OpenRaiser/Intern-Atlas huggingface dataset:
DAGP: Difficulty-Aware Graph Pruning for LLM-Based Multi-Agent System
Authors: Siqi Wang, Guangmo Tong
Venue: CIKM
Year: 2025

Large Language Model-based multi-agent systems demonstrate strong capabilities across different tasks. However, current methods often rely on static or task-specific designs. These instance-agnostic methods overlook varying instance complexities, leading to redundant communication in simple scenarios and insufficient coordination in demanding ones. Consequently, they failed to achieve effective and efficient collaborative reasoning among agents. To overcome these limitations, we propose Difficulty-Aware Graph Pruning (DAGP), an adaptive framework that configures communication structures based on instance-specific difficulty. DAGP integrates a difficulty estimation module and a sparsity control mechanism to selectively activate communication edges based on each instance, promoting efficient and targeted collaboration. Empirical evaluations across diverse benchmarks demonstrate that DAGP consistently achieves state-of-the-art performance compared to other baselines while reducing average token usage by 45%.
GCVPN: A Graph Convolutional Visual Prior-Transform Network for Actual Occluded Image Recognition
Authors: Lei Wang, Nannan Wu, Huaming Wu, Wei Yu, Fan Zhang, Shuo Chen
Venue: CIKM
Year: 2025

Image recognition plays a critical role in urban security, traffic management, and environmental monitoring, yet achieving high accuracy in obstructed scenes remains a challenge. To address this, we propose a Graph Convolutional Visual Prior-Transform Network (GCVPN), which significantly improves recognition accuracy and efficiency in complex environments. GCVPN introduces an image prior slicing and topology transformer to convert image data into graph-structured slice features, integrating domain overlap sampling and planar mapping to handle symmetry and enable precise, rapid anomaly detection. By combining a traditional VGG backbone with graph convolutional layers, GCVPN jointly captures topological relationships and feature semantics, while maintaining real-time efficiency with continuous recognition at 30 video frames per second. Extensive experiments demonstrate its effectiveness in photovoltaic panel anomaly detection and face occlusion recognition, highlighting strong potential for applications in intelligent surveillance and autonomous driving.
SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation
Authors: Binhao Wang, Yutian Xiao, Maolin Wang, Zhiqi Li, Tianshuo Wei, Ruocheng Guo, Xiangyu Zhao
Venue: CIKM
Year: 2025

Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critically impairing representation learning, especially for long-tail entities. Existing methods also often lack adaptive multi-source signal fusion tailored to item popularity. This paper introduces SPARK, a novel multi-stage framework systematically tackling these issues. SPARK first employs Tucker low-rank decomposition to denoise KGs and generate robust entity representations. Subsequently, an SVD-initialized hybrid geometric GNN concurrently learns representations in Euclidean and Hyperbolic spaces; the latter is strategically leveraged for its aptitude in modeling hierarchical structures, effectively capturing semantic features of sparse, long-tail items. A core contribution is an item popularity-aware adaptive fusion strategy that dynamically weights signals from collaborative filtering, refined KG embeddings, and diverse geometric spaces for precise modeling of both mainstream and long-tail items. Finally, contrastive learning aligns these multi-source representations. Extensive experiments demonstrate SPARK's significant superiority over state-of-the-art methods, particularly in improving long-tail item recommendation, offering a robust, principled approach to knowledge-enhanced recommendation. Implementation code is anonymously online. https://github.com/Applied-Machine-Learning-Lab/SPARK.
ACMCG: A Cost-effective Active Clustering with Minimal Constraint Graph
Authors: Qiu-Yu Wang, Wen-Bo Xie, Tao Deng, Tian Zou, Xuan-Lin Zhu, Xun Fu, Xin Wang
Venue: CIKM
Year: 2025

Active clustering enhances traditional semi-supervised clustering by introducing machine-led interaction, where informative constraints are dynamically selected and posed to humans. This enables goal-driven interaction and reduces the number of required constraints for achieving high-quality clustering. In this paper, we propose a newly designed Active Clustering framework with Minimal Constraint Graph (ACMCG). ACMCG operates on two cooperating tailored sparse graphs: a tree-structured graph (clustering tree) representing the nested clustering result, and a minimal constraint graph that supports constraint deduction during iterative refinement. In each refinement round, (a) the most suspicious edge in the tree is identified for constraint verification; (b) if a cannot-link constraint is confirmed, a pruning-and-grafting approach is performed to refine the clustering tree, guided by our proposed constraint deduction strategies; (c) the constraint is either deduced from the minimal constraint graph using transitive and probabilistic deduction, or obtained via user interaction when deduction fails. Extensive experiments across diverse domains demonstrate that ACMCG consistently outperforms both classical and state-of-the-art methods in accuracy, while significantly reducing the number of user-provided constraints and maintaining low computational cost, highlighting its cost-effectiveness in real-world applications.
Latent Graph Structure Learning for Large-Scale Traffic Forecasting
Authors: Meng Wang, Longgang Xiang, Chenhao Wu, Zejiao Wang, Xin Chen, Shaozu Xie, Ying Luo
Venue: CIKM
Year: 2025

Large-scale traffic forecasting poses new challenges in model architecture design due to the large data volume and high computation complexity. An effective solution is to partition traffic observation locations into several patches on which the computation burden decreases from node to patch level. However, since traffic states on road network are highly dynamic, current static and hard partition methods can not fully adapt to locations whose traffic semantics and evolving patterns vary across time. Therefore, we suppose locations on road network latently belong to certain graph structures and propose an adaptive and dynamic patching method in data-driven fashion. Besides, we design an end-to-end framework to learn patch assignments and predict future traffic states simultaneously. Experiments on real world large-scale traffic datasets further verify the effectiveness and interpretability of our proposed method.
Spatio-Temporal Wavelet Enhanced Attention Mamba for Stock Price Forecasting
Authors: Shurui Wang, Wenbo Yan, Ying Tan
Venue: CIKM
Year: 2025

Stock price forecasting remains a critical challenge due to market non-stationarity and the influence of multiple factors. Existing studies apply frequency domain analysis methods to mitigate the impacts of non-stationarity by decoupling high- and low-frequency variation patterns. However, these approaches primarily focus on single series decomposition while neglecting cross frequency interactions among different stocks. Moreover, as a key indicator of overall market trends, current methods inadequately utilize market index information. In this paper, we propose STEAM, a Spatio-Temporal Wavelet Enhanced Attention Mamba model. We introduce Discrete Wavelet Transform (DWT) to disentangle multi-frequency temporal features and propose Wavelet Enhanced Attention (WEA) to capture cross frequency spatial dependencies, effectively leveraging both local and global inter-stock relationships. To extract the synergistic spatio-temporal dependencies in stock data, AMamba module is designed that integrates WEA into the Mamba-2 architecture. Additionally, to further enhance the model's perception of macro-market conditions, we incorporate market index as a prefix, guiding predictions with holistic market information in both spatial and temporal dependencies learning. Extensive experiments across multiple national stock markets demonstrate that STEAM achieves state-of-the-art forecasting performance.
GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs
Authors: Hewen Wang, Renchi Yang, Xiaokui Xiao
Venue: CIKM
Year: 2025

Given a signed bipartite graph (SBG) G with two disjoint node sets U and V, the goal of link sign prediction is to predict the signs of potential links connecting U and V based on known positive and negative edges in G. The majority of existing solutions towards link sign prediction mainly focus on unipartite signed graphs, which are sub-optimal due to the neglect of node heterogeneity and unique bipartite characteristics of SBGs. To this end, recent studies adapt graph neural networks to SBGs by introducing message-passing schemes for both inter-partition (U x V) and intra-partition (U x U or V x V) node pairs. However, the fundamental spectral convolutional operators were originally designed for positive links in unsigned graphs, and thus, are not optimal for inferring missing positive or negative links from known ones in SBGs. Motivated by this, this paper proposes GegenNet, a novel and effective spectral convolutional neural network model for link sign prediction in SBGs. In particular, GegenNet achieves enhanced model capacity and high predictive accuracy through three main technical contributions: (i) fast and theoretically grounded spectral decomposition techniques for node feature initialization; (ii) a new spectral graph filter based on the Gegenbauer polynomial basis; and (iii) multi-layer sign-aware spectral convolutional networks alternating Gegenbauer polynomial filters with positive and negative edges. Our extensive empirical studies reveal that GegenNet can achieve significantly superior performance (up to a gain of 4.28% in AUC and 11.69% in F1) in link sign prediction compared to 11 strong competitors over 6 benchmark SBG datasets.
Full-Atom Protein-Protein Interaction Prediction via Atomic Equivariant Attention Network
Authors: Chunchen Wang, Cheng Yang, Wenchuan Yang, Le Song, Chuan Shi
Venue: CIKM
Year: 2025

Protein-protein Interaction (PPI) prediction, which aims to identify the interactions between proteins within a biological system, is an important problem in understanding disease mechanisms and drug discovery. Recently, Equivariant Graph Neural Networks (E3-GNNs) are advanced computational models that provide a powerful solution for accurately predicting PPIs by preserving the geometric integrity of protein interactions. However, most E3-GNNs model protein interactions at the residue level, potentially neglecting critical atomic details and side-chain conformations. In this paper, we propose a novel model, MEANT, designed to adaptively extract atom-level geometric information from varying numbers of atoms within different residues for PPI prediction. Specifically, we define a full-atom graph that contains atomic geometry and guides the message passing under the structure of residues. We also design a geometric relation extractor to integrate geometric information from different residues and adaptively handle variations in the number of atoms within each residue. Finally, we adopt the attention mechanism to update the residue representation and the atomic coordinates within a residue. Experimental results show that our proposed model, MEANT, significantly outperforms state-of-the-art methods on three typical PPI prediction tasks. Our code and data are available on GitHub at https://github.com/BUPT-GAMMA/MEANT.
EduCraft: A System for Generating Pedagogical Lecture Scripts from Long-Context Multimodal Presentations
Authors: Yucheng Wang, Jifan Yu, Daniel Zhang-Li, Joy Lim Jia Yin, Shangqing Tu, Haoxuan Li, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
Venue: CIKM
Year: 2025

Educators face substantial workload pressures, with significant time invested in preparing teaching materials. Generating high-quality lecture scripts from multimodal presentations is a particularly demanding aspect of this preparation. This paper introduces EduCraft, a novel system designed to automate Lecture Script Generation (LSG), addressing key difficulties such as comprehensive multimodal understanding, long-context coherence, and instructional design efficacy. EduCraft features a modular architecture comprising: (1) a Multimodal Input Processing pipeline for robust data extraction and association from slides; (2) a core Lecture Script Generation Engine with instruction-guided VLM and Caption+LLM workflows for pedagogical synthesis; (3) an optional Knowledge Augmentation Module using Retrieval-Augmented Generation (RAG) for enhanced factual grounding; and (4) a Model Integration and Deployment Interface supporting diverse AI models and providing a deployable API. Extensive evaluations, including human assessments and a new automated evaluation framework, demonstrate that EduCraft significantly outperforms strong baselines and teacher-refined scripts in producing coherent, readable, and pedagogically sound lecture scripts. By effectively tackling core LSG challenges, EduCraft offers a practical, configurable solution to reduce educator workload and enhance educational content creation. We open-source EduCraft at https://github.com/wyuc/EduCraft.
CLUE: Using Large Language Models for Judging Document Usefulness in Web Search Evaluation
Authors: Xingzhu Wang, Erhan Zhang, Yiqun Chen, Jinghan Xuan, Yucheng Hou, Yitong Xu, Ying Nie, Shuaiqiang Wang, Dawei Yin, Jiaxin Mao
Venue: CIKM
Year: 2025

The widely adopted Cranfield paradigm fails to adequately capture user satisfaction due to a weak relevance-satisfaction correlation. Additionally, constructing test collections incurs high relevance annotation costs. To address these two limitations, we aim to explore the use of large language models (LLMs) to generate multilevel usefulness labels. We propose CLUE, a user-centric evaluation method that explicitly incorporates users' search context and behavior information into LLMs. Inspired by ordinal regression, it employs a cascade structure tailored for multilevel usefulness judgments. Our study shows that using CLUE, LLMs can effectively assess usefulness when provided with search context and behavior, outperforming third-party labeling methods. We also conduct ablation studies to explore the impact of each component in CLUE. Finally, we utilize the usefulness labels generated by CLUE to predict user satisfaction. Real-world experiments reveal that incorporating CLUE's usefulness labels significantly enhances the performance of the satisfaction prediction model.
Strong Forgetting for ALCQ-Ontologies
Authors: Sen Wang, Yizheng Zhao
Venue: CIKM
Year: 2025

Forgetting is a non-standard reasoning procedure used to refine an ontology into a sub-signature by eliminating symbols not included in this subset, addressing fundamental challenges in knowledge management where ontology refinement and reuse are crucial for efficient information processing. It has two forms: weak forgetting (aka uniform interpolation), which preserves entailments within the source language, and strong forgetting, which additionally ensures model preservation modulo the eliminated symbols. This makes the latter significantly more challenging to compute. In this paper, we present the first method for strong role forgetting in description logics with qualified number restrictions (Q ). In particular, the method takes ALCQ-ontologies as input, yielding output either in ALCQ or in ALCQ(∇) by further incorporating the universal role ∇ to avoid information loss. This preserves model-theoretic properties crucial for applications such as modal correspondence theory and second-order quantifier elimination. While the method guarantees termination and soundness, its completeness is inherently constrained by the undecidability of strong forgetting. However, empirical evaluations on the Oxford-ISG and BioPortal benchmarks show that this theoretical limitation barely impedes practical utility, with experiment results demonstrating superb success rates and remarkably high efficiency.
TimeRAG: Enhancing Complex Temporal Reasoning with Search Engine Augmentation
Authors: Zhao Wang, Ziliang Zhao, Zhicheng Dou
Venue: CIKM
Year: 2025

While Large Language Models (LLMs) augmented with search engines have achieved remarkable progress in open-domain question answering, their ability to adapt to a rapidly evolving world remains limited. A critical challenge lies in the need for complex temporal reasoning to answer real-world questions. Current Retrieval-Augmented Generation (RAG) methods primarily focus on retrieving the latest information but often fail to perform sophisticated temporal reasoning. To address this gap, we propose TimeRAG, a novel RAG framework designed to dynamically handle complex temporal reasoning tasks. TimeRAG operates through the iterative collaboration of two modules: (1) a temporal-semantic Query Decomposition (QD) module, which breaks down the original question into atomic time-event sub-questions to guide multi-step retrieval, and (2) a time-aware Answer Generation (AG) module, which analyzes temporal contexts, generates intermediate answers with confidence scores, and synthesizes the final answer upon reasoning completion. The system is trained in three stages: (1) time-aware supervised fine-tuning of the AG module, (2) imitation learning for the QD module to enhance temporal decomposition ability, and (3) reinforcement learning for end-to-end joint optimization to enhance temporal coherence across the entire system. Evaluations on three challenging benchmarks show that TimeRAG significantly outperforms existing methods, particularly on questions involving fast-changing real-world events and those grounded in false premises that require detection and correction of outdated or incorrect assumptions.
GenR1-Searcher: Curriculum Reinforcement Learning for Dynamic Retrieval and Document Generation
Authors: Yu Wang, Yixuan Zhao, Renrui Duan, Jingyuan Li, Yuanzhuo Wang, Kun Zhang
Venue: CIKM
Year: 2025

Current RAG approaches follow two paradigms with complementary limitations: retrieve-then-read methods access reliable sources but produce noisy and incomplete information, while generate-then-read approaches create query-aligned documents but suffer from hallucinations. We propose GenR1-Searcher, a curriculum-based reinforcement learning framework that enables small language models to intelligently decide between retrieval and document generation during multi-hop reasoning through a three-stage progressive training strategy: first learning tool invocation syntax through format rewards, then mastering retrieval strategies with answer-based rewards, and finally acquiring adaptive tool selection capabilities when both knowledge sources are available. Extensive experiments on four multi-hop QA benchmarks demonstrate that GenR1-Searcher consistently outperforms strong baselines, achieving substantial relative improvements of 26.8%, 21.2%, and 15.7% on HotpotQA, 2WikiMultiHopQA, and MuSiQue respectively, outperforming competitive baselines including Search-o1, Search-R1, and ReARTeR, with analysis revealing that our model learns principled tool selection strategies that adapt based on tool capabilities and query characteristics.
Can Large Vision-Language Models Understand Multimodal Sarcasm?
Authors: Xinyu Wang, Yue Zhang, Liqiang Jing
Venue: CIKM
Year: 2025

Sarcasm is a complex linguistic phenomenon that involves a disparity between literal and intended meanings, making it challenging for sentiment analysis and other emotion-sensitive tasks. While traditional sarcasm detection methods primarily focus on text, recent approaches have incorporated multimodal information. However, the application of Large Visual Language Models (LVLMs) in Multimodal Sarcasm Analysis (MSA) remains underexplored. In this paper, we evaluate LVLMs in MSA tasks, specifically focusing on Multimodal Sarcasm Detection and Multimodal Sarcasm Explanation. Through comprehensive experiments, we identify key limitations, such as insufficient visual understanding and a lack of conceptual knowledge. To address these issues, we propose a training-free framework that integrates in-depth object extraction and external conceptual knowledge to improve the model's ability to interpret and explain sarcasm in multimodal contexts. The experimental results on multiple models show the effectiveness of our proposed framework. The code is available at https://github.com/cp-cp/LVLM-MSA.
Cequel: Cost-Effective Querying of Large Language Models for Text Clustering
Authors: Hongtao Wang, Taiyan Zhang, Renchi Yang, Jianliang Xu
Venue: CIKM
Year: 2025

Text clustering aims to automatically partition a collection of documents into coherent groups based on their linguistic features. In the literature, this task is formulated either as metric clustering over pre-trained text embeddings or as graph clustering based on pairwise similarities derived from an oracle, e.g., a large machine learning model. Recent advances in large language models (LLMs) have significantly improved this field by providing high-quality contextualized embeddings and accurate semantic similarity estimates. However, leveraging LLMs at scale introduces substantial computational and financial costs due to the large number of required API queries or inference calls. To address this issue, we propose Cequel, a cost-effective framework that achieves accurate text clustering under a limited budget of LLM queries. At its core, Cequel constructs must-link and cannot-link constraints by selectively querying LLMs on informative text pairs or triplets, identified via our proposed algorithms, EdgeLLM and TriangleLLM. These constraints are then utilized in a weighted constrained clustering algorithm to form high-quality clusters. Specifically, EdgeLLM and TriangleLLM employ carefully designed greedy selection strategies and prompting techniques to identify and extract informative constraints efficiently. Experiments on multiple benchmark datasets demonstrate that Cequel consistently outperforms existing methods in unsupervised text clustering under the same query budget.
Channel-Independent Refiner for Multivariate Time Series Forecasting
Authors: Jie Wang, Zhongguang Zheng, Chaoliang Zhong, Jun Sun
Venue: CIKM
Year: 2025

Real-world time series data are usually multivariate with complex channel relations. Some channels are highly related, while others are with limited correlation. Channel independence has shown great significance in multivariate time series forecasting to capture better individual channel characteristics. In order to learn channel patterns better, we propose a channel-independent Refiner as a plug-and-play module. Specifically, we devise a channel-wise Refiner connected to the output of existing methods. Leveraging the concatenation of original input and the coarse prediction from the base model, the Refiner produces a better estimation. Our Refiner also benefits from the channel-independent design and a post-training strategy, achieving significant improvement over base models. Extensive experiments on iTransformer, FEDformer, Autoformer, FreTS, DLinear and TSMixer demonstrate that our Refiner reduces forecasting errors on both Transformer-based and MLP-based models in over 90% of the experimental settings. Our Refiner combined with iTransformer establishes the new state-of-the-art.