OpenRaiser/Intern-Atlas huggingface dataset:
SUMMA: A Multimodal Large Language Model for Advertisement Summarization
Authors: Weitao Jia, Shuo Yin, Zhoufutu Wen, Han Wang, Zehui Dai, Kun Zhang, Zhenyu Li, Tao Zeng, Xiaohui Lv
Venue: CIKM
Year: 2025

Understanding multimodal video ads is crucial for improving query-ad matching and relevance ranking on short video platforms, enhancing advertising effectiveness and user experience. However, the effective utilization of multimodal information with high commercial value still largely constrained by reliance on highly compressed video embeddings-has long been inadequate. To address this, we propose SUMMA (the abbreviation of SUmmarizing MultiModalAds), a multimodal model that automatically processes video ads into summaries highlighting the content of highest commercial value, thus improving their comprehension and ranking in Douyin search-advertising systems. SUMMA is developed via a two-stage training strategy-multimodal supervised fine-tuning followed by reinforcement learning with a mixed reward mechanism-on domain-specific data containing video frames and ASR/OCR transcripts, generating commercially valuable and explainable summaries. We integrate SUMMA-generated summaries into our production pipeline, directly enhancing the candidate retrieval and relevance ranking stages in real search-advertising systems. Both offline and online experiments show substantial improvements over baselines, with online results indicating a statistically significant 1.5% increase in advertising revenue. Our work establishes a novel paradigm for condensing multimodal information into representative texts, effectively aligning visual ad content with user query intent in retrieval and recommendation scenarios.
Chart-CoCa: Self-Improving Chart Understanding of Vision LMs via Code-Driven Synthesis and Candidate-Conditioned Answering
Authors: Gongyao Jiang, Qiong Luo
Venue: CIKM
Year: 2025

Vision Language Models (VLMs) often struggle with chart understanding tasks, particularly in accurate chart description and complex reasoning. Synthetic data generation is a promising solution, while usually facing the challenge of noise labels. To address this challenge, we first introduce a chart synthesis pipeline that generates aligned chart-question-answer triplets through code generation and execution, ensuring the reliability of synthetic data without human intervention. Furthermore, inspired by test-time scaling that increases inference budget and thereby improves performance, we design a candidate-conditioned answering process. The VLM first generates multiple responses per query, and then synthesizes the final answer by contextualizing these candidates. Experiments demonstrate significant improvements, with up to 15.50 points accuracy gain over the initial VLM, in a fully self-improving paradigm without either human-labeled data or external models.
An LLM-based Behavior Modeling Framework for Malicious User Detection
Authors: Meng Jiang, Wenjie Wang, Chongming Gao, Shaofeng Hu, Kaishen Ou, Hui Lin, Fuli Feng
Venue: CIKM
Year: 2025

Malicious users pose significant threats to social platforms. Extensive efforts have leveraged user behavior sequences to model relationships between various actions and capture behavioral patterns for malicious user detection; however, they rely on behavior IDs, ignoring valuable behavior content such as self-introductions in friend requests, which offer crucial clues for detecting malicious user. We thus propose leveraging Large Language Models (LLMs) to jointly model IDs and content in user behavior sequences. The key to effective malicious user detection is to infer malicious user behavior patterns. However, inferring these patterns from labeled behavior sequences suffers from poor data efficiency and limited generalization, resulting in suboptimal malicious user detection performance. To overcome the limitations, we propose leveraging the malicious user specifications (e.g., definitions and common deceptive tactics) from the existing expert handbook. These specifications guide LLMs in reasoning over user behavior IDs and content before making predictions. To this end, we introduce an LLM-based behavior modeling framework with an expert handbook to enhance LLMs' behavior reasoning. We first distill the user's behaviors into a concise summary, guided by malicious user specifications in the expert handbook, and then feed the summary and users' demographic features into LLMs for comprehensive reasoning and detection. We conduct extensive online and offline experiments on the Weixin platform, validating the superiority of the proposed framework over the original Weixin detection baseline, achieving, for example, a 5.34% improvement in F1-Score.
Balance and Brighten: A Twin-Propeller Network to Release Potential of Physics Laws for Traffic State Estimation
Authors: Weihao Jiang, Yao Fu, Hong Zhao, Xiaoyu Cai, Ruiheng Yang, Linsen Li, Jiang Zhu
Venue: CIKM
Year: 2025

Traditional physics-informed deep learning combines the data-driven methods with the model-based methods by incorporating physics loss as a constraint in total loss function, which aims to enforce the neural network to behave according to the physics laws. However, the potential of physical knowledge is severely underestimated by this approach. Firstly, the physical knowledge fails to demonstrate its intended effects since the physics loss could have extremely small magnitude, more fluctuating convergence rates, and conflicting directions of the gradients compared to the data loss. Secondly, existing methods implicitly employ physics laws as auxiliary terms, which ignores that explicitly utilizing certain properties of physics laws can compensate for the shortcomings of data-driven models, particularly with regard to the data noise and relationships between variables. To alleviate these issues, we propose a Twin-Propeller Network (TPN) to realize fully message exchange among physical knowledge and data information, that releases the potential of the physics laws. Practically, we independently train data-driven model and physics-based model as two student models to get the information separated. Considering the measurement noise present in the data-driven model and the relatively robust physics-based model, we quantify the data uncertainty and utilize it as a weight to balance the two students in a integrated robust teacher model. The stronger teacher in turn transfers the respective knowledge to another student, where we innovatively propose traffic state relation distillation and physical knowledge distillation to guide the training of the data student and the physics student respectively. Through extensive experiments on both synthetic and real-world datasets, our model demonstrates better performance than the existing state-of-the-art methods.
Exploring the Tradeoff Between Diversity and Discrimination for Continuous Category Discovery
Authors: Ruobing Jiang, Yang Liu, Haobing Liu, Yanwei Yu, Chunyang Wang
Venue: CIKM
Year: 2025

Continuous category discovery (CCD) aims to automatically discover novel categories in continuously arriving unlabeled data. This is a challenging problem considering that there is no number of categories and labels in the newly arrived data, while also needing to mitigate catastrophic forgetting. Most CCD methods cannot handle the contradiction between novel class discovery and classification well. They are also prone to accumulate errors in the process of gradually discovering novel classes. Moreover, most of them use knowledge distillation and data replay to prevent forgetting, occupying more storage space. To address these limitations, we propose Independence-based Diversity and Orthogonality-based Discrimination (IDOD). IDOD mainly includes independent enrichment of diversity module, joint discovery of novelty module, and continuous increment by orthogonality module. In independent enrichment, the backbone is trained separately using contrastive loss to avoid it focusing only on features for classification. Joint discovery transforms multi-stage novel class discovery into single-stage, reducing error accumulation impact. Continuous increment by orthogonality module generates mutually orthogonal prototypes for classification and prevents forgetting with lower space overhead via representative representation replay. Experimental results show that on challenging fine-grained datasets, our method outperforms the state-of-the-art methods.
Hierarchy-Consistent Learning and Adaptive Loss Balancing for Hierarchical Multi-Label Classification
Authors: Ruobing Jiang, Mengzhe Liu, Haobing Liu, Yanwei Yu
Venue: CIKM
Year: 2025

Hierarchical Multi-Label Classification (HMC) faces critical challenges in maintaining structural consistency and balancing loss weighting in Multi-Task Learning (MTL). In order to address these issues, we propose a classifier called HCAL based on MTL integrated with prototype contrastive learning and adaptive task-weighting mechanisms. The most significant advantage of our classifier is semantic consistency including both prototype with explicitly modeling label and feature aggregation from child classes to parent classes. The other important advantage is an adaptive loss-weighting mechanism that dynamically allocates optimization resources by monitoring task-specific convergence rates. It effectively resolves the ''one-strong-many-weak'' optimization bias inherent in traditional MTL approaches. To further enhance robustness, a prototype perturbation mechanism is formulated by injecting controlled noise into prototype to expand decision boundaries. Additionally, we formalize a quantitative metric called Hierarchical Violation Rate (HVR) as to evaluate hierarchical consistency and generalization. Extensive experiments across three datasets demonstrate both the higher classification accuracy and reduced hierarchical violation rate of the proposed classifier over baseline models.
Community-Aware Social Community Recommendation
Authors: Runhao Jiang, Renchi Yang, Wenqing Lin
Venue: CIKM
Year: 2025

Social recommendation, which seeks to leverage social ties among users to alleviate the sparsity issue of user-item interactions, has emerged as a popular technique for elevating personalized services in recommender systems. Despite being effective, existing social recommendation models are mainly devised for recommending regular items such as blogs, images, and products, and largely fail for community recommendations due to overlooking the unique characteristics of communities. Distinctly, communities are constituted by individuals, who present high dynamicity and relate to rich structural patterns in social networks. To our knowledge, limited research has been devoted to comprehensively exploiting this information for recommending communities. To bridge this gap, this paper presents CASO, a novel and effective model specially designed for social community recommendation. Under the hood, CASO harnesses three carefully-crafted encoders for user embedding, wherein two of them extract community-related global and local structures from the social network via social modularity maximization and social closeness aggregation, while the third one captures user preferences using collaborative filtering with observed user-community affiliations. To further eliminate feature redundancy therein, we introduce a mutual exclusion between social and collaborative signals. Finally, CASO includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities. Our extensive experiments evaluating CASO against nine strong baselines on six real-world social networks demonstrate its consistent and remarkable superiority over the state of the art in terms of community recommendation performance.
Point-DMAE: Point Cloud Self-supervised Learning via Density-directed Masked Autoencoders
Authors: Xianglong Jin, Zheng Wang, Wenjie Zheng, Feiping Nie
Venue: CIKM
Year: 2025

Masked autoencoders have been extensively utilized in 3D point cloud self-supervised learning, where the fundamental approach involves masking a portion of the point cloud and subsequently reconstructing it. This process is hypothesized to enhance model learning by leveraging the inherent structure of the point cloud data. However, the information density within point clouds is inherently uneven, contrasting with the more uniform distributions found in language and 2D image data. This uneven distribution suggests that the application of random masking strategies, commonly adopted from NLP and 2D vision, may not be optimal for point cloud data, potentially leading to suboptimal learning outcomes. Based on this observation, we propose a simple yet effective Density-directed Masked Autoencoders for Point Cloud Self-supervised Learning (Point-DMAE), which learns latent semantic point cloud features using a density-directed masking strategy. Specifically, our method employs a dual-branch Transformer architecture to extract both high-level and fine-grained point features through global and local block density-directed masking, respectively. Point-DMAE demonstrates high pre-training efficiency and significantly outperforms our baseline (Point-MAE) on 3D object classification tasks within the ScanObjectNN dataset by 4.13% on OBJ-BG, 5.17% on OBJ-ONLY, and 4.17% on PB-T50-RS. Codes are available at https://github.com/jinxianglong10/Point-DMAE.
Dangerous Language Habits! Exploiting Code-Mixing for Backdoor Attacks on NLP Models
Authors: Haotian Jin, Haihui Fan, Jinchao Zhang, Yang Li, Bo Li, Junhao Zhou
Venue: CIKM
Year: 2025

Backdoor attacks threaten the reliability of NLP models by embedding hidden behaviors during training, which are activated by specific inputs at inference time. Traditional backdoor triggers often rely on explicit content alterations-such as token insertion or stylistic modification-which may compromise semantic coherence and be easily detected.In this work, we propose a novel backdoor attack strategy that leverages the linguistic properties of code-mixing(a language form that combines elements from two or more languages) as implicit triggers. Drawing inspiration from natural code-mixing communication, we design three types of linguistically grounded triggers: inter-word mixing , intra-sentential mixing , and inter-sentential mixing. These forms reflect realistic language usage patterns in bilingual communities, enhancing the stealthiness of the attack. The experiment results show that existing NLP models perform poorly when faced with backdoor attacks based on code-mixing triggers. We are the first to focus on code-mixing as a trigger for text backdoor attacks. We hope this research raises awareness of the vulnerability of models during training when faced with code-mixing.
M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction
Authors: Guangyin Jin, Sicong Lai, Xiaoshuai Hao, Jinlei Zhang, Mingtao Zhang
Venue: CIKM
Year: 2025

Achieving accurate traffic prediction is a fundamental but crucial task in the development of current intelligent transportation systems. These limitations pose significant challenges for the efficient deployment and operation of deep learning models on large-scale datasets. To address these challenges, we propose a cost-effective graph-free Multilayer Perceptron (MLP) based model M3-Net for traffic prediction. Extensive experiments conducted on multiple real datasets demonstrate the superiority of the proposed model in terms of prediction performance and lightweight deployment. Our code is available at https://github.com/jinguangyin/M3_NET
Motion-Based Bird-UAV Classification Using 3D-CNN for Long-Range Anti-UAV Systems
Authors: Woo-Cheol Jin, Daegun Oh, Sang-Chul Lee, Ji-Woong Choi
Venue: CIKM
Year: 2025

The increasing threat of malicious unmanned aerial vehicles (UAVs) necessitates robust anti-UAV systems. However, their performance is often degraded by bird misclassification caused by low-resolution imagery and unseen UAV types. This study proposes a motion-based 3D convolutional neural network (3D-CNN) trained on image sequences acquired from a radar-camera integrated anti-UAV solution. The proposed method effectively distinguishes UAVs from birds, even under low-resolution conditions and when encountering previously unseen UAV types.
Enhancing Information Diffusion Prediction via Multiple Granularity Hypergraphs and Position-aware Sequence Model
Authors: Weikai Jing, Yuchen Wang, Haotong Du, Songxin Wang, Xiaoyu Li, Chao Gao
Venue: CIKM
Year: 2025

With the rise of social media, accurately predicting information diffusion has become crucial for a wide range of applications. Existing methods usually employ sequential hypergraphs to model users' latent interaction preferences and use self-attention mechanisms to capture dependencies with users. However, they typically focus on a single temporal scale and lack the ability to effectively model temporal influence, which limits their performance in diffusion prediction tasks. To address these limitations, we propose a novel method (MHPS) to enhance information diffusion prediction via multiple granularity hypergraphs and a position-aware sequence model. Specifically, MHPS constructs hypergraph sequences of different granularities by grouping user interactions according to various time intervals. Additionally, to further enhance the modeling of temporal influence, two types of cross-attention mechanisms, namely next-step positional cross-attention and source influence cross-attention, are introduced within the cascade representation. The next-step positional cross-attention captures target position awareness, while the source influence cross-attention focuses on the impact of the initial source. Then, gating mechanisms and GRUs are employed to fuse the different attention outputs and predict the next target user. Extensive experiments on real-world datasets demonstrate that MHPS achieves competitive performance against state-of-the-art methods. The average improvements are up to 7.82% in terms of Hits@10 and 5.60% in terms of MAP@100. Our code is available at https://github.com/cgao-comp/MHPS.
Imputing Multi-Agent Trajectories from Event and Snapshot Data in Soccer
Authors: Geonhee Jo, Miru Hong, Han-Jun Choi, Minho Lee, Pascal Bauer, Sang-Ki Ko
Venue: CIKM
Year: 2025

Recent advances in wearable sensors and computer vision technologies have enabled the collection of tracking data in team sports, which has become a core resource for fine-grained analysis. However, the availability of tracking data remains constrained by high acquisition costs and technical limitations. Compared to tracking data, event data recording on-ball actions and snapshot data providing partial player positions are more widely accessible. To this end, this study proposes a novel approach to predict the positions of all players at each event timestamp in soccer matches, leveraging the limited information available from event and snapshot data. We propose an event-based imputation model that integrates spatial and temporal attention to capture the spatiotemporal multi-agent structure. In experiments, we evaluate our model on 13 soccer matches, achieving average position errors of 5.84 m. To assess the practical utility of our approach, we apply it to a downstream task called Pitch Control, which requires full tracking data. These results highlight the potential of event-based position imputation to expand access to fine-grained analysis in data-constrained settings.
Rethinking the Training Paradigm of Discrete Token-Based Multimodal LLMs: An Analysis of Text-Centric Bias
Authors: Wansik Jo, Jooyeong Na, Soyeon Hong, Seungtaek Choi, Hyunsouk Cho
Venue: CIKM
Year: 2025

Discrete token-based multimodal large language models (MLLMs), such as AnyGPT and MIO, integrate diverse modalities into an autoregressive framework by discretizing modality inputs into tokens compatible with language models. Unlike encoder-based approaches, such as LLaVA and Flamingo, which utilize pretrained modality-specific encoders, discrete token-based MLLMs simultaneously learn modality token representations and their alignment with the language, yet are exclusively trained on modality-text paired datasets without additional unimodal training. We identify a structural limitation inherent in this training paradigm, termed text-centric bias, defined as an over-reliance on the textual context that restricts intrinsic modality understanding. To systematically analyze the existence of this bias, we propose an analytical framework involving external perplexity-based and internal neuron-level analyses. Furthermore, to verify whether the bias originates from the paired-only training paradigm, we introduce an analytical methodology named Monotune, which is a simple unimodal training stage. Our analyses demonstrate that minimal exposure to unimodal data effectively mitigates text-centric bias, providing empirical evidence that the bias is fundamentally induced by the paired-only training strategy. Through comprehensive downstream task evaluations, we further reveal that this structural bias meaningfully affects real-world multimodal task performance, particularly under limited textual contexts. Our findings highlight a fundamental limitation in current discrete token-based MLLM training paradigms and suggest directions for future multimodal training strategies. Our code and experiments are available at https://github.com/41312432/Monotune
Generative Recommendation with Semantic IDs: A Practitioner's Handbook
Authors: Clark Mingxuan Ju, Liam Collins, Leonardo Neves, Bhuvesh Kumar, Louis Yufeng Wang, Tong Zhao, Neil Shah
Venue: CIKM
Year: 2025

Generative recommendation (GR) has gained increasing attention for its promising performance compared to traditional models. A key factor contributing to the success of GR is the semantic ID (SID), which converts continuous semantic representations (e.g., from large language models) into discrete ID sequences. However, varied modeling techniques, hyper-parameters, and experimental setups in existing literature make direct comparisons between GR proposals challenging. Furthermore, the absence of an open-source, unified framework hinders systematic benchmarking and extension, slowing model iteration. To address this challenge, our work introduces and open-sources a framework for Generative Recommendation with semantic ID, namely GRID, specifically designed for modularity to facilitate easy component swapping and accelerate idea iteration. Using GRID, we systematically experiment with and ablate different components of GR models with SIDs on public benchmarks. Our comprehensive experiments with GRID reveal that many overlooked architectural components in GR models with SIDs substantially impact performance. This offers both novel insights and validates the utility of an open-source platform for robust benchmarking and GR research advancement. GRID is open-sourced at https://github.com/snap-research/GRID.
Generalizing Query Performance Prediction under Retriever and Concept Shifts via Data-driven Correction
Authors: Jaehwan Jung, Jong-June Jeon
Venue: CIKM
Year: 2025

Query Performance Prediction (QPP) aims to estimate the effectiveness of an information retrieval (IR) system without access to ground-truth relevance judgments. Existing supervised QPP methods typically follow a regression model framework that maps query-document representations to target metrics such as RR@10 or nDCG@10. However, these approaches often suffer from degraded performance under concept shift, where the distribution of relevance given a query-document pair changes between training and test datasets. This paper proposes a novel classification-based framework, QPP-MLC (QPP Multi-Label Classification), which formulates QPP as a multi-label classification task. QPP-MLC infers the relevance of each document among the top-k retrieved results and aggregates these document-level relevance predictions to predict the overall query performance. As a result, QPP-MLC provides a diagnosis tool for the concept shift and a correction method under the concept shift by modulating a threshold level of classification tasks. Experiments on MS MARCO and TREC DL benchmarks show that QPP-MLC achieves strong prediction accuracy and outperforms traditional regression-based QPP methods.