OpenRaiser/Intern-Atlas huggingface dataset:
KnowFE : A Hybrid AI System for Explainable Feature Engineering using Knowledge-Guided Reinforcement Learning
Authors: Mohamed Bouadi, Arta Alavi, Salima Benbernou, Mourad Ouziri
Venue: CIKM
Year: 2025

Feature engineering is a critical yet often manual step in building effective machine learning models. While automated machine learning (AutoML) has streamlined many aspects of model development, the generation of high-quality, interpretable features remains a key bottleneck, requiring case-by-case domain knowledge and significant effort. This challenge highlights the importance of automated feature engineering (AutoFE) as a critical component within AutoML pipeline. To address this, we recently proposed SMART, a novel AutoFE approach that combines knowledge graph reasoning and deep reinforcement learning to guide the generation of interpretable features. In this demonstration, we introduce KnowFE, a web-based AutoFE platform powered by SMART. KnowFE enables users to generate high-quality, human-understandable features without writing any code, striking a balance between explainability and predictive performance. With a user-friendly interface, it empowers data practitioners to efficiently enhance machine learning workflows across diverse domains. A video demonstration is available at https://www.KnowFE.com .
Enhancing Fake News Video Detection via LLM-Driven Creative Process Simulation
Authors: Yuyan Bu, Qiang Sheng, Juan Cao, Shaofei Wang, Peng Qi, Yuhui Shi, Beizhe Hu
Venue: CIKM
Year: 2025

The emergence of fake news on short video platforms has become a new significant societal concern, necessitating automatic video-news-specific detection. Current detectors primarily rely on pattern-based features to separate fake news videos from real ones. However, limited and less diversified training data lead to biased patterns and hinder their performance. This weakness stems from the complex many-to-many relationships between video material segments and fabricated news events in real-world scenarios: a single video clip can be utilized in multiple ways to create different fake narratives, while a single fabricated event often combines multiple distinct video segments. However, existing datasets do not adequately reflect such relationships due to the difficulty of collecting and annotating large-scale real-world data, resulting in sparse coverage and non-comprehensive learning of the characteristics of potential fake news video creation. To address this issue, we propose a data augmentation framework AgentAug that generates diverse fake news videos by simulating typical creative processes. AgentAug implements multiple LLM-driven pipelines of four fabrication categories for news video creation, combined with an active learning strategy based on uncertainty sampling to select the potentially useful augmented samples during training. Experimental results on two benchmark datasets demonstrate that AgentAug consistently improves the performance of short video fake news detectors.
Large Model Annotation-Enhanced Spatio-Temporal Fusion Knowledge Tracing Model
Authors: Tianyu Cai, Xiaodi Huang, Tao Zhou, Yanting Li, Shenggen Ju
Venue: CIKM
Year: 2025

Knowledge Tracing (KT) aims to model students' evolving knowledge states based on their interactions, supporting downstream applications such as personalized resource recommendation. Recent works have leveraged large language models (LLMs) to annotate spatial topological structures -- e.g., prerequisite and hierarchical relations-between knowledge concepts, significantly reducing manual annotation costs. However, two major challenges remain: (1) low interpretability and potential unreliability due to LLM-induced annotation errors or hallucinations, and (2) limited cross-hierarchical interactions that hinder model performance. To address these issues, we propose a Large Model Annotation-Enhanced Spatio-Temporal Fusion KT model. First, we introduce a Detection-Reannotation Strategy to mitigate LLM annotation errors and hallucinations, resulting in a more accurate Knowledge Concept (KC) relation graph. Second, we present a Unit Relation Graph Annotation Method to reduce the distance between cross-hierarchical nodes, thereby enhancing their interactions. Lastly, we propose a Spatio-Temporal Fusion Framework, incorporating a dual-view contrastive learning module and a graph-structured knowledge state propagation module, to more effectively model students' knowledge propagation. Experiments on three real-world educational datasets demonstrate that our method effectively improves the practicality and reliability of LLM annotations, achieving state-of-the-art performance. Moreover, the knowledge propagation process based on the annotated graph enhances interpretability for educational applications.
ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions
Authors: Zi Cai, Yu Liu, Zhiyao Luo, Tingting Zhu
Venue: CIKM
Year: 2025

Digital healthcare systems have enabled the collection of mass healthcare data in electronic healthcare records (EHRs), allowing artificial intelligence solutions for various healthcare prediction tasks. However, existing studies often focus on isolated components of EHR data, limiting their predictive performance and interpretability. To address this gap, we propose ProtoEHR, an interpretable hierarchical prototype learning framework that fully exploits the rich, multi-level structure of EHR data to enhance healthcare predictions. More specifically, ProtoEHR models relationships within and across three hierarchical levels of EHRs: medical codes, hospital visits, and patients. We first leverage large language models to extract semantic relationships among medical codes and construct a medical knowledge graph as the knowledge source. Building on this, we design a hierarchical representation learning framework that captures contextualized representations across three levels, while incorporating prototype information within each level to capture intrinsic similarities and improve generalization. To perform a comprehensive assessment, we evaluate ProtoEHR in two public datasets on five clinically significant tasks, including prediction of mortality, prediction of readmission, prediction of length of stay, drug recommendation, and prediction of phenotype. The results demonstrate the ability of ProtoEHR to make accurate, robust, and interpretable predictions compared to baselines in the literature. Furthermore, ProtoEHR offers interpretable insights on code, visit, and patient levels to aid in healthcare prediction.
Dynamic Graph Learning via Historical Information Perception and Multi-Granular Temporal Curriculum Learning
Authors: Yuehang Cao, Xiang Zhao, Yang Fang, Yan Pan, Jiuyang Tang
Venue: CIKM
Year: 2025

Dynamic graph representation learning has emerged as a pivotal paradigm for modeling time-varying relational patterns in complex systems ranging from social networks to urban mobility. While existing methods achieve notable progress in temporal modeling, one critical challenge remains insufficiently addressed: identifying dual temporal evolution, i,e., instantaneous states and evolutionary trajectories. To address the challenge, we propose HMGNN, a novel dynamic graph learning framework that harmoniously integrates temporal dynamics modeling with stable structural representation learning, allowing adaptive pattern discovery while preserving feature consistency in evolving environments. Firstly, we propose a dynamic model that integrates a historical information perception module and a temporal aggregation module. The module converts the historical information into the model and adaptively measures the impact of the instantaneous and historical information effectively through the aggregation function. Secondly, we devise a dual-component model learning framework comprising contrastive learning and multi-granular temporal curriculum learning to holistically capture evolutionary dynamics. The contrastive learning component employs continuous-view contrastive alignment to preserve stable node feature across temporal evolution. Complementarily, our multi-granular temporal curriculum learning introduces masking mechanism to explicitly learn different time interval evolution patterns. Extensive experiments demonstrate the significant superiority of HMGNN against state-of-the-art dynamic graph learning methods in terms of all evaluation metrics.
Pantheon: Personalized Multi-objective Ensemble Sort via Iterative Pareto Policy Optimization
Authors: Jiangxia Cao, Pengbo Xu, Yin Cheng, Kaiwei Guo, Jian Tang, Shijun Wang, Dewei Leng, Shuang Yang, Zhaojie Liu, Yanan Niu, Guorui Zhou, Kun Gai
Venue: CIKM
Year: 2025

To provide promising recommendation results, there exist three major stages in the industrial RecSys chain to support our service: (1) The first Retrieval model aims at searching hundreds of item candidates. (2) Next, the Ranking model estimates the multiple aspect probabilities Pxtrs for each retrieved item. (3) At last, the Ensemble Sort stage merges those Pxtrs into one comparable score, and then selects the best dozen items with the highest scores to recommend them. To our knowledge, the wide-accepted industry ensemble sort approach still relies on manual formula-based adjustment, i.e., assigning manual weights for Pxtrs to control its influence on fusion score. Under this framework, the RecSys severely relies on expert knowledge to determine satisfactory weight for each Pxtr, which blocks RecSys's further advancements. In this paper, we provide Pantheon, a practical neural-network based ensemble sort. Compared with formulation-based ensemble sort, our Pantheon has the following advantages: (1) Personalized Joint Training: our Pantheon is jointly trained with the real-time ranking model, which could capture ever-changing user personalized interests accurately. (2) Representation inheritance: instead of the highly compressed Pxtrs, our Pantheon utilizes the fine-grained hidden-states as model input, which could benefit from the Ranking model to enhance our model complexity. Meanwhile, to reach a balanced multi-objective ensemble sort, we further devise an iterative Pareto policy optimization (IPPO) strategy to consider the multiple objectives at the same time. To our knowledge, this paper is the first work to replace the entire formulation-based ensemble sort in industry RecSys, which was fully deployed at Kuaishou live-streaming services, serving 400 Million users daily.
SDD: Shape-aware Data-driven Attention Mechanism for Time Series Analysis
Authors: Yanyun Cao, Rundong Zuo, Rui Cao, Byron Choi, Jianliang Xu, Sourav S. Bhowmick
Venue: CIKM
Year: 2025

Multivariate time series (mts ) analysis have extensive applications in various areas such as human activity recognition, healthcare, and economics, among others. Recently, Transformer approaches have been specifically designed for MTS and have consistently reported superior performance. In this paper, we demonstrate a software system for a recent efficient shape-aware Transformer (SDD ), where time-series subsequences (a.k.a shapes) are made available to users for investigation. First, a time-series Transformer, called SVP-T, takes shapes, together with their variable position information (VP information) as input to the training of a Transformer model. These shapes are computed from different variables and time intervals, enabling the Transformer model to learn dependencies simultaneously across both time and variables. Second, a data-driven kernel-based attention mechanism, called DARKER, reduces the time complexity of training Transformer models from O(N2) to O(N), where N is the number of inputs. As a result, the training process by using DARKER offers about 3x-4x speedup over vanilla Transformers'. In this demo, we present the first system (SDD ) that integrates SVP-T and DARKER. In particular, SDD visualizes the SVP-T's attention matrix and allows users to explore key shapes that have high attention weights. Furthermore, users can use SDD to decide the shape input to train a new model, to further balance between efficiency and accuracy.
State & Geopolitical Censorship on Twitter (X): Detection & Impact Analysis of Withheld Content
Authors: Yusuf Mücahit Çetinkaya, Tugrulcan Elmas
Venue: CIKM
Year: 2025

State and geopolitical censorship on Twitter, now X, has been turning into a routine, raising concerns about the boundaries between criminal content and freedom of speech. One such censorship practice, withholding content in a particular state has renewed attention due to Elon Musk's apparent willingness to comply with state demands. In this study, we present the first quantitative analysis of the impact of state censorship by withholding on social media using a dataset in which two prominent patterns emerged: Russian accounts censored in the EU for spreading state-sponsored narratives, and Turkish accounts blocked within Turkey for promoting militant propaganda. We find that censorship has little impact on posting frequency but significantly reduces likes and retweets by 25%, and follower growth by 90%-especially when the censored region aligns with the account's primary audience. Meanwhile, some Russian accounts continue to experience growth as their audience is outside the withholding jurisdictions. We develop a user-level binary classifier with a transformer backbone and temporal aggregation strategies, aiming to predict whether an account is likely to be withheld. Through an ablation study, we find that tweet content is the primary signal in predicting censorship, while tweet metadata and profile features contribute marginally. Our best model achieves an F1 score of 0.73 and an AUC of 0.83. This work informs debates on platform governance, free speech, and digital repression.
PyLate: Flexible Training and Retrieval for Late Interaction Models
Authors: Antoine Chaffin, Raphaël Sourty
Venue: CIKM
Year: 2025

Neural ranking has become a cornerstone of modern information retrieval. While single vector search remains the dominant paradigm, it suffers from the shortcoming of compressing all the information into a single vector. This compression leads to notable performance degradation in out-of-domain, long-context, and reasoning-intensive retrieval tasks. Multi-vector approaches pioneered by ColBERT aim to address these limitations by preserving individual token embeddings and computing similarity via the MaxSim operator. This architecture has demonstrated superior empirical advantages, including enhanced out-of-domain generalization, long-context handling, and performance in complex retrieval scenarios. Despite these compelling empirical results and clear theoretical advantages, the practical adoption and public availability of late interaction models remain low compared to their single-vector counterparts, primarily due to a lack of accessible and modular tools for training and experimenting with such models. To bridge this gap, we introduce PyLate, a streamlined library built on top of Sentence Transformers to support multi-vector architectures natively, inheriting its efficient training, advanced logging, and automated model card generation while requiring minimal code changes to code templates users are already familiar with. By offering multi-vector-specific features such as efficient indexes, PyLate aims to accelerate research and real-world application of late interaction models, thereby unlocking their full potential in modern IR systems. Finally, PyLate has already enabled the development of state-of-the-art models, including GTE-ModernColBERT and Reason-ModernColBERT, demonstrating its practical utility for both research and production environments.
Multivariate Wind Power Time Series Forecasting with Noise-Filtering Neural ODEs
Authors: Tianyu Chang, Dongming Chen, Dongqi Wang
Venue: CIKM
Year: 2025

In wind energy generation, wind power prediction traditionally relies on the simulation of wind turbine operational data. Recently, many complex deep learning networks have challenged the traditional paradigm.Multivariate Wind Power Time Series (MWTS) with strong volatility are often non-uniformly sampled, making it difficult for traditional time series methods to model. To address these challenges, our approach for MWTS forecasting includes two modules: (1) The ODE-filter module uses a noise extraction network to learn the Ordinary Differential Equations (ODE) continuous-time dynamics, which removes high-frequency noise by treating the noise as a neural flow. The neural flow pushes the ODE forward in time steps. (2) An adaptive decomposition module applies an unfixed window driven by gradients to capture trend and seasonal components including more abrupt change details. Our method models the precise wind power evolution, can naturally forecast the multivariate wind power time series and reduce the interference from noise and outliers in the data. Experimental results show that our approach outperforms existing models on regularly and irregularly sampled MWTS.
Enhancing Contrastive Link Prediction With Edge Balancing Augmentation
Authors: Chen-Hao Chang, Hui-Ju Hung, Chia-Hsun Lu, Chih-Ya Shen
Venue: CIKM
Year: 2025

Link prediction is one of the most fundamental tasks in graph mining, which motivates the recent studies of leveraging contrastive learning to enhance the performance. However, we observe two major weaknesses of these studies: i) the lack of theoretical analysis for contrastive learning on link prediction, and ii) inadequate consideration of node degrees in contrastive learning. To address the above weaknesses, we provide the first formal theoretical analysis for contrastive learning on link prediction, where our analysis results can generalize to the autoencoder-based link prediction models with contrastive learning. Motivated by our analysis results, we propose a new graph augmentation approach, Edge Balancing Augmentation (EBA), which adjusts the node degrees in the graph as the augmentation. We then propose a new approach, named Contrastive Link Prediction with Edge Balancing Augmentation (CoEBA), that integrates the proposed EBA and the proposed new contrastive losses to improve the model performance. We conduct experiments on 8 benchmark datasets. The results demonstrate that our proposed CoEBA significantly outperforms the other state-of-the-art link prediction models.
T-Retrievability: A Topic-Focused Approach to Measure Fair Document Exposure in Information Retrieval
Authors: Xuejun Chang, Zaiqiao Meng, Debasis Ganguly
Venue: CIKM
Year: 2025

Retrievability of a document is a collection-based statistic that measures its expected (reciprocal) rank of being retrieved within a specific rank cut-off. A collection with uniformly distributed retrievability scores across documents is an indicator of fair document exposure. While retrievability scores have been used to quantify the fairness of exposure for a collection, in our work, we use the distribution of retrievability scores to measure the exposure bias of retrieval models. We hypothesise that an uneven distribution of retrievability scores across the entire collection may not accurately reflect exposure bias but rather indicate variations in topical relevance. As a solution, we propose a topic-focused localised retrievability measure, which we call T-Retrievability (topic-retrievability), which first computes retrievability scores over multiple groups of topically-related documents, and then aggregates these localised values to obtain the collection-level statistics. Our analysis using this proposed T-Retrievability measure uncovers new insights into the exposure characteristics of various neural ranking models. The findings suggest that this localised measure provides a more nuanced understanding of exposure fairness, offering a more reliable approach for assessing document accessibility in IR systems.
Pruning Strategies for Backdoor Defense in LLMs
Authors: Santosh Chapagain, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Venue: CIKM
Year: 2025

Backdoor attacks are a significant threat to the performance and integrity of pre-trained language models. Although such models are routinely fine-tuned for downstream NLP tasks, recent work shows they remain vulnerable to backdoor attacks that survive vanilla fine-tuning. These attacks are difficult to defend because end users typically lack knowledge of the attack triggers. Such attacks consist of stealthy malicious triggers introduced through subtle syntactic or stylistic manipulations, which can bypass traditional detection and remain in the model, making post-hoc purification essential. In this study, we explore whether attention-head pruning can mitigate these threats without any knowledge of the trigger or access to a clean reference model. To this end, we design and implement six pruning-based strategies: (i) gradient-based pruning, (ii) layer-wise variance pruning, (iii) gradient-based pruning with structured L1/L2 sparsification, (iv) randomized ensemble pruning, (v) reinforcement-learning-guided pruning, and (vi) Bayesian uncertainty pruning. Each method iteratively removes the least informative heads while monitoring validation accuracy to avoid over-pruning. Experimental evaluation shows that gradient-based pruning performs best while defending the syntactic triggers, whereas reinforcement learning and Bayesian pruning better withstand stylistic attacks.
ProActLLM: Proactive Conversational Information Seeking with Large Language Models
Authors: Shubham Chatterjee, Xi Wang, Shuo Zhang, Sajad Ebrahimi, Zhaochun Ren, Debasis Ganguly, Gareth J. F. Jones, Emine Yilmaz, Hamed Zamani
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have transformed information access by enabling human-like text understanding and generation. This workshop explores the next step for conversational AI: building proactive information-seeking assistants that go beyond reactive question answering. We aim to investigate how LLMs can anticipate user needs, model complex context, support mixed-initiative interactions, integrate retrieval and external tools, personalize responses, adapt through feedback, and ensure fairness, transparency, and cognitive grounding. Bringing together experts from NLP, IR, HCI, and cognitive science, the workshop will serve as a timely forum for advancing intelligent, proactive dialogue systems. It will also foster interdisciplinary collaboration.
Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation
Authors: Xiaocong Chen, Siyu Wang, Lina Yao
Venue: CIKM
Year: 2025

Reinforcement learning-based recommender systems (RL4RS) have gained attention for their ability to adapt to dynamic user preferences. However, these systems face challenges, particularly in offline settings, where data inefficiency and reliance on pre-collected trajectories limit their broader applicability. While offline reinforcement learning methods leverage extensive datasets to address these issues, they often struggle with noisy data and fail to capture long-term user preferences, resulting in suboptimal recommendation policies. To overcome these limitations, we propose Diffusion-enhanced Actor-Critic for Offline RL4RS (DAC4Rec), a novel framework that integrates diffusion processes with reinforcement learning to model complex user preferences more effectively. DAC4Rec leverages the denoising capabilities of diffusion models to enhance the robustness of offline RL algorithms and incorporates a Q-value-guided policy optimization strategy to better handle suboptimal trajectories. Additionally, we introduce an energy-based sampling strategy to reduce randomness during recommendation generation, ensuring more targeted and reliable outcomes. We validate the effectiveness of DAC4Rec through extensive experiments on six real-world offline datasets and in an online simulation environment, demonstrating its ability to optimize long-term user preferences. Furthermore, we show that the proposed diffusion policy can be seamlessly integrated into other commonly used RL algorithms in RL4RS, highlighting its versatility and wide applicability.