OpenRaiser/Intern-Atlas huggingface dataset:
ORCAS: Obfuscation-Resilient Binary Code Similarity Analysis using Dominance Enhanced Semantic Graph
Authors: Yufeng Wang, Yuhong Feng, Yixuan Cao, Haoran Li, Haiyue Feng, Yifeng Wang
Venue: CIKM
Year: 2025

Binary code similarity analysis (BCSA) serves as a foundational technique for binary analysis tasks such as vulnerability detection and malware identification. Existing graph based BCSA approaches capture more binary code semantics and demonstrate remarkable performance. However, when code obfuscation is applied, the unstable control flow structure degrades their performance. To address this issue, we develop ORCAS, an Obfuscation-Resilient BCSA model based on Dominance Enhanced Semantic Graph (DESG). The DESG is an original binary code representation, capturing more binaries' implicit semantics without control flow structure, including inter-instruction relations (e.g., def-use), inter-basic block relations (i.e., dominance and post-dominance), and instruction-basic block relations. ORCAS takes binary functions from different obfuscation options, optimization levels, and instruction set architectures as input and scores their semantic similarity more robustly. Extensive experiments have been conducted on ORCAS against eight baseline approaches over the BinKit dataset. For example, ORCAS achieves an average 12.1% PR-AUC improvement when using combined three obfuscation options compared to the state-of-the-art approaches. In addition, an original obfuscated real-world vulnerability dataset has been constructed and released to facilitate a more comprehensive research on obfuscated binary code analysis. ORCAS outperforms the state-of-the-art approaches over this newly released real-world vulnerability dataset by up to a recall improvement of 43%.
LCHGNN: Towards Distributed Hypergraph Neural Network Training Based on Communication Graphs with Lightweight Communication Optimization
Authors: Taibo Wang, Yu Gu, Xinning Cui, Zhen Song, Xiaohua Li, Fangfang Li
Venue: CIKM
Year: 2025

Hypergraph Neural Networks (HGNNs) build on Graph Neural Networks (GNNs) by using hyperedges to capture complex, high-order relationships in data. However, training HGNNs on large hypergraphs is limited by computational and memory bottlenecks on a single machine. To overcome this, we propose LCHGNN, a distributed training method based on a new data structure called the communication graph, which simplifies hypergraph communication by representing cut hyperedges as vertices for structured message passing. LCHGNN employs a vertex-centric, hyperedge-replication-based storage scheme and introduces specialized forward and backward propagation mechanisms tailored for distributed execution. To mitigate communication overhead, We propose a lightweight optimization strategy that employs full synchronization in the initial round, followed by lightweight synchronization in subsequent rounds. Additionally, we present a learnable semi-supervised synchronization (LSS) aggregation mechanism for adaptive hyperedge selection. Extensive experiments on benchmark datasets demonstrate that LCHGNN preserves training accuracy while substantially reducing communication costs and enhancing scalability. This work addresses a critical gap in distributed HGNN research by delivering a communication-efficient and scalable training method, thereby facilitating the application of hypergraph learning to large-scale problems.
You Only Evaluate Once: A Tree-based Rerank Method at Meituan
Authors: Shuli Wang, Yinqiu Huang, Changhao Li, Yuan Zhou, Yonggang Liu, Yongqiang Zhang, Yinhua Zhu, Haitao Wang, Xingxing Wang
Venue: CIKM
Year: 2025

Reranking plays a crucial role in modern recommender systems by capturing the mutual influences within the list. Due to the inherent challenges of combinatorial search spaces, most methods adopt a two-stage search paradigm: a simple General Search Unit (GSU) efficiently reduces the candidate space, and an Exact Search Unit (ESU) effectively selects the optimal sequence. These methods essentially involve making trade-offs between effectiveness and efficiency, while suffering from a severe inconsistency problem, that is, the GSU often misses high-value lists from ESU. To address this problem, we propose YOLOR, a one-stage reranking method that removes the GSU while retaining only the ESU. Specifically, YOLOR includes: (1) a Tree-based Context Extraction Module (TCEM) that hierarchically aggregates multi-scale contextual features to achieve ''list-level effectiveness'', and (2) a Context Cache Module (CCM) that enables efficient feature reuse across candidate permutations to achieve ''permutation-level efficiency''. Extensive experiments across public and industry datasets validate YOLOR's performance and we have successfully deployed YOLOR on the Meituan food delivery platform.
Retrieval-LTV: Fine-Grained Transfer Learning for Lifetime Value Estimation in Large-Scale Industrial Retrieval
Authors: Shirui Wang, Shengbin Jia, Tianyue Cao, Shuo Yang, Lei Jiang, Qi He, Lingling Yao, Yang Xiang
Venue: CIKM
Year: 2025

In computational advertising, platforms are increasingly optimizing toward advertisers' real assessment metrics to help achieve more reliable advertising performance. Consequently, predicting customers' Lifetime Value (LTV) has become an essential component of the advertising system, as it directly impacts the actual Return On Investment (ROI) of advertisers. Recent research on LTV prediction primarily focuses on the ranking stage, lacking consideration of the initial retrieval stage. This oversight may lead to the inconsistency between retrieval and ranking, resulting in a loss of efficiency. Unlike the LTV estimation in the ranking stage, the retrieval stage faces more severe data sparsity and constraints inherent in online scoring. Incorporating rich data from other domains can mitigate the sparsity while introducing the negative transfer issue. To tackle these challenges, we introduce Retrieval-LTV, a two-tower retrieval model for LTV prediction. This model employs a cooperative framework and incorporates a fine-grained evaluation for each sample across each expert, thereby enhancing effective selective learning from the source domain while mitigating the risk of negative transfer. Additionally, we have designed a specialized representation transformation to obtain the LTV-oriented score for online retrieval. Experiments on three real-world industrial datasets demonstrate that Retrieval-LTV outperforms all the baselines, achieving superior performance. An online A/B test further confirms the effectiveness of Retrieval-LTV, increasing the overall LTV by 2.08%. As a result, Retrieval-LTV has now been fully deployed in Tencent Ads.
Unified Robustness via Spurious-Invariant Features and On-Manifold Adversaries
Authors: Xu Wang, Rajgopal Kannan, Viktor K. Prasanna
Venue: CIKM
Year: 2025

Vision models fail both under tiny pixel attacks and under real-world shifts in style or background because they latch onto spurious features. We propose a two-step, label-free method. (1) Spurious-Invariant Self-Supervised Pre-training (SISSP) trains an encoder to collapse representations of the same object despite randomized styles and backgrounds, pruning shortcut signals. (2) Semantic-Alignment Adversarial Refinement (SAAR) takes any attack and projects it back into a small ball within SISSP feature space, yielding adversaries that look natural yet still fool the classifier. Fine-tuning with SISSP features and SAAR images produces a ResNet-50 that retains 64% ImageNet accuracy, 46% PGD robustness without environment labels or specialized augmentations. Together, SISSP provides a semantics-aware metric and SAAR generates on-manifold adversaries, achieving the first ImageNet-scale model robust to both pixel-level noise and semantic shifts.
Generative Data Augmentation in Graph Contrastive Learning for Recommendation
Authors: Yansong Wang, Qihui Lin, Junjie Huang, Tao Jia
Venue: CIKM
Year: 2025

Recommendation systems have become indispensable in various online platforms, from e-commerce to streaming services. A fundamental challenge in this domain is learning effective embeddings from sparse user-item interactions. While contrastive learning has recently emerged as a promising solution to this issue, generating augmented views for contrastive learning through most existing random data augmentation methods often leads to the alteration of original semantic information. In this paper, we propose a novel framework, GDA4Rec (Generative Data Augmentation in graph contrastive learning for Recommendation) to generate high-quality augmented views and provide robust self-supervised signals. Specifically, we employ a noise generation module that leverages deep generative models to approximate the distribution of original data for data augmentation. Additionally, GDA4Rec further extracts an item complement matrix to characterize the latent correlations between items and provide additional self-supervised signals. Lastly, a joint objective that integrates recommendation, data augmentation and contrastive learning is used to enforce the model to learn more effective and informative embeddings. Extensive experiments are conducted on three public datasets to demonstrate the superiority of the model. The code is available at: https://github.com/MrYansong/GDA4Rec.
Beyond Surface Similarity: A Riemannian Hierarchical Ranking Framework for Sociological Concept Equivalence
Authors: Zeqiang Wang, Wing Yan Li, Jon Johnson, Nishanth Sastry, Suparna De
Venue: CIKM
Year: 2025

Vocabularies such as the European Language Social Science Thesaurus (ELSST) and the CLOSER ontology are the foundational taxonomies capturing core social science concepts that form the foundations of large-scale longitudinal social science surveys. However, standard text embeddings often fail to capture the complex hierarchical and relational structures of the sociological concepts, relying on surface similarity. In this work, we propose a framework to model these nuances by adapting a large language model based text embedding model with a learnable diagonal Riemannian metric. This metric allows for a flexible geometry where dimensions can be scaled to reflect semantic importance. Additionally, we introduce a Hierarchical Ranking Loss with dynamic margins as the sole training objective to enforce the multi-level hierarchical constraints (e.g., distinguishing 'self' from narrower, broader, or related concepts, and all from 'unrelated' ones) from ELSST within the Riemannian space, such as ensuring a specific concept like 'social stratification' is correctly positioned by, for instance, being embedded closer to 'social inequality' (as its broader, related concept) and substantially further from an 'unrelated' concept like 'particle physics'. Lastly, we show that our parameter-efficient approach significantly outperforms strong contrastive learning and hyperbolic embedding baselines on hierarchical concept retrieval and classification tasks using the ELSST and CLOSER datasets. Visualizations confirm the learned embedding space exhibits a clear hierarchical structure. Our work offers a more accurate and geometrically informed method for representing complex sociological constructs.
MFAE: Multimodal Feature Adaptive Enhancement for Fake News Video Detection
Authors: Wenhao Wang, Mingxin Li, Jiao Qiao, Haotong Du, Xianghua Li, Chao Gao, Zhen Wang
Venue: CIKM
Year: 2025

With the rapid global growth of short video platforms, the spread of fake news has become increasingly prevalent, creating an urgent demand for effective automated detection methods. Current approaches typically rely on feature extractors to gather information from multiple modalities and then generate predictions through classifiers. However, these methods often fail to fully utilize the complex information across all modalities and overlook the potential for video manipulation, limiting their overall performance. To tackle these issues, MFAE is proposed, a novel framework for Multimodal Feature Adaptive Enhancement for Fake News Video Detection. The framework starts by extracting semantic and emotional features from the news, which are the basis for generating coarse multimodal representations. These representations are further refined through Adaptive Enhancement, a module specifically designed to strengthen the visual and audio modalities. Subsequently, spatial and temporal features are extracted separately, with temporal features undergoing additional refinement via a Temporal Enhancement module. The final result is obtained by feeding the individually enhanced features into the multimodal feature integration module for interaction Comprehensive experiments on two benchmark datasets highlight the exceptional performance of MFAE in detecting fake news on short video platforms. Specifically, the method achieves accuracy improvements of 2.21% and 4.35% on FakeSV and FakeTT, respectively.
Weakly Supervised Fine-grained Span-Level Framework for Chinese Radiology Report Quality Assurance
Authors: Kaiyu Wang, Lin Mu, Zhiyao Yang, Ximing Li, Xiaotang Zhou, Wanfu Gao, Huimao Zhang
Venue: CIKM
Year: 2025

Quality Assurance (QA) for radiology reports refers to judging whether the junior reports (written by junior doctors) are qualified. The QA scores of one junior report are given by the senior doctor(s) after reviewing the image and junior report. This process requires intensive labor costs for senior doctors. Additionally, the QA scores may be inaccurate for reasons like diagnosis bias, the ability of senior doctors, and so on. To address this issue, we propose a Span-level Quality Assurance EvaluaTOR (Sqator) to mark QA scores automatically. Unlike the common document-level semantic comparison method, we try to analyze the semantic difference by exploring more fine-grained text spans. Specifically, Sqator measures QA scores by measuring the importance of revised spans between junior and senior reports, and outputs the final QA scores by merging all revised span scores. We evaluate Sqator using a collection of 12,013 radiology reports. Experimental results show that Sqator can achieve competitive QA scores. Moreover, the importance scores of revised spans can be also consistent with the judgments of senior doctors.
Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation
Authors: Shijia Wang, Tianpei Ouyang, Qiang Xiao, Dongjing Wang, Yintao Ren, Songpei Xu, Da Guo, Chuanjiang Luo
Venue: CIKM
Year: 2025

In music recommendation systems, multimodal interest learning is pivotal, which allows the model to capture nuanced preferences, including textual elements such as lyrics and various musical attributes such as different instruments and melodies. Recently, methods that incorporate multimodal content features through semantic IDs have achieved promising results. However, existing methods suffer from two critical limitations: 1) intra-modal semantic degradation, where residual-based quantization processes gradually decouple discrete IDs from original content semantics, leading to semantic drift; and 2) inter-modal modeling gaps, where traditional fusion strategies either overlook modal-specific details or fail to capture cross-modal correlations, hindering comprehensive user interest modeling. To address these challenges, we propose a novel multimodal recommendation framework with two stages. In the first stage, our Progressive Semantic Residual Quantization (PSRQ) method generates modal-specific and modal-joint semantic IDs by explicitly preserving the prefix semantic feature. In the second stage, to model multimodal interest of users, a Multi-Codebook Cross-Attention (MCCA) network is designed to enable the model to simultaneously capture modal-specific interests and perceive cross-modal correlations. Extensive experiments on multiple real-world datasets demonstrate that our framework outperforms state-of-the-art baselines. This framework has been deployed on one of China's largest music streaming platforms, and online A/B tests confirm significant improvements in commercial metrics, underscoring its practical value for industrial-scale recommendation systems.
SolarMAE: A Unified framework for Regional Centralized and Distributed Solar Power Forecasting with Weather Pre-training
Authors: Jin Wang, Bingqing Peng, Wenwei Wang, Yuanjie Hu, Yuejiang Chen, Peisong Niu, Liang Sun
Venue: CIKM
Year: 2025

The recent surge in solar plant installations has notably decreased the reliance on fossil fuels while also presenting significant challenges to power grid. Therefore, the accurate forecasting of centralized and distributed solar power has become critically important. Although site-specific forecasting models typically perform better for utility-scale solar power plants, the model maintenance can be troublesome as the number of solar plants grows. Furthermore, the rapid growth and difficulties in real-time data collection associated with distributed solar systems exacerbate the complexity of regional gross solar power forecasting. To address these issues, we propose SolarMAE, a unified regional solar power forecasting framework enabling end-to-end precise forecasting for both centralized and distributed solar systems. It adopts masked autoencoder (MAE) pre-training strategy for numerical weather prediction (NWP) reconstruction at first, aiming to derive spatiotemporal correlations within meteorological variables, and then fine-tunes a temporal convolutional neural network which predicts future solar power generation. Experiments show that this framework outperforms state-of-the-art centralized or distributed solar power forecasting methods in accuracy, and significantly reduces model maintenance cost. It also demonstrates strong few-shot learning capabilities, which is particularly useful for the cold start problem of newly installed solar plants. The unified solar power forecasting system has been deployed in a province in eastern China, serving solar systems with over 73 GW gross installed capacity and more than 400 centralized solar plants.
Fairness in Language Models: A Tutorial
Authors: Zichong Wang, Avash Palikhe, Zhipeng Yin, Wenbin Zhang
Venue: CIKM
Year: 2025

Language Models (LMs) achieve outstanding performance across diverse applications but often produce biased outcomes, raising concerns about their trustworthy deployment. These concerns call for fairness research specific to LMs; however, most existing work in machine learning assumes access to model internals or training data, conditions that rarely hold in practice. As LMs continue to exert growing societal influence, it becomes increasingly important to understand and address fairness challenges unique to these models. To this end, our tutorial begins by showcasing real-world examples of bias to highlight their practical implications and uncover underlying sources. We then define fairness concepts tailored to LMs, review methods for bias evaluation and mitigation, and present a multi-dimensional taxonomy of benchmark datasets for fairness assessment. We conclude by outlining open research challenges, aiming to provide the community with both conceptual clarity and practical tools for fostering fairness in LMs. All tutorial resources are publicly accessible at https://github.com/vanbanTruong/fairness-in-large-language-models.
Google Ads Content Moderation with RAG
Authors: Yuan Wang, Wei Qiao, Jingxiang Li, Tiantian Fang, Eric Xiao, Megan Oftelie, Zhimin Wang, Yintao Liu, Jimin Li, Yi-Ting Chen, Zhongli Ding, Enming Luo
Venue: CIKM
Year: 2025

Keeping ad content policy classifiers up to date while maintaining the high quality bar is a significant challenge, especially with new threats emerging constantly. This paper introduces a new application to apply RAG-inspired in-context learning to accelerate content policy enforcement, especially when mitigating new emerging violations. Our application leverages RAG-based LLM inference for classification tasks and incorporates augmented reasoning information for better performance. We also developed a practical framework to enforce new violation patterns in O(1) days demonstrating improved memorization and generalization capabilities compared to traditional parametric and non-parametric models.
Forecasting at Full Spectrum: Holistic Multi-Granular Traffic Modeling under High-Throughput Inference Regimes
Authors: Zhaoyan Wang, Xiangchi Song, In-Young Ko
Venue: CIKM
Year: 2025

Notably, current intelligent transportation systems rely heavily on accurate traffic forecasting and swift inference provision to make timely decisions. While Graph Convolutional Networks (GCNs) have shown benefits in modeling complex traffic dependencies, the existing GCN-based approaches cannot fully extract and fuse multi-granular spatiotemporal features across various spatial and temporal scales sufficiently in a complete manner, proven to yield less accurate results. As extracting multi-granular features across scales has been a promising strategy across domains such as computer vision, natural language processing, and time-series forecasting, pioneering studies have attempted to leverage a similar mechanism for spatiotemporal traffic data mining. However, additional feature extraction branches introduced in prior studies critically increased model complexity and extended inference time, making it challenging to provide fast forecasts. In this paper, we propose MultiGran-STGCNFog, an efficient fog distributed inference system with a novel traffic forecasting model that employs multi-granular spatiotemporal feature fusion on generated dynamic traffic graphs to fully capture interdependent traffic dynamics. The proposed scheduling algorithm GA-DPHDS, optimizing layer execution order and layer-device scheduling scheme simultaneously, contributes to considerable inference throughput improvement by coordinating heterogeneous fog devices in a pipelined manner. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method over selected GCN baselines.
MGFSG-EE: A Method based on Multi-grained Fusion and Scene Graph Enhancement for Event Extraction
Authors: Xiaoyu Wang, Tao Sun, Gengchen Liu, Zhi Yang, Jiahui Liu, Zimeng Xu
Venue: CIKM
Year: 2025

The Multimedia Event Extraction (MEE) task, as a core task in the field of event analysis, has seen many benefits in downstream applications. Existing MEE methods focus on the fusion of co-occurring information in images and text during the fusion process, failing to model the background information correlation between images and text, making it difficult to extract events and arguments in complex scenarios. Meanwhile, the neglect of utilizing the interaction information between objects in images leads to the loss of event information and incomplete argument extraction. To address these issues, a novel method based on Multi-grained Fusion and Scene Graph Enhancement (MGFSG-EE) has been proposed, that introduces a Multi-grained Fusion Module, which captures co-occurring information between image and text through dynamic screening of cross-modal features for coarse-grained fusion; builds a Multimodal Graph and utilizes graph convolutional neural networks (GCN) to achieve fine-grained interaction fusion at the vector level, mining semantic associations and contextual information between image and text. Moreover, MGFSG-EE constructs a Scene Graph to model the spatial and semantic relationships among objects and uses GCN to learn rich event representations. In M2 E2 benchmark dataset, MGFSG-EE outperforms the existing SOTA baselines, particularly in visual and multimedia event tasks, the F1 score for the event trigger task is improved by 3.2% and 2.3%, respectively; and for event argument extraction task it is improved by 2.7% and 2.8%, respectively, verifying the effectiveness of the proposed method.