OpenRaiser/Intern-Atlas huggingface dataset:
HCLeK: Hierarchical Compression of Legal Knowledge for Retrieval-Augmented Generation
Authors: Jianhui Yang, Huanghai Liu, Mingruo Yuan, Yiran Hu, Yun Liu, Weixing Shen, Ben Kao
Venue: CIKM
Year: 2025

Prompt compression for Retrieval-Augmented Generation (RAG) often fails by treating all retrieved information uniformly. This undifferentiated approach neglects the critical distinction between foundational core knowledge and illustrative practical knowledge, a failure especially damaging in hierarchical domains like law where essential principles can be discarded for redundant details, diminishing information gain. To address this, we propose HCLeK, a Hierarchical Compression framework for Legal Knowledge. HCLeK uniquely leverages high-density core knowledge to guide the hierarchical compression of voluminous practical knowledge. The framework operates in three stages: (1) Core-Knowledge Guided Reranking to prioritize practical knowledge based on its semantic relevance to core legal principles; (2) Priority-Decay Budget Allocation to dynamically assign compression budgets, focusing on the most salient information; and (3) Relevance-Diversity Aware Semantic Compression for fine-grained sentence-level compression. Experimental results on the complex task of Legal Judgment Prediction (LJP) validate that HCLeK achieves state-of-the-art performance across various high compression ratios (0.5--0.05), demonstrating its effectiveness and robustness. Our code is available at https://github.com/fupanY/HCLeK.
A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering
Authors: Lin Li, Chunyang Li, Yu Yin, Xiaohui Tao, Jianwei Zhang
Venue: CIKM
Year: 2025

In the realm of collaborative filtering recommendation systems, Graph Neural Networks (GNNs) have demonstrated remarkable performance but face significant challenges in deployment on resource-constrained edge devices due to their high embedding parameter requirements and computational costs. Using common quantization method directly on node embeddings may overlooks their graph based structure, causing error accumulation during message passing and degrading the quality of quantized embeddings.To address this, we propose Graph based Node-Aware Dynamic Quantization training for collaborative filtering (GNAQ), a novel quantization approach that leverages graph structural information to enhance the balance between efficiency and accuracy of GNNs for Top-K recommendation. GNAQ introduces a node-aware dynamic quantization strategy that adapts quantization scales to individual node embeddings by incorporating graph interaction relationships. Specifically, it initializes quantization intervals based on node-wise feature distributions and dynamically refines them through message passing in GNN layers. This approach mitigates information loss caused by fixed quantization scales and captures hierarchical semantic features in user-item interaction graphs. Additionally, GNAQ employs graph relation-aware gradient estimation to replace traditional straight-through estimators, ensuring more accurate gradient propagation during training. Extensive experiments on four real-world datasets demonstrate that GNAQ outperforms state-of-the-art quantization methods, including BiGeaR and N2UQ, by achieving average improvement in 27.8% Recall@10 and 17.6% NDCG@10 under 2-bit quantization. In particular, GNAQ is capable of maintaining the performance of full-precision models while reducing their model sizes by 8 to 12 times; in addition, the training time is twice as fast compared to quantization baseline methods.
Jailbreaking LLMs Through Alignment Vulnerabilities in Out-of-Distribution Settings
Authors: Yue Huang, Jingyu Tang, Dongping Chen, Bingda Tang, Yao Wan, Lichao Sun, Philip S. Yu, Xiangliang Zhang
Venue: CIKM
Year: 2025

Recently, Large Language Models (LLMs) have shown remarkable capabilities, but concerns about their trustworthiness-especially under ''jailbreaking'' attacks-remain unresolved. Prior work often assumes white-box access or relies on fixed prompt templates, limiting practicality. We propose ObscurePrompt, a simple yet effective black-box jailbreak method inspired by fragile LLM alignment on Out-of-Distribution (OOD) inputs. ObscurePrompt constructs base prompts using existing jailbreak techniques, then employs powerful LLMs to iteratively generate obscure variants that evade detection. Extensive experiments demonstrate that ObscurePrompt outperforms existing methods and remains effective against two widely-used defenses.
SpeedSteiner: A Fast O(k1/2)-Approximation Algorithm for Directed Steiner Tree
Authors: Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Venue: CIKM
Year: 2025

The directed Steiner tree problem is fundamental in computer science with numerous applications. However, to date, there are no efficient algorithms with quality guarantees. In this paper, we take on this challenge and offer a fast algorithm with provable approximation guarantees. We introduce SpeedSteiner, a O(k1/2)-approximation algorithm, where k is the number of terminal nodes. In practice, SpeedSteiner can be several orders of magnitude faster than other methods with a similar approximation ratio. The speedup is achieved by combining several optimization techniques that exploit the inner structure of recursive-greedy algorithms. We systematically evaluate the proposed algorithm and verify its scalability and strong empirical performance.
Towards Reliable GNNs: Adversarial Calibration Learning for Confidence Estimation
Authors: Yilong Wang, Jiahao Zhang, Tianxiang Zhao, Suhang Wang
Venue: CIKM
Year: 2025

Graph neural networks (GNNs) have achieved strong predictive performance across a range of tasks, yet they often exhibit poor confidence calibration-where the predicted confidence scores do not accurately reflect the true likelihood of correctness. This shortcoming raises concerns about their reliability in critical domains such as fraud detection and risk assessment, where well-calibrated predictions are essential for sound decision-making. Although several calibration methods have been proposed for GNNs, our experiments reveal that they tend to focus on global calibration while failing to generalize across different node groups, such as those defined by degree, class, or local structural patterns. In some cases, these methods even degrade calibration performance compared to the original uncalibrated models. To address this limitation, we introduce AdvCali, a novel framework that adaptively improves calibration across diverse node groups. AdvCali employs adversarial training to automatically identify miscalibrated groups and incorporates a differentiable Group Expected Calibration Error (ECE) loss to refine confidence estimates within them. This enables the model to adjust its calibration strategy dynamically, without relying on prior knowledge of which node groups are miscalibrated. Extensive experiments on real-world datasets show that AdvCali not only improves global calibration but also significantly enhances calibration within groups defined by feature similarity, graph topology, and connectivity patterns, outperforming existing approaches.
FUTURE: Flexible Unlearning for Tree Ensemble
Authors: Ziheng Chen, Jin Huang, Jiali Cheng, Yuchan Guo, Mengjie Wang, Lalitesh Morishetti, Kaushiki Nag, Hadi Amiri
Venue: CIKM
Year: 2025

Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the right to be forgotten, several unlearning algorithms have been proposed to enable tree ensembles to forget sensitive information. However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets. To address these limitations, we propose FUTURE, a novel unlearning algorithm for tree ensembles. Specifically, we formulate the problem of forgetting samples as a gradient-based optimization task. In order to accommodate non-differentiability of tree ensembles, we adopt the probabilistic model approximations within the optimization framework. This enables end-to-end unlearning in an effective and efficient manner. Extensive experiments on real-world datasets show that FUTURE yields significant and successful unlearning performance.
Learning from Graph: Mitigating Label Noise on Graph through Topological Feature Reconstruction
Authors: Zhonghao Wang, Yuanchen Bei, Sheng Zhou, Zhiyao Zhou, Jiapei Fan, Hui Xue, Haishuai Wang, Jiajun Bu
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) have shown remarkable performance in modeling graph data. However, Labeling graph data typically relies on unreliable information, leading to noisy node labels. Existing approaches for GNNs under Label Noise (GLN) employ supervision signals beyond noisy labels for robust learning. While empirically effective, they tend to over-reliance on supervision signals built upon external assumptions, leading to restricted applicability. In this work, we shift the focus to exploring how to extract useful information and learn from the graph itself, thus achieving robust graph learning. From an information theory perspective, we theoretically and empirically demonstrate that the graph itself contains reliable information for graph learning under label noise. Based on these insights, we propose the Topological Feature Reconstruction (TFR) method. Specifically, TFR leverages the fact that the pattern of clean labels can more accurately reconstruct graph features through topology, while noisy labels cannot. TFR is a simple and theoretically guaranteed model for robust graph learning under label noise. We conduct extensive experiments across datasets with varying properties. The results demonstrate the robustness and broad applicability of our proposed TFR compared to state-of-the-art baselines. Codes are available at https://github.com/eaglelab-zju/TFR.
FROG: Fair Removal on Graph
Authors: Ziheng Chen, Jiali Cheng, Hadi Amiri, Kaushiki Nag, Lu Lin, Sijia Liu, Gabriele Tolomei, Xiangguo Sun
Venue: CIKM
Year: 2025

With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many of which are naturally represented as graphs. However, existing graph unlearning methods often modify nodes or edges indiscriminately, overlooking their impact on fairness. For instance, forgetting links between users of different genders may inadvertently exacerbate group disparities. To address this issue, we propose a novel framework that jointly optimizes both the graph structure and the model to achieve fair unlearning. Our method rewires the graph by removing redundant edges that hinder forgetting while preserving fairness through targeted edge augmentation. We further introduce a worst-case evaluation mechanism to assess robustness under challenging scenarios. Experiments on real-world datasets show that our approach achieves more effective and fair unlearning than existing baselines.
Learning Global-Local Multi-Scale Node Embeddings with Random Walks and Landmark-Guided Optimization
Authors: Ali Assi, Nour El Islem Karabadji, Mohamed Elati, Wajdi Dhifli
Venue: CIKM
Year: 2025

Learning low-dimensional representations for nodes is fundamental to various graph learning tasks. Existing approaches often struggle to comprehensively capture the connections between local structure, contextual information, and global node positioning in the graph. This paper introduces GLOW, a novel approach for node representation learning that jointly maximizes the likelihood of preserving these crucial aspects of the graph structure. GLOW integrates 1) local structural and contextual information, captured through random walks, with 2) global node positioning, approximated using a set of selected landmark nodes. This strategy allows GLOW to learn node embeddings that are structurally aware and informative for downstream tasks. We evaluate GLOW's performance on several benchmark datasets for node classification and link prediction, and compare it to a wide range of established baseline methods. The obtained results consistently demonstrate that GLOW achieves substantial performance improvements. This highlights the advantages of integrating local and global structural learning in effectively capturing graph structure and generating highly resilient node embeddings.
From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification
Authors: Junhua Liu, Tan Yong Keat, Bin Fu, Kwan Hui Lim
Venue: CIKM
Year: 2025

In conversational AI systems, a critical challenge in training effective multi-turn intent classification models lies in the generation of large-scale, domain-specific, multilingual dialogue datasets. In this paper, we introduce Chain-of-Intent, a novel framework that integrates Hidden Markov Models (HMMs) with Large Language Models (LLMs) to generate intent-driven, context-aware dialogues through self-play. Our method first extracts domain-specific intent transition patterns from real-world e-commerce chat logs, which guide the modeling of turn-level dynamics and intent sequences. LLMs are then employed to parameterize the emission probabilities of HMMs, enabling the generation of natural, coherent utterances aligned with predicted intents and dialogue context. We also propose MINT-CL, a multi-task contrastive learning framework for multi-turn intent classification, which improves performance while reducing dependence on large-scale annotated datasets. Empirical results demonstrate that our approach outperforms competitive baselines in dialogue generation quality and classification accuracy, particularly in multilingual settings. To facilitate future research, we release MINT-E, a comprehensive, multilingual, intent-aware multi-turn dialogue corpus derived from the e-commerce domain.
Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
Authors: Ping Liu, Jianqiang Shen, Qianqi Shen, Chunnan Yao, Kevin Kao, Dan Xu, Rajat Arora, Baofen Zheng, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang
Venue: CIKM
Year: 2025

Query understanding is essential in modern relevance systems, where user queries are often short, ambiguous, and highly context-dependent. Traditional approaches often rely on multiple task-specific Named Entity Recognition models to extract structured facets as seen in job search applications. However, this fragmented architecture is brittle, expensive to maintain, and slow to adapt to evolving taxonomies and language patterns. In this paper, we introduce a unified query understanding framework powered by a Large Language Model (LLM), designed to address these limitations. Our approach jointly models the user query and contextual signals such as profile attributes to generate structured interpretations that drive more accurate and personalized recommendations. The framework improves relevance quality in online A/B testing while significantly reducing system complexity and operational overhead. The results demonstrate that our solution provides a scalable and adaptable foundation for query understanding in dynamic web applications.
STORM: Spatio-Temporal Similar Trajectory Retrieval on Non-Uniform Maritime Data
Authors: Xiaolin Han, Yonghao Zhou, Chenhao Ma, Fang Li, Xuequn Shang
Venue: CIKM
Year: 2025

Similar trajectory retrieval is crucial for maritime trajectory data analysis. However, due to issues such as errors in maritime positioning devices and the accuracy limitations of satellite positioning systems at sea, maritime trajectory data often exhibit characteristics of non-uniform sampling. Existing algorithms struggle to effectively model the irregularity of non-uniformly sampled maritime trajectories, leading to reduced performance in similar trajectory retrieval. In this demonstration, we present STORM, a system designed to effectively retrieve the top-k similar trajectories, which supports both user-specified and automated query settings. STORM utilizes a learnable Fourier-based encoding method to efficiently extract spatiotemporal features from non-uniform trajectories, significantly enhancing the model's performance in similar trajectory retrieval. Our demonstration shows that, compared to state-of-the-art (SOTA) methods, STORM achieves a 41.9% improvement in performance for similar trajectory retrieval on non-uniform maritime data. Our demonstration video is available at https://github.com/itszzzyyy/STORM.
GCLS2: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics
Authors: Qi Wen, Yiyang Zhang, Yutong Ye, Yingbo Zhou, Nan Zhang, Xiang Lian, Mingsong Chen
Venue: CIKM
Year: 2025

Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structure semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.
Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation
Authors: Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Wei Wang, Xiping Hu, Edith C. H. Ngai
Venue: CIKM
Year: 2025

Graph Collaborative Filtering (GCF) has emerged as a dominant paradigm in modern recommendation systems, excelling at modeling complex user-item interactions and capturing high-order collaborative signals. Most existing GCF models predominantly rely on simplified graph architectures like LightGCN, which strategically remove feature transformation and activation functions from vanilla graph convolution networks. Through systematic analysis, we reveal that feature transformation in message propagation can enhance model representation, though at the cost of increased training difficulty. To this end, we propose FourierKAN-GCF, a novel framework that adopts Fourier Kolmogorov-Arnold Networks as efficient transformation modules within graph propagation layers. This design enhances model representation while decreasing training difficulty. Our FourierKAN-GCF can achieve higher recommendation performance than most widely used GCF backbone models and can be integrated into existing advanced self-supervised models as a backbone, replacing their original backbone to achieve enhanced performance. Extensive experiments on three public datasets demonstrate the superiority of FourierKAN-GCF.
Hypercomplex Prompt-aware Multimodal Recommendation
Authors: Zheyu Chen, Jinfeng Xu, Hewei Wang, Shuo Yang, Zitong Wan, Haibo Hu
Venue: CIKM
Year: 2025

Modern recommender systems face critical challenges in handling information overload while addressing the inherent limitations of multimodal representation learning. Existing methods suffer from three fundamental limitations: (1) restricted ability to represent rich multimodal features through a single representation, (2) existing linear modality fusion strategies ignore the deep nonlinear correlations between modalities, and (3) static optimization methods failing to dynamically mitigate the over-smoothing problem in graph convolutional network (GCN). To overcome these limitations, we propose HPMRec, a novel Hypercomplex Prompt-aware Multimodal Recommendation framework, which utilizes hypercomplex embeddings in the form of multi-components to enhance the representation diversity of multimodal features. HPMRec adopts the hypercomplex multiplication to naturally establish nonlinear cross-modality interactions to bridge semantic gaps, which is beneficial to explore the cross-modality features. HPMRec also introduces the prompt-aware compensation mechanism to aid the misalignment between components and modality-specific features loss, and this mechanism fundamentally alleviates the over-smoothing problem. It further designs self-supervised learning tasks that enhance representation diversity and align different modalities. Extensive experiments on four public datasets show that HPMRec achieves state-of-the-art recommendation performance.
Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models
Authors: Zhenliang Zhang, Junzhe Zhang, Xinyu Hu, Huixuan Zhang, Xiaojun Wan
Venue: CIKM
Year: 2025

Large language models (LLMs) have achieved remarkable success in various tasks, yet they remain vulnerable to faithfulness hallucinations, where the output does not align with the input. In this study, we investigate whether social bias contributes to these hallucinations, a causal relationship that has not been explored. A key challenge is controlling confounders within the context, which complicates the isolation of causality between bias states and hallucinations. To address this, we utilize the Structural Causal Model (SCM) to establish and validate the causality and design bias interventions to control confounders. In addition, we develop the Bias Intervention Dataset (BID), which includes various social biases, enabling precise measurement of causal effects. Experiments on mainstream LLMs reveal that biases are significant causes of faithfulness hallucinations, and the effect of each bias state differs in direction. We further analyze the scope of these causal effects across various models, specifically focusing on unfairness hallucinations, which are primarily targeted by social bias, revealing the subtle yet significant causal effect of bias on hallucination generation.