OpenRaiser/Intern-Atlas huggingface dataset:
MRCLQR: A Framework for Logical Query Reasoning Based on Multi-information Relation Constraints
Authors: Pengwei Pan, Yu Liu, Jun Ma, Jianfeng Qu, Wen Hua, Yanmei Kang
Venue: CIKM
Year: 2025

The Knowledge Graph logical reasoning task faces a dual challenge of insufficient semantic coverage from type information and missing structural information from relations. Although type annotations provide semantic priors for entities, their coarse-grained features cannot comprehensively characterize entity attributes; conversely, relational structure can enhance semantic representation, but the incompleteness of edges in real-world graphs limits modeling when relying on a single information source. To address these issues, we propose MRCLQR (Multi-information Relation Constraint-based Logical Query Reasoning), a framework with three core innovations: (1) an Information Semantic Alignment module based on contrastive learning, which achieves cross-modal semantic collaboration via entity-type-structure pairing; (2) a Constraint-aware Relation Encoding method that decomposes relation semantics into domain aggregation features, relation ontology semantics, and range constraint features; and (3) Neural-Symbolic Operators guided by domain constraints, which narrow the reasoning space through a constraint-aware attention mechanism. Experiments on FB15k, FB15k-237, and NELL-995 demonstrate that MRCLQR achieves average MRR scores of 35.8%, 16.2%, and 19.6%, respectively improving over the strongest baselines by 0.5%, 0.2%, and 0.2% --- and exhibits an 8.0% average gain on complex queries involving negation. Ablation studies validate the effectiveness of multi-source collaboration and the curriculum learning strategy. This work offers a novel paradigm for heterogeneous knowledge fusion and logical query reasoning.
Eliminating Sentiment Bias in Recommender Systems by Counterfactual Inference
Authors: Le Pan, Yuanjiang Cao, Chengkai Huang, Wenjie Zhang, Lina Yao
Venue: CIKM
Year: 2025

Sentiment bias is newly discovered in Recommender Systems (RSs). Critical users and niche items are disadvantaged by such unfair recommendations. To mitigate this bias, we propose a novel approach by counterfactual inference, which is implemented in two stages. Experiment results validate that our model achieves comparable performance in rating prediction, providing better recommendations and effectively mitigating sentiment bias. To the best of our knowledge, this is the first work to employ counterfactual inference on sentiment bias mitigation in RSs.
Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models
Authors: Dayan Pan, Zhaoyang Fu, Jingyuan Wang, Xiao Han, Yue Zhu, Xiangyu Zhao
Venue: CIKM
Year: 2025

Large Language Models (LLMs) possess remarkable generalization capabilities but struggle with multi-task adaptation, particularly in balancing knowledge retention with task-specific specialization. Conventional fine-tuning methods suffer from catastrophic forgetting and substantial resource consumption, while existing parameter-efficient methods perform suboptimally in complex multi-task scenarios. To address this, we propose Contextual Attention Modulation (CAM), a novel mechanism that dynamically modulates the representations of self-attention modules in LLMs. CAM enhances task-specific features while preserving general knowledge, thereby facilitating more effective and efficient adaptation. For effective multi-task adaptation, CAM is integrated into our Hybrid Contextual Attention Modulation (HyCAM) framework, which combines a shared, full-parameter CAM module with multiple specialized, lightweight CAM modules, enhanced by a dynamic routing strategy for adaptive knowledge fusion. Extensive experiments on heterogeneous tasks, including question answering, code generation, and logical reasoning, demonstrate that our approach significantly outperforms existing approaches, achieving an average performance improvement of 3.65%. The implemented code and data are available to ease reproducibility.
Revisiting Long-Tailed Learning: Insights from an Architectural Perspective
Authors: Yuhan Pan, Yanan Sun, Wei Gong
Venue: CIKM
Year: 2025

Long-Tailed (LT) recognition has been widely studied to tackle the challenge of imbalanced data distributions in real-world applications. However, the design of neural architectures for LT settings has received limited attention, despite evidence showing that architecture choices can substantially affect performance. This paper aims to bridge the gap between LT challenges and neural network design by providing an in-depth analysis of how various architectures influence LT performance. Specifically, we systematically examine the effects of key network components on LT handling, such as topology, convolutions, and activation functions. Based on these observations, we propose two convolutional operations optimized for improved performance. Recognizing that operation interactions are also crucial to network effectiveness, we apply Neural Architecture Search (NAS) to facilitate efficient exploration. We propose LT-DARTS, a NAS method with a novel search space and search strategy specifically designed for LT data. Experimental results demonstrate that our approach consistently outperforms existing architectures across multiple LT datasets, achieving parameter-efficient, state-of-the-art results when integrated with current LT methods.
Numerical Linear Algebraic Foundations for Large-Scale Unsupervised Learning
Authors: Haesun Park
Venue: CIKM
Year: 2025

Numerical Linear Algebra provides essential foundations in many large-scale data analytic tasks. In this talk, it is illustrated that some of the powerful methods especially for unsupervised tasks such as clustering, topic modeling, community detection, embedding, and representation learning can be derived based on a framework of low rank approximation (LRA). These include the ubiquitous singular value decomposition (SVD), latent semantic indexing (LSI), principal component analysis (PCA), and the constrained LRA (CLRA)-based methods such as nonnegative matrix factorization (NMF) and its variants such as Symmetric NMF (SymNMF), and JointNMF. It is shown that all these methods can be explained using one framework which can then be further generalized into more advanced methods such as co-clustering and co-embedding for more complex situations including multi-view and multi-granularity data sets, and into semi-supervised methods incorporating prior knowledge. The presented algorithms that utilize advances in numerical linear algebra are shown to achieve scalability, efficiency, and effectiveness. Substantial experimental results on synthetic and real-life problems illustrate significant benefits of exploiting numerical linear algebra-based methods in many data analytic tasks.
Temporal Distance-aware Subgoal Generation for Offline Hierarchical Reinforcement Learning
Authors: Taegeon Park, Seungho Baek, Jongchan Park, Seungjun Oh, Yusung Kim
Venue: CIKM
Year: 2025

Efficient subgoal generation is essential in offline Hierarchical Re- inforcement Learning (HRL) for tackling long-horizon and sparse- reward tasks. Existing approaches often struggle with redundant and inefficient subgoal candidates and fail to maintain meaningful temporal relationships due to fixed-step subgoal sampling. To ad- dress these issues, we propose Temporal Distance-Aware Subgoal Generation (TDSG), a novel framework leveraging pre-trained Tem- poral Distance (TD) representations. TDSG identifies a compact set of anchor states in the TD representation space. These states, evenly spaced at consistent temporal distance intervals and collectively covering all states in the dataset while comprising less than 1% of the entire dataset, serve as the training targets for subgoal gener- ation. This ensures efficient and temporally consistent high-level policy learning. Furthermore, the low-level policy leverages intrin- sic rewards derived from the alignment between current states and subgoals in the TD representation space, enabling effective learning even under sparse-reward conditions. Experimental results demon- strate that TDSG achieves consistent performance improvement over prior offline HRL methods across numeric and visual environ- ments. Our code is available at https://github.com/Ptaegeon/TDSG.git
EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
Authors: Yujin Park, Haejun Chung, Ikbeom Jang
Venue: CIKM
Year: 2025

Pairwise comparison is often favored over absolute rating or ordinal classification in subjective or difficult annotation tasks due to its improved reliability. However, exhaustive comparisons require a massive number of annotations (O(n²)). Recent work[8] has reduced the annotation burden (O(n log n)) by actively sampling pairwise comparisons using a sorting algorithm. We further improve annotation efficiency by (1) roughly pre-ordering items using the CLIP (Contrastive Language-Image Pre-training) model hierarchically without training, and (2) replacing easy, obvious human comparisons with automated ones. The proposed EZ-Sort first produces a CLIP-based zero-shot pre-ordering, then initializes bucket-aware Elo scores, and finally runs an uncertainty-guided human-in-the-loop MergeSort. We validated our method using datasets from three domains: face-age estimation (FGNET)[10], historical image chronology (DHCI)[14], and retinal image quality assessment (EyePACS)[6]. EZ-Sort reduced human annotation cost by 90.5% compared to exhaustive pairwise comparisons and by 19.8% compared to prior work[8] (at n = 100), while improving or maintaining inter-rater reliability. These results demonstrate that combining CLIP-based priors with uncertainty-aware sampling yields an efficient and scalable solution for pairwise ranking.
AnDri: A System for Anomaly and Drift co-Detection
Authors: Jongjun Park, Ziqi Guo, Fei Chiang
Venue: CIKM
Year: 2025

The presence of concept drift poses challenges for anomaly detection in time series. While anomalies are caused by undesirable changes in the data, differentiating abnormal changes from varying normal behaviours is difficult due to differing frequencies of occurrence, varying time intervals when normal patterns occur, and identifying similarity thresholds to separate the boundary between normal vs. abnormal sequences. Differentiating between concept drift and anomalies is critical for accurate analysis as studies have shown that the compounding effects of error propagation in downstream data analysis tasks lead to lower detection accuracy and increased overhead due to unnecessary model updates. Unfortunately, existing work has largely explored anomaly detection and concept drift detection in isolation. We develop AnDri, a system for Anomaly detection in the presence of Drift, and enables users to interactively co-explore the interaction of anomalies and drift. Our system demonstration provides two motivating scenarios that extend existing anomaly detection baselines with partial labels towards improved co-detection accuracy, and highlights the superiority of AnDri over these baselines.
A Pivot-Enhanced Question Answering Framework: Using Iterative Sub-Question Decomposition and Answer-to-Question Verification
Authors: Seyeon Park, Beakcheol Jang
Venue: CIKM
Year: 2025

Question and Answering(QA) in low-resource languages remains a significant challenge due to the scarcity of high-quality training data. To address this, we propose a robust framework for low-resource QA. Our framework enhances performance through the integration of pivot-based translation, sub-question decomposition, and semantic consistency verification. Our proposed approach utilizes pivoting by translating questions into a high-resource language, and then translating the answer back into the original language. To improve the handling of complex queries, we introduce sub-question decomposition, which breaks down the original question into simpler sub-units for independent QA. Also, we incorporate a reverse QA mechanism that generates a new question from the predicted answer and measures its semantic similarity to the original question, thereby validating answer consistency. Evaluated on the TyDi QA benchmark, the proposed framework achieves a 19.21 chrF score and 0.67 BERTScore, corresponding to at least a 12% improvement in metrics over direct generation baselines.
MOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System
Authors: Jungbae Park, Heonseok Jang
Venue: CIKM
Year: 2025

E-commerce retrieval and ranking optimization have expanded to incorporate broader metrics that capture user engagement and business objectives. Modern search frameworks now incorporate advanced quality features, such as sales counts and document-query relevance, to better align search results with these goals. Traditional methods typically focus on click-through rate (CTR) as a measure of engagement or relevance, but this can miss true purchase intent, creating a gap between user interest and actual conversions. Joint training with the click-through conversion rate (CTCVR) has become essential for understanding buying behavior, although its sparsity poses challenges for reliable optimization. This study presents MOHPER, a Multi-Objective HyperParameter optimization framework for E-commerce Retrieval systems. Using Bayesian optimization and sampling, it jointly optimizes CTR, CTCVR, and other relevant objectives, with a focus on user engagement and conversion. To enhance configuration selection in multi-objective optimization, we propose advanced hyperparameter selection methods, including a meta-configuration voting strategy and a cumulative training approach that leverages prior optima to improve training efficiency and performance. Currently deployed in a live setting, our proposed framework substantiates its practical efficacy in achieving a balanced optimization that aligns with both user satisfaction and revenue goals.
ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
Authors: Heewon Park, Mugon Joe, Miru Kim, Minhae Kwon
Venue: CIKM
Year: 2025

In real-world applications, machine learning models face online label shift, where label distributions change over time. Effective adaptation requires careful learning rate selection: too low slows adaptation and too high causes instability. We propose ASAP (Adaptive Shift Aware Post-training), which dynamically adjusts the learning rate by computing the cosine distance between current and previous unlabeled outputs and mapping it within a bounded range. ASAP requires no labels, model ensembles, or past inputs, using only the previous softmax output for fast, lightweight adaptation. Experiments across multiple datasets and shift scenarios show ASAP consistently improves accuracy and efficiency, making it practical for unsupervised model adaptation.
Federated Gradient Boosting for Financial Fraud Detection: An Empirical Study in the Banking Sector
Authors: Dae-Young Park, In-Young Ko, Taek-Ho Lee, Junghye Lee
Venue: CIKM
Year: 2025

The development of effective fraud detection systems (FDS) is hindered by strict data privacy regulations that prevent centralized data sharing. Federated learning (FL) has emerged as a promising alternative, enabling collaborative model training without exposing sensitive data. While FL has been explored in the healthcare domain, research on its application to financial fraud detection remains relatively limited. Specifically, FL research on real-world banking fraud types-with detailed customer, account, and transaction data-remains underexplored. We present the first empirical study of federated gradient boosting models for financial fraud detection in the banking sector, motivated by their superior performance over deep learning models on tabular fraud data. We evaluate and compare four representative federated gradient boosting models using both a private multi-fraud banking dataset from the Financial Security Institute (FSI) and a publicly available banking dataset, under various scenarios. Key findings include the consistent superiority of FedXGBBagging (a federated gradient boosting model), general vulnerability to data quantity skew, performance instability under bank join/dropout, and limitations in detecting localized banking fraud types such as ATM skimming. The findings from our empirical study highlight challenges and design considerations for deploying FL-based FDSs in the banking sector.
FnRGNN: Distribution-aware Fairness in Graph Neural Network
Authors: SoYoung Park, Sungsu Lim
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) excel at learning from structured data, yet fairness in regression tasks remains underexplored. Existing approaches mainly target classification and representation-level debiasing, which cannot fully address the continuous nature of node-level regression. We propose FnRGNN, a fairness-aware in-processing framework for GNN-based node regression that applies interventions at three levels: (i) structure-level edge reweighting, (ii) representation-level alignment via MMD, and (iii) prediction-level normalization through Sinkhorn-based distribution matching. This multi-level strategy ensures robust fairness under complex graph topologies. Experiments on four real-world datasets demonstrate that FnRGNN reduces group disparities without sacrificing performance. Code is available at https://github.com/sybeam27/FnRGNN.
TwinBandit Prompt Optimizer: Adaptive Prompt Optimization via Synergistic Dual MAB-Guided Feedback
Authors: Young-Joon Park, Seong-Ryeong Lee, Anh-Dung Vo, Minsung Jung, Daewoo Choi
Venue: CIKM
Year: 2025

A common deficiency in Automatic Prompt Engineering (APE) is the failure to strategically employ specific failure feedback in concert with the adaptive and coordinated selection of diverse generation strategies. To address this deficiency, we introduce TwinBandit Prompt Optimizer (TBPO), an APE framework that employs a synergistic dual Multi-Armed Bandit (MAB) mechanism for adaptive prompt generation, applicable to black-box Large Language Models (LLMs). The first MAB identifies the most challenging training instances, informing an LLM-driven feedback pipeline responsible for generating (1) parameterized changes to guide prompt evolution and (2) n-shot example configurations. A second MAB then adaptively selects and ranks the proposed changes based on the empirical performance of previously generated prompts. TBPO then combines these ranked modifications and n-shot configurations to generate child prompts with targeted enhancements that specifically address these challenging instances. Iteratively applying this process to the best-performing prompts, TBPO forms a closed-loop cycle, strategically generating and exploring a tree of enhanced prompts. Benchmarking shows that TBPO achieves stronger performance compared to state-of-the-art APE baselines, highlighted by a 3.87% higher exact match rate on the GPQA-Diamond dataset. Our approach offers a more targeted and adaptive APE method by strategically learning from common failures and leveraging empirically validated generation strategies for a given dataset. More information is available at https://github.com/yjpark-pub/tbpo_release.
SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation
Authors: Minje Park, Jeonghwa Lim, Taehyung Yu, Sunghoon Joo
Venue: CIKM
Year: 2025

Electrocardiogram (ECG) delineation, the segmentation of meaningful waveform features, is critical for clinical diagnosis. Despite recent advances using deep learning, progress has been limited by the scarcity of publicly available annotated datasets. Semi-supervised learning presents a promising solution by leveraging abundant unlabeled ECG data. In this study, we present SemiSegECG, the first systematic benchmark for semi-supervised semantic segmentation (SemiSeg) in ECG delineation. We curated and unified multiple public datasets, including previously underused sources, to support robust and diverse evaluation. We adopted five representative SemiSeg algorithms from computer vision, implemented them on two different architectures: the convolutional network and the transformer, and evaluated them in two different settings: in-domain and cross-domain. Additionally, we propose ECG-specific training configurations and augmentation strategies and introduce a standardized evaluation framework. Our results show that the transformer outperforms the convolutional network in semi-supervised ECG delineation. We anticipate that SemiSegECG will serve as a foundation for advancing semi-supervised ECG delineation methods and will facilitate further research in this domain. The code repository is available at https://github.com/bakqui/semi-seg-ecg.
Beyond Masking: Landmark-based Representation Learning and Knowledge-Distillation for Audio-Visual Deepfake Detection
Authors: Chan Park, Muhammad Shahid Muneer, Simon S. Woo
Venue: CIKM
Year: 2025

Audio-visual deepfake detection methods demonstrate strong performance on academic datasets but fail significantly when applied to real-world. To address the shortcomings of previous approaches, we utilize landmarks dynamic information. First, we propose Landmark-based Distillation (LBD), motivated by I-JEPA's representation learning approach. LBD utilizes KL-divergence to align facial landmark predictions from visual and audio encoders, enforcing focus on geometric facial features rather than spurious background information. Second, we introduce Multimodal Temporal Information Alignment (MTIA), which employs contrastive learning to enhance temporal consistency between audio and visual representations. We conduct experiments on academic datasets and web-based deepfakes collected from diverse social media platforms, serving as real-world examples. Our proposed landmark-guided distillation framework achieves computational efficiency while improving multimodal video deepfake detection performance across a diverse range of deepfakes compared to existing methods. The code is available at https://github.com/Ckck12/Beyond-Masking.
Investigating the Usage and Evaluation of Quantum Computing Technologies for Information Access
Authors: Andrea Pasin
Venue: CIKM
Year: 2025

Quantum Computing (QC) is an emerging research field that is attracting significant interest from the scientific community. In fact, it is believed that quantum computers can be employed to solve complex computational problems more efficiently than traditional computers, due to their inherent capabilities of exploring large search spaces very efficiently by leveraging the principles of quantum mechanics, such as superposition, entanglement, and tunnelling. However, quantum computers are still in their early stages of development and their applications are very limited, especially in the field of Information Access (IA). Nevertheless, IA systems often face complex optimization problems that might be solved more efficiently through the usage of quantum computers. This paper outlines the author's PhD objectives in designing new methodologies for the application and evaluation of QC technologies for IA problems. Furthermore, this work provides an overview of the achieved preliminary results and a discussion of possible future research directions.