted papers, 3 keynote talks, 1 panel discussion, and over 50 attendees) at CIKM 2025, catering to professionals, researchers, and practitioners who are interested in spatio-temporal data intelligence and foundation models to address real-world challenges. The workshop will not only offer a platform for knowledge exchange but also acknowledge outstanding contributions through a distinguished Best Paper Award. A dedicated panel discussion will explore recent advances, emerging trends, and open challenges in integrating spatio-temporal data and emerging machine learning techniques, fostering dialogue between academia and industry. Note that this will be the eleventh time that our core members have organized a similar workshop. The previous 10 workshops were hosted in top-tier data mining and management venues, e.g., SIGKDD, WWW, and IJCAI, each of which attracted over 60 participants and 25 submissions on average.
Transformers are Good Clusterers for Lifelong User Behavior Sequence Modeling
Authors: Xingmei Wang, Shiyao Wang, Wuchao Li, Jiaxin Deng, Song Lu, Defu Lian, Guorui Zhou
Venue: CIKM
Year: 2025

Modeling user long-term behavior sequences is critical for enhancing Click-Through Rate (CTR) prediction. Existing methods typically employ two cascaded search units-General Search Unit (GSU) for rapid retrieval and Exact Search Unit (ESU) for precise modeling-to balance efficiency and effectiveness. However, they are constrained to recent behaviors due to computational limitations. Clustering user behaviors offers a potential solution, enabling GSU to access lifelong behaviors while maintaining inference efficiency, but current clustering approaches often lack generalizability, or fail to remain effective in high-dimensional data due to non-end-to-end clustering and recommendation. Given that centroids in clustering group similar data points based on proximity, similar to how queries function in transformers, we can integrate the learning of queries with CTR tasks in an end-to-end manner, shifting clustering from meaningless Euclidean distances to meaningful semantic distances. Therefore, we propose C-Former, a transformer-based clustering model specifically designed for modeling lifelong behavior sequences. The C-Former encoder leverages a group of learnable clustering anchor points that access the lifelong user behaviors to extract personalized interests. Then, the C-Former decoder reconstructs lifelong user behaviors based on the compact output of the encoder. The reconstruction and orthogonal loss ensure that centroids are informative and diverse in capturing user preferences. Clustering is further guided by supervisory signals from CTR, establishing an end-to-end framework. The proposed C-Former achieves linear time complexity in training with respect to sequence length and significantly reduces inference latency by directly utilizing cached centroids. Experiments on four benchmark datasets demonstrate the effectiveness of C-Former for lifelong user behavior sequence modeling. The code is available at https://github.com/pepsi2222/C-Former.
PKGRec: Personal Knowledge Graph Construction and Mining for Federated Recommendation Enhancement
Authors: Haochen Yuan, Yang Zhang, Quan Z. Sheng, Lina Yao, Yipeng Zhou, Xiang He, Zhongjie Wang
Venue: CIKM
Year: 2025

Personal Knowledge Graphs (PKGs) organize an individual user's information into a structured format comprising entities, attributes, and relationships. By leveraging this structured and semantically rich data, PKGs have become essential for securing personal data management and delivering personalized services. To unlock their potential in personalized recommendations, prior research has explored the construction of PKGs and recommendation methods built upon them. However, these studies often overlook challenges associated with distributed PKGs across different users, such as joint training and privacy protection. To address these challenges, we propose PKGRec, a federated graph recommendation method specifically designed for PKGs, which utilizes a federated learning framework to ensure user privacy and data security during joint learning. Furthermore, to accommodate the user-centric graph structure of PKGs, our approach categorizes entities into three types: users, items, and other entities. It then applies a novel staged graph convolution method to model various entities based on these entity categories during local training. To enable efficient graph information sharing among distributed PKGs without requiring additional data transfer or aggregation, PKGRec performs graph expansion on the trained gradients by federated aggregation. Extensive experiments conducted on four publicly available datasets demonstrate that our method consistently outperforms the existing federated recommendation approaches.
GCoder: Improving Large Language Model for Generalized Graph Reasoning
Authors: Qifan Zhang, Xiaobin Hong, Jianheng Tang, Nuo Chen, Yuhan Li, Wenzhong Li, Jing Tang, Jia Li
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable progress across a variety of reasoning tasks. Among these, graph-related tasks-which require the integration of multiple reasoning paradigms and whose complexity increases with graph size-present unique challenges for LLMs. Existing research primarily centers on chain-of-thought (CoT) methods and code-based approaches. While CoT methods have shown promise in many domains, they often underperform in graph reasoning due to unverifiable reasoning steps, computational inaccuracies, and limited generalization capabilities. Code-based approaches, which leverage LLMs to generate executable programs, offer an alternative paradigm by offloading complex computations to external tools. However, current LLMs still face challenges related to closed-source restrictions, deployment difficulties, code quality, and generalization. To address these limitations, we propose GCoder, a code-based LLM specifically designed to enhance performance in generalized graph reasoning tasks. Our approach includes the construction of a comprehensive training dataset, GraphWild, which encompasses a wide range of graph formats and algorithms. We employ a multi-stage post-training process, incorporating Supervised Fine-Tuning (SFT) and Reinforcement Learning from Compiler Feedback (RLCF), to further refine the model's capabilities. For unseen tasks, a hybrid retrieval strategy is utilized to boost performance. Experimental results demonstrate that GCoder outperforms GPT-4o, achieving an average accuracy improvement of 13.29% across 13 different graph reasoning problems. Additionally, GCoder efficiently handles large-scale graphs with millions of nodes and diverse input formats. This advancement paves the way for more intuitive and effective graph reasoning using LLMs. Our code is available at https://github.com/Bklight999/GCoder.
Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Authors: Zhengjia Wang, Qiang Sheng, Danding Wang, Beizhe Hu, Juan Cao
Venue: CIKM
Year: 2025

Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, such as writing patterns that include emotional words, stylistic features, etc. However, detectors tuned solely to such semantic clues can easily fall into surface detection patterns, which can shift rapidly in dynamic environments, leading to limited performance in the evolving news landscape. To address this issue, this paper investigates a novel perspective by incorporating news intent into fake news detection, bridging intents and semantics together. The core insight is that by considering news intents, one can deeply understand the inherent thoughts behind news deception, rather than the surface patterns within words alone. To achieve this goal, we propose Graph-based INtent-Semantic joInt moDEling (InSide) for fake news detection, which models deception clues from both semantic and intent signals via graph-based joint learning. Specifically, Inside reformulates news semantic and intent signals into heterogeneous graph structures, enabling long-range context interaction through entity guidance and capturing both holistic and implementation-level intent via coarse-to-fine intent modeling. To achieve better alignment between semantics and intents, we further develop a dynamic pathway-based graph alignment strategy for effective message passing and aggregation across these signals by establishing a common space. Extensive experiments on four benchmark datasets demonstrate the superiority of the proposed Inside compared to state-of-the-art methods.
ESPRESSO: Privacy-Preserving Keyword Search on Decentralized Data with Differential Visibility Constraints
Authors: Mohamed Ragab, Mohamed Bahrani, Helen Oliver, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, George Roussos
Venue: CIKM
Year: 2025

We present ESPRESSO, a system designed for scalable and privacy-preserving keyword search in decentralized data cooperatives. It addresses the challenges that differential access control (allowing different data access rights for different search parties) poses to de- centralized search by leveraging decentralized indexing, metadata-driven source selection, and decentralized ranking techniques. The system ensures that search parties can only access data within their data visibility scope, while maintaining high retrieval efficiency and results quality. This demo will showcase its functionality through interactive scenarios, including live querying, dynamic source selection, and real-time visualization of search results. The audience will have hands-on interaction with the system, exploring its application in real-world scenarios, such as in the healthcare domain.
Socially Responsible and Trustworthy Generative Foundation Models: Principles, Challenges, and Practices
Authors: Yue Huang, Canyu Chen, Lu Cheng, Bhavya Kailkhura, Nitesh V. Chawla, Xiangliang Zhang
Venue: CIKM
Year: 2025

Generative foundation models (GenFMs), including large language and multimodal models, are transforming information retrieval and knowledge management. However, their rapid adoption raises urgent concerns about social responsibility, trustworthiness, and governance. This tutorial offers a comprehensive, hands-on overview of recent advances in responsible GenFMs, covering foundational concepts, multi-dimensional risk taxonomies (including safety, privacy, robustness, truthfulness, fairness, and machine ethics), state-of-the-art evaluation benchmarks, and effective mitigation strategies. We integrate real-world case studies and practical exercises using open-source tools, and present key perspectives from both policy and industry, including recent regulatory developments and enterprise practices. The session concludes with a discussion of open challenges, providing actionable guidance for the CIKM community.
Urban In-context Learning: A New Paradigm for Urban Indicator Prediction
Authors: Zerong Deng, Liangzhe Han, Tongyu Zhu, Ziqi Miao, Yi Xu, Leilei Sun
Venue: CIKM
Year: 2025

Recent years have witnessed the rapid development of urbanization. Specifically, urban indicator prediction has become an important tool for urban planning and decision-making and promoting the process of urbanization. However, the existing methods have the following two drawbacks. First, they follow the ''pre-training and fine-tuning'' paradigm, which is time-consuming and resource-intensive. Second, to encode urban knowledge for downstream tasks effectively, complex pre-training tasks must be designed to train the model in a task-agnostic manner while ensuring generalization. In this work, we propose UrbanICL, an urban in-context learning framework as a new paradigm for urban indicator prediction. Compared to directly predicting urban indicators, we obtain predictions for new regions by aggregating the downstream labels of similar regions. Specifically, a retrieval-based urban in-context learning module is proposed to retrieve regions with similar urban semantics and aggregate their corresponding labels to make predictions for new regions. We also design a region-dependent distribution learning module to learn the new distribution of unknown regions and facilitate the adaptation of UrbanICL for distributional shifts and outliers. Our framework, with in-context learning, brings a new insight for urban indicator prediction. We conduct extensive experiments on real-world datasets collected from three cities. The experiment results demonstrate the effectiveness of UrbanICL, even in an extremely low-consumption and time-efficient manner.
Parameter-Efficient Transfer Learning for EEG Foundation Models via Task-Relevant Feature Focusing
Authors: Jaehyun Jeon, Seungwoo Jeong, Yeajin Shon, Heung-Il Suk
Venue: CIKM
Year: 2025

Electroencephalogram (EEG)-based brain-computer interfaces face challenges in data insufficiency to train neural networks for tasks and generalizability for multiple subjects. To address the issue of data scarcity in EEG research, transfer learning using EEG foundation models (EFMs) has recently gained attention for its ability to leverage prior knowledge. Although transfer learning with EFMs enables tasks to be performed with limited training data, their increasing size presents significant computational challenges. Parameter-efficient transfer learning (PETL) methods address this computational issue by tuning only a small subset of parameters from the pre-trained model. However, existing PETL methods mostly fail to account for the high-dimensional nature of EEG data, which limits their ability to fully leverage the prior knowledge of the EFM when applied to downstream tasks. To address these challenges, we propose a novel PETL method with a TASk-relevanT fEature Focusing modULe (TASTEFUL) to transfer EFMs efficiently. TASTEFUL is designed to focus on task-relevant features and efficiently learn representations tailored for downstream tasks. We evaluated our proposed TASTEFUL on tasks using publicly available EEG datasets, demonstrating its superior performance. Finally, our work highlights TASTEFUL's potential to enhance the practical application of EFMs, marking a significant advancement in PETL for EFMs.
On Influence Tail Bounds in Online Social Networks
Authors: Michael Simpson, Laks V. S. Lakshmanan, Venkatesh Srinivasan, Alex Thomo
Venue: CIKM
Year: 2025

The influence estimation and maximization problems study the expected reach of a seed set in social networks under a stochastic propagation model. Motivated by the practical utility of characterizing the distribution of reach values, we systematically analyze the tail behaviour of the reach of a seed set. We study tail bound query problems that, for a given seed set, compute either the maximum reach for a given probability threshold or the highest probability of achieving a target reach. We prove #P-hardness and propose algorithms that balance efficiency and accuracy. We also examine tail bound optimization problems that find a seed set maximizing reach for a target probability or maximizing the probability of achieving a target reach, and establish strong inapproximability results. Experiments on real datasets demonstrate the effectiveness of our algorithms, showing that good approximations of the actual reach for a desired probability can be computed efficiently and that the actual reach can be very different from the expected reach.
Self-supervised Dual-view Framework with Tailored Negative Sampling for New Activity Detection
Authors: Hyunju Kim, Dongman Lee
Venue: CIKM
Year: 2025

With the growing adoption of IoT, analyzing multi-modal sensor time series for human activity recognition has become crucial for intelligent and context-aware applications. While existing approaches assume a fixed set of known activities, real-world deployments encounter new activities not present in the training data. Detecting them is challenging due to overlapping patterns between known and new activities, high intra-class variability, and sensor heterogeneity across datasets. To address these challenges, we propose CLAN, a novel self-supervised dual-view framework for new activity detection. It employs a two-tower architecture that extracts discriminative representations from both time and frequency domains. By treating multiple strongly augmented views of known activity samples as negatives, the model learns invariant representations that effectively distinguish new activities. In addition, a dataset-aware augmentation selection mechanism adaptively determines transformations tailored to each dataset's characteristics, thereby enhancing generalization across diverse sensor environments. Extensive experiments on five real-world human activity datasets demonstrate that CLAN consistently outperforms new activity detection baselines, achieving up to 9.24% improvement in AUROC.
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Authors: Zhichen Zeng, Xiaolong Liu, Mengyue Hang, Xiaoyi Liu, Qinghai Zhou, Chaofei Yang, Yiqun Liu, Yichen Ruan, Laming Chen, Yuxin Chen, Yujia Hao, Jiaqi Xu, Jade Nie, Xi Liu, Buyun Zhang, Wei Wen, Siyang Yuan, Hang Yin, Xin Zhang, Kai Wang, Wen-Yen Chen, Yiping Han, Huayu Li, Chunzhi Yang, Bo Long, Philip S. Yu, Hanghang Tong, Jiyan Yang
Venue: CIKM
Year: 2025

Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction. However, most of the existing methods suffer from two fundamental limitations, including (1) insufficient inter-mode interaction due to the unidirectional information flow between modes, and (2) aggressive information aggregation caused by early summarization, resulting in excessive information loss. To address these limitations, we propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style. To achieve better interaction learning, InterFormer enables bidirectional information flow for mutually beneficial learning across different modes. To avoid aggressive information aggregation, we retain complete information in each data mode and use a separate Cross Arch for effective information selection and summarization. InterFormer has been deployed across multiple platforms at Meta Ads, achieving 0.15% performance gain and 24% QPS gain compared to prior state-of-the-art models and yielding sizable online impact.
PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation
Authors: Ching Chang, Ming-Chih Lo, Wen-Chih Peng, Tien-Fu Chen
Venue: CIKM
Year: 2025

Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained system behaviors to fine-grained, detailed events. Effectively segmenting and integrating states across these different granularities is crucial for tasks like predictive maintenance and performance optimization. However, existing time series segmentation methods face two key challenges: (1) the inability to handle multiple levels of granularity within a unified model, and (2) limited adaptability to new, evolving patterns in dynamic environments. To address these challenges, we propose PromptTSS, a novel framework for time series segmentation with multi-granularity states. PromptTSS uses a unified model with a prompting mechanism that leverages label and boundary information to guide segmentation, capturing both coarse- and fine-grained patterns while adapting dynamically to unseen patterns. Experiments show PromptTSS improves accuracy by 24.49% in multi-granularity segmentation, 17.88% in single-granularity segmentation, and up to 599.24% in transfer learning, demonstrating its adaptability to hierarchical states and evolving time series dynamics. Our code is available at https://github.com/blacksnail789521/PromptTSS.