OpenRaiser/Intern-Atlas huggingface dataset:
Unplug and Play Language Models: Decomposing Experts in Language Models at Inference Time
Authors: Nakyeong Yang, Jiwon Moon, Junseok Kim, Yunah Jang, Kyomin Jung
Venue: CIKM
Year: 2025

Enabled by large-scale text corpora with huge parameters, pre-trained language models operate as multi-task experts using a single model architecture. However, recent studies have revealed that certain neurons play disproportionately important roles in solving specific tasks, suggesting that task-relevant substructures can be isolated and selectively activated for each task. Therefore, we introduce Decomposition of Experts (DoE), a novel framework that dynamically identifies and activates task-specific experts within a language model to reduce inference cost without sacrificing accuracy. We first define a task expert as a set of parameters that significantly influence the performance of a specific task and propose a four-step unplug-and-play process: (1) receiving a user request, (2) identifying the corresponding task expert, (3) performing inference using the expert-localized model, and (4) restoring the original model and waiting for the next task. Using attribution methods and prompt tuning, DoE isolates task-relevant neurons, minimizing computational overhead while maintaining task performance. We assume a setting where a language model receives user requests from five widely used natural language understanding benchmarks, processing one task at a time. In this setup, we demonstrate that DoE achieves up to a x1.73 inference speed-up with a 65% pruning rate, without compromising accuracy. Comparisons with various task expert localization methods reveal that DoE effectively identifies task experts, while ablation studies validate the importance of its components. Additionally, we analyze the effects of batch size, token count, and layer types on inference speed-up, providing practical insights for adopting DoE. The proposed framework is both practical and scalable, applicable to any transformer-based architecture, offering a robust solution for efficient task-specific inference.
CheckDAPR: An MLLM-based Sketch Analysis System for Draw-A-Person-in-the-Rain Assessments
Authors: Migyeong Yang, Chaehee Park, Taeeun Kim, Hayeon Song, Jinyoung Han
Venue: CIKM
Year: 2025

Sketch-based drawing assessments in art therapy are commonly used to understand the cognitive and psychological states of individuals. In conjunction with self-report measures, drawing assessments serve to enhance insights into an individual's psychological state. However, interpreting the drawing assessments is labor-intensive and substantially reliant on the experience of the art therapists. While a few automated approaches for analyzing drawing-based assessments have been proposed to remedy this issue, they mostly rely on existing object detection methods, where complex drawing attributes cannot be accurately decoded. To overcome these challenges, we propose a novel and comprehensive Draw-A-Person-in-the-Rain (DAPR) analysis system, CheckDAPR, which utilizes a Multimodal Large Language Model (MLLM) with object detection methods for in-depth evaluation. Our experimental results show the promising performance of CheckDAPR and its ability to reduce analysis time for art therapists, indicating its potential to aid professionals in art therapy.
Measuring Uncertainty in Medical Image Diagnosis via Conformal Focal Loss
Authors: Ao Yang, Xiaodong Yue, Yufei Chen
Venue: CIKM
Year: 2025

Medical image diagnosis inherently involves uncertainty due to artifacts, occlusions, and ambiguous visual patterns, often leading to high inter-observer variability. While deep neural networks offer strong predictive performance, their outputs tend to be overconfident and poorly calibrated, limiting their clinical reliability. We propose Conformal Focal Loss (CFL), a principled approach that leverages the focal loss and the statistical validity of conformal prediction to better characterize diagnostic uncertainty. By emphasizing hard or ambiguous examples, CFL enables more accurate estimation of both predictive confidence and ambiguity. We evaluate CFL on diagnostic tasks using both clean and noise-augmented datasets, demonstrating its ability to effectively identify uncertain cases while maintaining robust classification performance under label noise.
HIT Model: A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems
Authors: Haoqiang Yang, Congde Yuan, Kun Bai, Mengzhuo Guo, Wei Yang, Chao Zhou
Venue: CIKM
Year: 2025

Online display advertising platforms rely on pre-ranking systems to efficiently filter and prioritize candidate ads from large corpora, balancing relevance to users with strict computational constraints. The prevailing two-tower architecture, though highly efficient due to its decoupled design and pre-caching, suffers from cross-domain interaction and coarse similarity metrics, undermining its capacity to model complex user-ad relationships. In this study, we propose the Hierarchical Interaction-Enhanced Two-Tower (HIT) model, a new architecture that augments the two-tower paradigm with two key components: generators that pre-generate holistic vectors incorporating coarse-grained user-ad interactions through a dual-generator framework with a cosine-similarity-based generation loss as the training objective, and multi-head representers that project embeddings into multiple latent subspaces to capture fine-grained, multi-faceted user interests and multi-dimensional ad attributes. This design enhances modeling effectiveness without compromising inference efficiency. Extensive experiments on public datasets and large-scale online A/B testing on Tencent's advertising platform demonstrate that HIT significantly outperforms several baselines in relevance metrics, yielding a 1.66% increase in Gross Merchandise Volume and a 1.55% improvement in Return on Investment, alongside similar serving latency to the vanilla two-tower models. The HIT model has been successfully deployed in Tencent's online display advertising system, serving billions of impressions daily. The code is available at https://github.com/HarveyYang123/HIT_model.
GFlowNet with Gradient-based Optimization for Bayesian Network Structure Learning
Authors: Zhu Yang, Kun Yue, Zhiwei Qi, Liang Duan, Jianyu Li
Venue: CIKM
Year: 2025

Bayesian network (BN) structure learning on the discrete observations is crucial for representing uncertainty in data. However, existing single-structure learning methods are commonly trapped in local optimum or yielding structures that may lead to poorly calibrated predictions. Although posterior approximation methods can quantify the epistemic uncertainty over the learned BN structures, they cannot reliably identify the optimal BN structures. To tackle these issues, we propose a GFlowNet with Gradient-based Optimization (GFlowOpt) for the BN structure learning method. Initially, we employ Bayesian Information Criterion (BIC) scores as the rewards of BN structures, and adopt a contrastive learning-based training technique on GFlowNet to efficiently generate much better DAG representations. Subsequently, we train a proxy model on the continuous DAG representations by adopting the gradient-ascent-based optimization method to search more high proxy-scoring discrete candidate DAGs. Finally, we adopt Hill Climbing (HC) on these candidate DAGs to search high-scoring DAGs as the ultimate BN structures. Extensive experiments conducted on benchmark datasets demonstrate that the superiority of our proposed method compared with other state-of-the-art methods.
Advanced News Event Clustering via Topic Enhanced Modeling with Multi-Aspect Contrastive Learning
Authors: Hang Yang, Xiaoyan Yu, Dianbo Sui
Venue: CIKM
Year: 2025

News event clustering, a crucial task for discovering and comprehending real-world information, aims to aggregate news articles into fine-grained clusters based on specific key events. As the presence of topic-unrelated documents within clusters and redundant information within individual documents, it is challenging to learn a discriminative document representation. To address this issue, we introduce a novel method, TECL (Topic Enhanced modeling with Contrastive Learning), that leverages topic enhanced modeling with multi-aspect contrastive learning for news event clustering. The topic-enhanced modeling employs neural topic models to incorporate global and local semantics into document representations, while contrastive learning refines these representations from both inter-document and intra-document perspectives.Experiments conducted in both unsupervised and supervised scenarios indicate that the proposed method significantly improves performance, demonstrating its effectiveness.
USE-LTV: Customer LifeTime Value Prediction via Uncertain Sequence Modeling in Baidu Ads
Authors: Lei Yang, Jiahui Zhang, Guoyu Liu, Houzhi Wang, Zhiyuan Zhou, Xiaohui Zhao
Venue: CIKM
Year: 2025

Accurate customer LifeTime Value (LTV) predictions are of critical importance for evaluating the efficiency of customer management strategies, which could enhance advertising placement for better decision-making in ad systems. However, existing solutions for LTV prediction usually rely on determined historical sequence data, which are challenging to apply in Baidu ads due to two unique features including (i) uncertain behavior sequence of customers caused by dynamic advertising strategies, (ii) complex and long-tail distribution of LTV caused by continuous customer behaviors and unique business pattern of search and news feed ads in Baidu. To incorporate these new factors, we propose an Uncertain behavior Sequence modeling framework to predict customer LifeTime Value (USE-LTV), where we (i) utilize a transformer module to extract uncertain sequence features, and develop a dynamic weight mechanism to capture differentiated information under uncertain behavior sequence, (ii) design an continuous loss function tailored to the real-world long-tail exponential LTV distribution in Baidu ads. We extensively evaluate our method based on the industrial-scale real-world data from Baidu, one of the world's largest ads platform, demonstrate that USE-LTV achieves 11.64% of NMAE improvement for a-year LTV compared to the state-of-the-art method.
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
Authors: Lishan Yang, Wei Emma Zhang, Quan Z. Sheng, Lina Yao, Weitong Chen, Ali Shakeri
Venue: CIKM
Year: 2025

In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources further enhances its potential. Multimodal Federated Learning (MFL) is a distributed approach that enhances the efficiency and quality of multimodal learning, ensuring collaborative work and privacy protection. However, missing modalities pose a significant challenge in MFL, often due to data quality issues or privacy policies across the clients. In this work, we present MMiC, a framework for Mitigating Modality incompleteness in MFL within the Clusters. MMiC replaces partial parameters within client models inside clusters to mitigate the impact of missing modalities. Furthermore, it leverages the Banzhaf Power Index to optimize client selection under these conditions. Finally, MMiC employs an innovative approach to dynamically control global aggregation by utilizing Markowitz Portfolio Optimization. Extensive experiments demonstrate that MMiC consistently outperforms existing federated learning architectures in both global and personalized performance on multimodal datasets with missing modalities, confirming the effectiveness of our proposed solution. Our code is available at https://github.com/gotobcn8/MMiC.
StreamingRT: Stream KNN Join with Ray Tracing Core
Authors: Shixi Yang, Kai Zhang, Zhigang Zhao, Chunxiao Wang, Zhenying He, Yinan Jing, X. Sean Wang
Venue: CIKM
Year: 2025

Efficient processing of k-nearest neighbor (kNN) join operations on streaming data is critical for applications in location-aware services, recommendation systems, and spatial analytics. To serve users in real time, these applications generally require a high-performance kNN join on continuously changing streaming data. This paper introduces StreamingRT, a framework that leverages ray tracing (RT) cores in GPUs to accelerate stream kNN joins in 3D space. By modeling stream data into large primitives and transferring queries into short rays, StreamingRT transforms the kNN join problem into an efficient ray tracing task. To address the ray tracing index updating overhead on stream data, we propose two key techniques, i.e., boundary-extended point partitioning and query-driven BVH lazy updating. Moreover, we also adopt multi-BVH coprocessing and CPU-GPU pipelining to improve performance. These techniques enable efficient stream kNN join on ray tracing cores, delivering unprecedented performance improvement. Experimental evaluations show that StreamingRT can achieve up to 2.2× and 5.8× speedup over the state-of-the-art approach on RT cores and CUDA cores, respectively.
RepMedGAN: Self-supervised Representation-guided Medical GAN for Label-free Medical Image Synthesis
Authors: Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
Venue: CIKM
Year: 2025

Medical image synthesis addresses healthcare data scarcity by generating realistic samples for clinical support systems, AI training, and research. However, the field faces challenges due to the complexity of imaging data with its diverse modalities, characteristics, and disease variations. To produce high-quality images, medical image synthesis typically relies on conditional generation, where labels and annotations serve as essential conditions that provide critical guidance signals during the generation process to control desired semantics and fidelity. However, in the medical domain, labels are often inaccessible due to the high cost of annotation, requirements for clinical expertise, as well as ethical concerns. To address this critical challenge, we propose RepMedGAN, a novel self-supervised representation-guided image generation framework that enhances label-free medical image synthesis by leveraging self-supervised learning representations, enabling high-quality generation across different modalities without requiring labels or annotations. Our framework incorporates a Self-supervised Guidance Module that provides rich semantic knowledge during training and introduces a Guidance Representation Generator to bridge the train-inference disparity. Through extensive evaluation across four diverse medical datasets including brain MRI, chest X-ray, kidney CT, and eye glaucoma images, we demonstrate that RepMedGAN consistently achieves state-of-the-art results across multiple metrics and produces superior-quality medical images.
Rethinking Masked Image Modeling for Ultrasound Image Denoising
Authors: Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
Venue: CIKM
Year: 2025

Ultrasound imaging serves as an important clinical diagnostic modality due to its non-invasive, radiation-free, and real-time capabilities. However, ultrasound images suffer from speckle noise that significantly compromises diagnostic accuracy and clinical interpretation. Traditional denoising methods are limited by speckle noise's signal-dependent nature, often removing important diagnostic features. While deep learning performs better, it requires large labelled datasets that are difficult to obtain due to privacy concerns and annotation costs. Self-supervised learning through masked image modeling (MIM) shows potential in addressing data scarcity, but conventional MIM, developed for high-level vision tasks, is unsuitable for low-level tasks like image denoising due to its framework architecture and learning strategy. To this end, we propose Image Denoising Masked Image Modeling (ID-MIM), the first MIM framework for ultrasound image denoising. ID-MIM incorporates a novel high-frequency oriented dual-branch masking and a specialized learning objective for noise reduction. Our encoder-only architecture features a multi-scale hierarchical transformer with dynamic skip connections, where the encoder directly performs denoising rather than relying on separate decoder reconstruction as in conventional MIM approaches. Extensive experiments demonstrate the superior performance of our ID-MIM framework across diverse noise scenarios, establishing new state-of-the-art results.
Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
Authors: Tiandi Ye, Wenyan Liu, Kai Yao, Lichun Li, Shangchao Su, Cen Chen, Xiang Li, Shan Yin, Ming Gao
Venue: CIKM
Year: 2025

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw data. Personalized federated learning (pFL) has gained increasing attention for its ability to address data heterogeneity. However, most existing pFL methods assume that each client's data follows a single distribution and learn one client-level personalized model for each client. This assumption often fails in practice, where a single client may possess data from multiple sources or domains, resulting in significant intra-client heterogeneity and suboptimal performance. To tackle this challenge, we propose pFedBayesPT, a fine-grained instance-wise pFL framework based on visual prompt tuning. Specifically, we formulate instance-wise prompt generation from a Bayesian perspective and model the prompt posterior as an implicit distribution to capture diverse visual semantics. We derive a variational training objective under the semi-implicit variational inference framework. Extensive experiments on benchmark datasets demonstrate that pFedBayesPT consistently outperforms existing pFL methods under both feature and label heterogeneity settings.
SG-Filter: Enhancing Similar Text Retrieval via Hierarchical Summarized-Semantic Index and Adaptive Filtering
Authors: Jiancai Ye, Jun Liu, Haoyu Zhang, Maojia Sheng, Tao Yang, Jiaming Xu, Jinhao Li, Yu Wang, Guohao Dai
Venue: CIKM
Year: 2025

Similar Text Retrieval (STR) is an essential scenario in the field of information retrieval (IR). Unfortunately, existing mainstream vector-based retrieval methods cannot meet the recall rate requirements in STR scenarios (with a recall rate of less than 72%). This is because existing works have solely focused on the local information of text segments, that is, the text segments themselves ( i.e., semantic information ) and the relationships between them ( i.e., structured information ). Our key insight is that utilizing the global information of text segments ( i.e., summarized information ~. It includes the key expression of the documents to which the text segments belong and the relationship between documents. ) is crucial for improving the recall rate in STR, because the distinction of summarized information helps to filter out confusing vectors during retrieval. However, existing methods using summarized info still have a critical challenge. Their vectorization-based approaches fail to effectively model the global relationship in the summarized information, resulting in a further 79% deterioration in recall rate. To address this challenges, we present the SG-Filter, a novel retrieval framework that integrates summarized information by designing the hierarchical summarized-semantic index and the adaptive filtering strategy applied on it. (1) We propose a hierarchical summarized-semantic index by designing a summarized graph to model the summarized information. Specifically, we exploit the global information at both document and text segment levels through co-occurrence relationships and semantic associations. (2) We propose an adaptive filtering strategy that automatically determines which summarized words to filter per retrieval for effectively utilization of summarized information. (3) To ensure robustness and low retrieval latency, we propose a multi-path merge recall strategy to obtain summarized and semantic information at varying proportions, and develop an efficient vector retrieval method with filtering conditions. Experiments show that SG-Filter significantly increases recall rate by 10.53% ~ 22.92% on average compared with existing vector-based retrieval methods in STR. SG-Filter also ensures retrieval latency remains within tens of milliseconds. The code is open-sourced in https://github.com/strong-leaf/SG-Filter
Can LLMs Really Help Query Understanding In Web Search? A Practical Perspective
Authors: Dezhi Ye, Ye Qin, Bowen Tian, Jiabin Fan, Jie Liu, Haijin Liang, Jin Ma
Venue: CIKM
Year: 2025

As a core module of web search, query understanding aims to bridge the semantic gap between user queries and web page documents, thereby enhancing the ability to deliver more relevant results. Recently, Large Language Models (LLMs) have achieved significant breakthroughs that have fundamentally altered the workflow of existing search ranking tasks. However, few researchers have explored the integration of LLMs into the field of query understanding. In this paper, we investigate the potential of LLMs in query understanding by conducting a comprehensive evaluation across three dimensions: term, structure, and topic. This evaluation includes several representative tasks such as segmentation, term weighting, error correction, query expansion, and intent recognition. The experimental results reveal that LLMs are particularly effective in query expansion and intent recognition but show limited improvement in other areas. This limitation may be attributed to LLMs' primary focus on modeling the semantic knowledge of entire queries, while lacking the capability to capture token-level information with finer granularity. Additionally, we explore potential practical applications of LLMs in query understanding, such as integrating the evaluation and training capabilities of smaller models with LLMs and constructing unsupervised samples. Based on comprehensive empirical results, collaborative training emerges as a promising approach to leverage LLMs for query understanding. We hope this research will advance the practical application of LLMs in query understanding and contribute to the development of this field.
DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
Authors: Wencai Ye, Mingjie Sun, Shaoyun Shi, Peng Wang, Wenjin Wu, Peng Jiang
Venue: CIKM
Year: 2025

Semantic IDs are discrete identifiers generated by quantizing the Multi-modal Large Language Models embeddings, enabling efficient multi-modal content integration in recommendation systems. However, their lack of collaborative signals results in a misalignment with downstream discriminative and generative recommendation objectives. Recent studies have introduced various alignment mechanisms to address this problem, but their two-stage framework design still leads to two main limitations: (1) inevitable information loss during alignment, and (2) inflexibility in applying adaptive alignment strategies, consequently constraining the mutual information maximization during the alignment process. To address these limitations, we propose a novel and flexible one-stage Dual-Aligned Semantic IDs (DAS) method that simultaneously optimizes quantization and alignment, preserving semantic integrity and alignment quality while avoiding the information loss typically associated with two-stage methods. Meanwhile, DAS achieves more efficient alignment between the semantic IDs and collaborative signals, with the following two innovative and effective approaches: (1) Multi-view Constrative Alignment: To maximize mutual information between semantic IDs and collaborative signals, we first incorporate an ID-based CF debias module, and then design three effective contrastive alignment methods: dual user-to-item (u2i), dual item-to-item/user-to-user (i2i/u2u), and dual co-occurrence item-to-item/user-to-user (i2i/u2u). (2) Dual Learning: By aligning the dual quantizations of users and ads, the constructed semantic IDs for users and ads achieve stronger alignment. Finally, offline experiments and online A/B tests confirm DAS's efficacy, now serving 400M+ users in Kuaishou's ad platform.