OpenRaiser/Intern-Atlas huggingface dataset:
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Authors: Yunfeng Zhao, Yixin Liu, Shiyuan Li, Qingfeng Chen, Yu Zheng, Shirui Pan
Venue: CIKM
Year: 2025

Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. Despite the current advancements in deep learning-based GAD, existing approaches often suffer from high deployment costs and poor scalability due to their complex and resource-intensive training processes. Surprisingly, our empirical findings suggest that the training phase of deep GAD methods, commonly perceived as crucial, may actually contribute less to anomaly detection performance than expected. Inspired by this, we propose FreeGAD, a novel training-free yet effective GAD method. Specifically, it leverages an affinity-gated residual encoder to generate anomaly-aware representations. Meanwhile, FreeGAD identifies anchor nodes as pseudo-normal and anomalous guides, followed by calculating anomaly scores through anchor-guided statistical deviations. Extensive experiments demonstrate that FreeGAD achieves superior anomaly detection performance, efficiency, and scalability on multiple benchmark datasets from diverse domains, without any training or iterative optimization.
ClariLM: Enhancing Open-domain Clarification Ability for Large Language Models
Authors: Ziliang Zhao, Haonan Chen, Shiren Song, Jian Xie, Zhicheng Dou
Venue: CIKM
Year: 2025

Active understanding and clarification of user intent is crucial for information-seeking systems based on Large Language Models (LLMs), as it enhances search efficiency and improves user experience for human-LLM interaction. While existing systems rely on domain-specific resources to generate clarifying questions, they face challenges when extended to open-domain scenarios due to the lack of human-LLM clarification data. In this paper, we propose ClariLM to synthesize large-scale clarification data and enhance the LLMs' clarification capability. Specifically, we design two key stages to prepare data: first, given a user question, the Clarification Facet Detection (CFD) stage employs a facet mining model learned from human-LLM conversation logs to predict realistic potential clarification candidates. Additionally, it incorporates direct predictions from powerful LLMs as supplements to guarantee comprehensive facet coverage. While CFD ensures high recall of facet candidates, the subsequent Optimal Facet Selection (OFS) stage synthesizes a set of new questions and employs a reasoning model to annotate the optimal facet for each question, which further improves the precision of ClariLM in clarification necessity prediction and optimal facet selection. The collected data are then applied for supervised fine-tuning, followed by constructing preference data for preference optimization. Experiments on our custom test set and two public benchmarks demonstrate that ClariLM significantly outperforms various baseline models across clarification necessity, clarifying question quality, and GPT-4-based comparative evaluation.
Antelope: Potent and Concealed Jailbreak Attack Strategy
Authors: Xin Zhao, Xiaojun Chen, Haoyu Gao
Venue: CIKM
Year: 2025

Due to the remarkable generative potential of diffusion-based models, numerous researches have investigated jailbreak attacks targeting these frameworks. A particularly concerning threat within image models is the generation of Not-Safe-for-Work (NSFW) content. Despite the implementation of security filters, numerous efforts continue to explore ways to circumvent these safeguards. Current attack methodologies primarily encompass adversarial prompt engineering or concept obfuscation, yet they frequently suffer from slow search efficiency, conspicuous attack characteristics and poor alignment with targets. To overcome these challenges, we propose Antelope, a more robust and covert jailbreak attack strategy designed to expose security vulnerabilities inherent in generative models. Specifically, Antelope leverages the confusion of sensitive concepts with similar ones, facilitates searches in the semantically adjacent text space of these related concepts and aligns them with the target imagery, thereby generating sensitive images that are consistent with the target and capable of evading detection. Besides, we successfully exploit the transferability of model-based attacks to penetrate online black-box services. Experimental evaluations demonstrate that Antelope outperforms existing baselines across multiple defensive mechanisms, underscoring its efficacy and versatility. Disclaimer: This paper contains unsafe imagery that might be offensive to some readers.
ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Authors: Yicong Zhao, Shisong Chen, Jiacheng Zhang, Zhixu Li
Venue: CIKM
Year: 2025

Recent advances in large language models (LLMs) have demonstrated impressive capabilities in code-related tasks such as code generation and automated program repair. Despite their promising performance, most existing approaches for code repair suffer from high training costs or computationally expensive inference. Retrieval-augmented generation (RAG), with its efficient in-context learning paradigm, offers a more scalable alternative. However, conventional retrieval strategies, which are often based on holistic code-text embeddings, fail to capture the structural intricacies of code, resulting in suboptimal retrieval quality. To address the above limitations, we propose ReCode, a fine-grained retrieval-augmented in-context learning framework designed for accurate and efficient code repair. Specifically, ReCode introduces two key innovations: (1) an algorithm-aware retrieval strategy that narrows the search space using preliminary algorithm type predictions; and (2) a modular dual-encoder architecture that separately processes code and textual inputs, enabling fine-grained semantic matching between input and retrieved contexts. Furthermore, we propose RACodeBench, a new benchmark constructed from real-world user-submitted buggy code, which addresses the limitations of synthetic benchmarks and supports realistic evaluation. Experimental results on RACodeBench and competitive programming datasets demonstrate that ReCode achieves higher repair accuracy with significantly reduced inference cost, highlighting its practical value for real-world code repair scenarios.
Hybrid2: Distributed GNN Training System Enhanced by Dual-Hybrid for Sampling and Loading
Authors: Chu Zhao, Shengjie Dong, Yuhai Zhao, Yuan Li, Zhengkui Wang, Xingwei Wang
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) are the rising standard for graph tasks, yet their distributed training in servers or computing power network remains challenging. Cross-machine sampling and data loading often create bottlenecks, leading to inefficient resource utilization. In this paper, we present Hybrid2, a distributed GNN training system that combines full-graph and mini-batch training through a novel hybrid-batch training method. It also adopts hybrid feature extraction, leveraging both local caching and remote access to improve feature retrieval efficiency. The integration of these methods in Hybrid² results in a dual hybrid-gain effect. First, it reduces sampling and loading overhead by pre-aggregating neighbors for each target vertex, minimizing the layers to sample and load. Second, it accelerates data loading by dynamically identifying and locally caching the most frequently accessed vertices during training, maximizing memory efficiency. Experimental results demonstrate that Hybrid² brings substantial performance improvements across key components of distributed GNN training. Network communication overhead is reduced by up to tens of times, while both sampling and loading achieve at least several-fold speedups. These gains contribute to an overall training acceleration exceeding 20× compared to DistDGL, all with comparable GPU memory usage and no loss in accuracy. Compared to the state-of-the-art system, it achieves nearly 3× speedup while using fewer resources.
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
Authors: Haokun Zhao, Jinyi Han, Jie Shi, Chengyu Du, Jiaqing Liang, Yanghua Xiao, Weikang Zhou, Zeye Sun, Fei Yu
Venue: CIKM
Year: 2025

Large Language Models (LLMs) achieve remarkable success, but their static nature leads to inherent limitations and persistent mistakes in dynamic real-world scenarios. While Continual Instruction Tuning (CIT) and Continual Pre-training (CPT) are primary continual learning approaches, they struggle with scalable knowledge acquisition and maintaining model capabilities. To address these, we propose the Continue Evolving from Mistakes (CEM) method, a novel and data-efficient framework for continuous LLM evolution. Inspired by human learning, CEM establishes an iterative process: it efficiently collects targeted CPT data by robustly identifying LLM mistakes and uncertainties (via an Ambiguity-Aware Knowledge Collection (AAKC) algorithm), and employs a novel joint training paradigm that leverages CIT and CPT to assimilate knowledge efficiently while maintaining existing capabilities and mitigating catastrophic forgetting. Extensive experiments confirm CEM's effectiveness, yielding substantial accuracy gains for multiple models, increasing accuracy by up to 29.63%. Code and datasets are available on GitHub https://anonymous.4open.science/r/cem-BB25.
CyberBOT: Ontology-Grounded Retrieval Augmented Generation for Reliable Cybersecurity Education
Authors: Chengshuai Zhao, Riccardo De Maria, Tharindu Kumarage, Kumar Satvik Chaudhary, Garima Agrawal, Yiwen Li, Jongchan Park, Ying-Chih Chen, Yuli Deng, Huan Liu
Venue: CIKM
Year: 2025

Advancements in large language models (LLMs) have enabled the development of intelligent educational tools that support inquiry-based learning across technical domains. In cybersecurity education, where accuracy and safety are paramount, systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate. To address this challenge, we introduce CyberBOT. Code: https://github.com/rccrdmr/CyberBOT, a question-answering chatbot that leverages a retrieval-augmented generation (RAG) pipeline to incorporate contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology. The ontology serves as a structured reasoning layer that constrains and verifies LLM-generated answers, reducing the risk of misleading or unsafe guidance. CyberBOT has been deployed in a large graduate-level course at Arizona State University (ASU). Video: https://youtu.be/X2WorBxOQHo which illustrates a promising direction for developing reliable and curriculum-aligned AI applications in specialized educational contexts.
OntoLDiff: A Highly Efficient System for Tracking Logical Difference in Large-Scale Ontologies
Authors: Yizheng Zhao, Renate A. Schmidt
Venue: CIKM
Year: 2025

Modern ontologies undergo continuous evolution to accommodate new domain knowledge, correct modeling errors, and adapt to changing user requirements. Monitoring these changes is crucial for maintaining ontology quality and understanding the semantic impact of modifications on dependent systems and applications. This paper describes OntoLDiff, a highly efficient system for tracking the logical difference between two ontologies formulated in the description logic ELH. Intuitively, the logical difference between two versions of an ontology refers to the set of axioms entailed by one version but not the other, indicating the information gain or loss between them. Typically, such axioms, referred to as 'witnesses ', can be infinite, making logical difference computation infeasible. To address this challenge, OntoLDiff employs a Uniform Interpolation (UI) approach to compute a finite representation of these axioms. Instead of computing all the entailments of one ontology but not the other, which would be computationally infeasible, the UI-based approach focuses on identifying only the strongest entailments, from which all witnesses can in principle be computed from its deductive closure. Despite UI's computational complexity, OntoLDiff is currently the only tool that efficiently tracks logical differences in industrial-scale ontologies, enabling ontology curators to precisely identify meaningful changes during ontology evolution.
FollowGPT: A Framework of Follow-up Question Generation for Large Language Models via Conversation Log Mining
Authors: Ziliang Zhao, Shiren Song, Zhicheng Dou
Venue: CIKM
Year: 2025

During interactions between users and Large Language Models (LLMs), users often engage in multi-turn questioning. Understanding the user's potential follow-up intents and generating follow-up question candidates for the user is crucial for enhancing their experience with LLMs. Existing methods for follow-up question generation mainly rely on hand-crafted rules, the internal knowledge of LLMs, or the integration of external knowledge. However, these approaches fail to effectively leverage real-world user follow-up intents when interacting with LLMs, resulting in generated questions that do not meet the needs of practical scenarios. In this paper, we propose FollowGPT, a model that mines user follow-up intents from user-LLM conversational logs. However, directly introducing raw conversation logs leads to significant noise and sparsity issues. Therefore, to address noises, FollowGPT adopts a hierarchical filtering strategy for data cleaning. To mitigate the sparsity issue, FollowGPT employs data synthesis methods to augment the log data across three dimensions: topic diversity, intent transition diversity, and negative sample diversity. The processed data is then consolidated into a new dataset named ShareFQG for both training and evaluation. Finally, we train FollowGPT using a two-stage training framework involving supervised fine-tuning and preference optimization. In our experiments, we evaluate on both the ShareFQG test set and a publicly available dataset, FollowupQG, using both automated metrics and GPT-4o-based comparative evaluation. The experimental results demonstrate that our method outperforms existing baselines in various metrics, including lexical similarity, semantic similarity, and GPT-4-based evaluation for follow-up question generation, demonstrating FollowGPT's effectiveness.
Robust Heterogeneous GNNs via Semantic Attention and Contrastive Learning
Authors: Chongjie Zhao, Jinyan Wang, Linlin Su, Zeming Gan, Ziyang Zhou
Venue: CIKM
Year: 2025

Heterogeneous Graph Neural Networks (HGNNs) have achieved significant success in various graph-related applications. However, their vulnerability to adversarial attacks remains insufficiently studied, particularly when malicious modifications are made to the graph structure, such as the addition of redundant edges or the removal of key semantic edges. These perturbations not only interfere with the aggregation of neighboring information but also compromise the semantic integrity of meta-paths, leading to a significant degradation in model performance. Although previous studies have preliminarily revealed the impact of structural perturbations on HGNN performance, there has been insufficient exploration into how to develop efficient defense mechanisms from both structural and semantic perspectives. To address this, we propose a novel defense framework that integrates a meta-path-guided semantic-aware attention mechanism. This mechanism dynamically adjusts edge weights to suppress noisy connections and enhance those that are structurally and semantically significant. Additionally, to compensate for the expressive power of raw features, we introduce a contrastive learning strategy that combines local and global structural augmentations to guide the model in learning perturbation-invariant representations in a self-supervised manner. Extensive experiments on multiple real-world heterogeneous graph datasets demonstrate that our proposed method significantly improves the robustness of HGNNs against adversarial attacks, showing both effectiveness and generalization capability.
Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment
Authors: Bo Zhao, Yinghao Zhang, Ziqi Xu, Yongli Ren, Xiuzhen Zhang, Renqiang Luo, Zaiwen Feng, Feng Xia
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have shown impressive capabilities in natural language processing but still struggle to perform well on knowledge-intensive tasks that require deep reasoning and the integration of external knowledge. Although methods such as Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) have been proposed to enhance LLMs with external knowledge, they still suffer from internal bias in LLMs, which often leads to incorrect answers. In this paper, we propose a novel causal prompting framework, Conditional Front-Door Prompting (CFD-Prompting), which enables the unbiased estimation of the causal effect between the query and the answer, conditional on external knowledge, while mitigating internal bias. By constructing counterfactual external knowledge, our framework simulates how the query behaves under varying contexts, addressing the challenge that the query is fixed and is not amenable to direct causal intervention. Compared to the standard front-door adjustment, the conditional variant operates under weaker assumptions, enhancing both robustness and generalisability of the reasoning process. Extensive experiments across multiple LLMs and benchmark datasets demonstrate that CFD-Prompting significantly outperforms existing baselines in both accuracy and robustness.
Towards Unbiased and Real-Time Staytime Prediction for Live Streaming Recommendation
Authors: Haiyuan Zhao, Changshuo Zhang, Yang Wang, Hao Wang, Zhen Ouyang, Bin Yuan, Qinglei Wang, Zuotao Liu
Venue: CIKM
Year: 2025

Live streaming has emerged as a dynamic content format that delivers real-time and interactive experiences to users. Distinguished by the short lifespan and immersive nature of live rooms, live streaming poses two key challenges for recommendation: (1) Timeliness: the model must rapidly identify and promote relevant live rooms to target users within a limited window; and (2) Accurate staytime prediction: since extended watching often reflects content quality and user satisfaction, precisely predicting staytime serves as a critical indicator of recommendation relevance and user engagement. Existing approaches often improve timeliness by repeatedly sending staytime signals to accelerate model learning. However, this introduces label truncation bias, distorting the unbiased estimation of high staytime samples. To reconcile these competing demands, we propose MS3M (Multi-Stream Segmented Staytime Modeling), a novel framework that leverages multiple data streams for faster learning while employing segmented staytime modeling-converting staytime regression into a series of time-segmented classification tasks to ensure unbiased training. Furthermore, to address the sparsity of high staytime samples, MS3M's task-dependent architecture allows high staytime parameters to leverage prior knowledge from low staytime data, significantly improving generalization for long-duration watching behaviors. Extensive offline experiments and online A/B tests on TikTok confirm that MS3M effectively balances timeliness and unbiased learning, leading to substantial gains in recommendation accuracy. The proposed approach currently serves TikTok's live streaming recommendation system, contributing to continuous improvement in user watching experience.
Adapting LLMs for Personalized Evaluation of Explanations for Recommendations: A Meta-Learning Approach based on MAML
Authors: Yurou Zhao, Yingfei Zhang, Quan Zhou, Shuang Zhang, Wei Lin, Jiaxin Mao
Venue: CIKM
Year: 2025

Providing explanations to justify recommendations enhances user satisfaction and trust. Despite significant research on explanation generation methods, evaluating their quality remains a critical yet under-explored challenge. Although large language models (LLMs) have been used for automated evaluation of explanations, existing approaches fail to account for the highly personalized na- ture of explanation assessment, where user judgments towards the same explanations vary significantly. To address this, we pro- pose MAML+PEFT method that combines Model-Agnostic Meta- Learning (MAML) with LoRA-based parameter-efficient tuning to adapt LLMs for personalized explanation evaluation. Building on this, we introduce TSA-MAML (Task Similarity Aware MAML)+PEFT, which clusters users based on their estimated optimal model param- eters and learns group-specific meta models by leveraging implicit group distributions of user preferences. Experiments on synthetic and human-annotated datasets demonstrate superior alignment of MAML-based methods with human ratings in both generalization and few-shot adaptation settings. Additionally, we examine the cor- relation of MAML-based LLM-simulated human ratings with real online user behaviors on a large-scale recommendation platform, demonstrating the practical utility of our methods for real-world explainable recommendation systems.
Personalized Multi Modal Alignment Encoding for CTR-Recommendation in WeChat
Authors: Jiawei Zheng, Hao Gu, Lingling Yi, Jie Wen, Chuan Chen
Venue: CIKM
Year: 2025

In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling. In industry recommendation system, a wide-used modeling architecture is to first pre-train a multi-modal model to provide omnipotent representations and then encode to discrete semantic IDs for online model. Although such a paradigm achieves remarkable improvements, there still exist two problems that limit model performance: (1) Modalities Mapping Independence: Each modal representation is independently mapped to semantic spaces and then get the specific code, which ignores the consistency and complementarity of different modalities of the same item. (2) User-irrelevant Clustering Assignment: For the specific item, most of existing quantization methods assume that all users share the same cluster assignments, failing to account for the varying interpretations and emotional responses users may have toward an item. To address these challenges, we propose a Personalized Multi Modal Alignment Encoding for CTR-Recommendation in WeChat (PMMAE for short). First, we design a multi modal contrastive alignment module to ensure the consistency of various modalities encoding. We then fuse them to form a consistent and comprehensive semantic label. Second, a meta network fed with users' interest embeddings is learned to generate personalized functions to achieve personalized clustering assignment for each user. Benefiting from the meta-generated personalized assignment function, we can take full account of the variability in users' understanding of items.Extensive experimental results demonstrate that our model PMMAE significantly outperforms baseline models on both offline performance and online A/B tests in WeChat recommendation scenario. Our model has been deployed on WeChat's various services, serving hundreds of millions of users daily.