OpenRaiser/Intern-Atlas huggingface dataset:
SeLeRoSa: Sentence-Level Romanian Satire Detection Dataset
Authors: Razvan-Alexandru Smadu, Andreea Iuga, Dumitru-Clementin Cercel, Florin Pop
Venue: CIKM
Year: 2025

Satire, irony, and sarcasm are techniques that are typically used humorously or critically, rather than deceptively; they can occasionally be mistaken for factual reporting, akin to fake news. These techniques can be applied at a more granular level, allowing satirical information to be incorporated into news articles. In this paper, we introduce the first sentence-level dataset for Romanian satire detection for news articles, called SeLeRoSa. The dataset comprises 13,873 manually annotated sentences spanning various domains, including social issues, IT, science, and movies. With the rise and recent progress of large language models (LLMs) in the natural language processing literature, LLMs have demonstrated enhanced capabilities to tackle various tasks in zero-shot settings. We evaluate multiple baseline models based on LLMs in both zero-shot and fine-tuning settings, as well as transformer-based models. Our findings reveal the current limitations of these models in the sentence-level satire detection task, paving the way for new research directions.
AutoCoRe-FL: Automatic Concept-based Rule Reasoning in Federated Learning
Authors: Ahmed Soliman, Radwa El Shawi
Venue: CIKM
Year: 2025

Federated learning (FL) enables decentralized model training without centralizing raw data, yet achieving interpretability under such constraints remains challenging. We propose AutoCoRe-FL, a framework for interpretable FL that eliminates the need for predefined or manually labeled concepts. In AutoCoRe-FL, each client automatically extracts high-level visual concepts-clusters of semantically coherent image regions that correspond to human-understandable properties-using local segmentation, self-supervised representation learning, and clustering. These concepts are used to encode data as interpretable vectors, from which clients train symbolic models that generate rule-based explanations. The server then aggregates these rules through an iterative, communication-efficient process to build a global, coherent, and transparent model. Experiments on benchmark datasets demonstrate that AutoCoRe-FL produces accurate symbolic explanations while achieving competitive predictive performance. Notably, it outperforms LR-XFL-the current state-of-the-art interpretable FL baseline that relies on predefined concept supervision-in both rule quality and classification accuracy.
Caption, Create, Continue: Continual Learning with Pre-trained Generative Vision-Language Models
Authors: Indu Solomon, Aye Phyu Phyu Aung, Uttam Kumar, Senthilnath Jayavelu
Venue: CIKM
Year: 2025

Continual learning (CL) enables models to adapt to evolving data streams without catastrophic forgetting, a fundamental requirement for real-world AI systems. However, the current methods often depend on large replay buffers or heavily annotated datasets which are impractical due to storage, privacy, and cost constraints. We propose CLTS (Continual Learning via Text-Image Synergy), a novel class-incremental framework that mitigates forgetting without storing real task data. CLTS leverages pre-trained vision-language models, BLIP (Bootstrapping Language-Image Pre-training) for caption generation and stable diffusion for sample generation. Each task is handled by a dedicated Task Head, while a Task Router learns to assign inputs to the correct Task Head using the generated data. On three benchmark datasets, CLTS improves average task accuracy by up to 54% and achieves 63 times better memory efficiency compared to four recent continual learning baselines, demonstrating improved retention and adaptability. CLTS introduces a novel perspective by integrating generative text-image augmentation for scalable continual learning.
Filtered One-Shot Training for Quantum Architecture Search
Authors: Seok Bin Son, Samuel Yen-Chi Chen, Joongheon Kim, Soohyun Park
Venue: CIKM
Year: 2025

Quantum neural networks (QNNs) have attracted growing interest for their potential to accelerate computation and leverage quantum supremacy. Their performance largely depends on gate placement within parameterized quantum circuits (PQCs), making neural architecture search (NAS) a suitable approach for discovering efficient structures. However, applying conventional NAS to QNNs is hindered by barren plateaus, noise, hardware constraints, and high computational costs. To address these challenges, this paper proposes filtered one-Shot training for quantum architecture search, which combines deep reinforcement learning (DRL) for constraint-aware gate placement with a one-shot supernet for efficient weight sharing. A filtering mechanism further removes weak paths to narrow the search space. Experimental results show that the method reduces parameters by up to 76% while maintaining high accuracy.
Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Authors: Tengwei Song, Min Wu, Yuan Fang
Venue: CIKM
Year: 2025

Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailable or computationally expensive to generate. To overcome this limitation, we propose FlexMol, a flexible molecule pre-training framework that learns unified molecular representations while supporting single-modality input. Specifically, inspired by the unified structure in vision-language models, our approach employs separate models for 2D and 3D molecular data, leverages parameter sharing to improve computational efficiency, and utilizes a decoder to generate features for the missing modality. This enables a multistage continuous learning process where both modalities contribute collaboratively during training, while ensuring robustness when only one modality is available during inference. Extensive experiments demonstrate that FlexMol achieves superior performance across a wide range of molecular property prediction tasks, and we also empirically demonstrate its effectiveness with incomplete data. Our code and data are available at https://github.com/tewiSong/FlexMol.
ProxySampler: Proxy Informativeness Estimation for Efficient Data Selection in Active Learning
Authors: Miao-Hui Song, Lan Zhang, Mu Yuan, Yijun Liu
Venue: CIKM
Year: 2025

Large-scale data analysis services require efficient periodic model updates to adapt to the possibly changing data distributions. Manually labeling all available samples for task model updates is infeasible for a large sample scale. Active learning technique is proposed to iteratively select subsets of the most informative samples for labeling. From our experience of applying active learning in a real-world video analysis system, we identify a previously overlooked bottleneck of time cost: data selection. Existing active learning methods select data by estimating informativeness (e.g., output confidence) over all unlabeled samples in each iteration. This data selection process can take up to 42% of the time cost of end-to-end model updates in our system (totals include the time for manual labeling, data selection, and model updates.). To address the time cost bottleneck caused by data selection, we propose a new idea: proxy informativeness estimation. We start with modeling the time cost of data selection, from which we identify three key factors: unit estimation cost, the number of samples for estimation, and the number of iteration rounds. The influence of the first two factors increases cumulatively with the number of iteration rounds. Correspondingly, we design a proxy estimator and a sample pooling method, respectively. Our proxy estimator is a lightweight neural network for direct informativeness estimation to replace the role of the high-cost task model, thus reducing the unit cost. And, our sample pooling method leverages historical estimation results to narrow the scope of sample candidates. Based on the above design, we develop ProxySampler, which can be integrated with various active learning approaches as a plug-in. Experimental results show that integrating ProxySampler with state-of-the-art active learning methods can reduce the time cost by 53.6-83.3% (a 2.15-6.01x speedup) when achieving the same accuracy.
Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval
Authors: Jonghyun Song, Youngjune Lee, Gyu-Hwung Cho, Ilhyeon Song, Saehun Kim, Yohan Jo
Venue: CIKM
Year: 2025

Vision-Language Pretrained (VLP) models have achieved impressive performance on multimodal tasks, including text-image retrieval, based on dense representations. Meanwhile, Learned Sparse Retrieval (LSR) has gained traction in text-only settings due to its interpretability and efficiency with fast term-based lookup via inverted indexes. Inspired by these advantages, recent work has extended LSR to the multimodal domain. However, these methods often rely on computationally expensive contrastive pre-training, or distillation from a frozen dense model, which limits the potential for mutual enhancement. To address these limitations, we propose a simple yet effective framework that enables bi-directional learning between dense and sparse representations through Self-Knowledge Distillation. This bi-directional learning is achieved using an integrated similarity score-a weighted sum of dense and sparse similarities-which serves as a shared teacher signal for both representations. To ensure efficiency, we fine-tune the final layer of the dense encoder and the sparse projection head, enabling easy adaptation of any existing VLP model. Experiments on MSCOCO and Flickr30k demonstrate that our sparse retriever not only outperforms existing sparse baselines, but also achieves performance comparable to-or even surpassing-its dense counterparts, while retaining the benefits of sparse models.
PRECISE: Pre-training and Fine-tuning Sequential Recommenders with Collaborative and Semantic Information
Authors: Chonggang Song, Chunxu Shen, Hao Gu, Yaoming Wu, Lingling Yi, Jie Wen, Chuan Chen
Venue: CIKM
Year: 2025

Recommendation platforms commonly offer diverse content scenarios for users to interact with. Pre-training models are the most commonly used approach in recommendation systems to capture users' full-domain interests. Traditional ID-based pre-training models mainly capture user interests by leveraging collaborative signals. However, a prevalent drawback of those systems is the incapacity to handle cold-start scenarios. With the recent advent of large language models, there has been a significant increase in research efforts exploiting LLMs to extract semantic information for items. However, text-based recommendations highly rely on elaborate feature engineering and often fail to capture collaborative similarities. To overcome these limitations, we propose a novel pre-training and fine-tuning framework for sequential recommendation, termed Precise. Precise employs a pre-training framework that models users' comprehensive interests across all recommendation scenarios combining collaborative signals with semantic information. To address rapid shifting data distributions in recommendation scenarios, we further propose a fine-tuning phase tailored to specific target scenarios/tasks, thereby achieving efficient industrial deployment while maintaining fast responsiveness. Additionally, we introduce practical training strategies to enhance the model's performance in real-world applications. Empirical findings reveal that the Precise framework attains outstanding performance in both offline experiments and online A/B tests. Precise has been fully deployed in multiple online recommendation scenarios in WeChat.
Quantized Factor Identifiable Causal Effect Variational Autoencoder
Authors: Sujeong Song, Junghyo Sohn, Eunsong Kang, Heung-Il Suk
Venue: CIKM
Year: 2025

Causal inference involves determining how interventions affect outcomes and explaining the underlying mechanisms, and it holds critical importance across various fields. A key assumption in causal inference is that the measured covariates form a sufficient adjustment set. However, this assumption often fails due to unobserved confounders, as confounding mechanisms are rarely fully captured by measured covariates alone. Recent research has attempted to address this challenge using variational autoencoders (VAEs), but these approaches face practical limitations, including unidentifiability and bias toward proxy variables. To overcome these issues, we propose a novel method that incorporates quantized factor identifiability into VAEs for causal effect estimation. This integration mitigates unidentifiability and reduces the dominance of proxy variables, thereby enhancing consistency and accuracy in causal inference. Extensive experiments on both simulated and real-world datasets demonstrate the robustness and effectiveness of our method, establishing a new benchmark in deep causal modeling.
Parse-LLM: A Prior-Free LLM Parser for Unknown System Logs
Authors: Chengyu Song, Lin Yang, Jianming Zheng, Jinzhi Liao, Feng Yang, Linru Ma, Fei Cai
Venue: CIKM
Year: 2025

Log parsing extracts structured information from unstructured logs and serves as a fundamental pre-processing step for various log-based analytics and monitoring tasks. Recent advances have leveraged Large Language Models (LLMs) to handle log format complexities and enhance parsing performance. However, these methods heavily rely on labeled data, which is often scarce in rapidly evolving industrial systems, limiting their applicability in real-world scenarios. Moreover, the sheer volume of logs results in slow parsing and high computational costs, further hindering the deployment of LLM-based log parsing systems. To address these issues, we propose Parse-LLM, an unsupervised end-to-end log parsing framework based on LLMs Specifically, we first developed a Log Decomposer Agent that leverages Chain-of-Thought (CoT) reasoning and callable tools, enabling the LLM to autonomously separate log headers from content. Next, we introduce the Hybrid Log Partition module, which segments logs by balancing commonalities and differences. Finally, we developed a novel Variation-aware Log Parsing module that allows the LLM to harness additional supervisory signals through comparative analysis of similar logs. Comprehensive experiments conducted on large-scale public datasets show that Parse-LLM outperforms state-of-the-art log parsers in an unsupervised setting, offering an effective and scalable solution for the practical application of unsupervised log parsing.
When Facts Expire: Benchmarking Temporal Validity in Knowledge Graphs
Authors: Thibaut Soulard, Fatiha Saïs, Joe Raad
Venue: CIKM
Year: 2025

Knowledge Graphs (KGs) are essential in applications like semantic search, question answering, and decision support. They structure knowledge, validate facts, enable inference, and increasingly enhance Large Language Models (LLMs) by grounding outputs in structured, factual data. However, KGs have treated facts as static and timeless, ignoring the temporal nature of many truths. This leads to outdated or incorrect inferences. Temporal Knowledge Graphs (TKGs), like Wikidata and YAGO, address this by modeling the time-bound validity of facts. Multiple recent work has focused on predicting missing temporal facts, yet validating existing temporal information, to ensure the reliability and accuracy of TKGs, remains underexplored. In order to advance this area of research, we introduce the first benchmark designed to evaluate temporal fact validation methods. Derived from Wikidata, this benchmark supports systematic, quantitative, and qualitative comparisons, incorporating diverse assumptions about temporal data (e.g., timestamps, intervals) and KG structures (e.g., density, depth).
The Temporal Game: A New Perspective on Temporal Relation Extraction
Authors: Hugo O. Sousa, Ricardo Campos, Alípio Jorge
Venue: CIKM
Year: 2025

In this paper we demo the Temporal Game, a novel approach to temporal relation extraction that casts the task as an interactive game. Instead of directly annotating interval-level relations, our approach decomposes them into point-wise comparisons between the start and end points of temporal entities. At each step, players classify a single point relation, and the system applies temporal closure to infer additional relations and enforce consistency. This point-based strategy naturally supports both interval and instant entities, enabling more fine-grained and flexible annotation than any previous approach. The Temporal Game also lays the groundwork for training reinforcement learning agents, by treating temporal annotation as a sequential decision-making task. To showcase this potential, the demo presented in this paper includes a Game mode, in which users annotate texts from the TempEval-3 dataset and receive feedback based on a scoring system, and an Annotation mode, that allows custom documents to be annotated and resulting timeline to be exported. Therefore, this demo serves both as a research tool and an annotation interface. The demo is publicly available at https://temporal-game.inesctec.pt, and the source code is open-sourced to foster further research and community-driven development in temporal reasoning and annotation.
Compare: A Framework for Scientific Comparisons
Authors: Moritz Staudinger, Wojciech Kusa, Matteo Cancellieri, David Pride, Petr Knoth, Allan Hanbury
Venue: CIKM
Year: 2025

Navigating the vast and rapidly increasing sea of academic publications to identify institutional synergies, benchmark research contributions and pinpoint key research contributions has become an increasingly daunting task, especially with the current exponential increase in new publications. Existing tools provide useful overviews or single-document insights, but none supports structured, qualitative comparisons across institutions or publications. To address this, we demonstrate Compare, a novel framework that tackles this challenge by enabling sophisticated long-context comparisons of scientific contributions. Compare empowers users to explore and analyze research overlaps and differences at both the institutional and publication granularity, all driven by user-defined questions and automatic retrieval over online resources. For this we leverage on Retrieval-Augmented Generation over evolving data sources to foster long context knowledge synthesis. Unlike traditional scientometric tools, Compare goes beyond quantitative indicators by providing qualitative, citation-supported comparisons.
Towards Explainable Transaction Risk Analysis With Dual Graph Retrieval Augmented Generation
Authors: Liang Su, Mingyang Zhang, Kangxiang Jia, Tengfei Liu, Weiqiang Wang, Yun Xiong, Xixi Wu, Xinyu Gao, Yongrui Fu, Jiawei Zhang
Venue: CIKM
Year: 2025

Explainable transaction risk analysis is a challenge for traditional deep learning models, which only predict suspicious transactions without explanations. Current explainable methods rely on hand-crafted rules and lack the ability to automatically generate language-based explanations. Large Language Models (LLMs) offer promise due to their reasoning and text generation abilities but struggle with domain knowledge and hallucinations, making risk analysis difficult. Specifically, LLMs face: (1) insufficient adaptation to transaction data analysis, and (2) ineffective knowledge retrieval methods that ignore the rich graph structure of transaction data. To address these issues, we propose the Dual Graph Retrieval-Augmented Generation (Dual-gRAG) framework, which utilizes dual retrieval: expert knowledge and reasoning case retrieval. Expert knowledge compensates for domain gaps, while reasoning case retrieval provides step-wise analysis guidance. We incorporate both graph-structured features and semantic features into the retrieval process to enhance the effectiveness of the retrieval. Extensive experiments show that Dual-gRAG improves LLMs' risk analysis capabilities, achieving a 15% increase in different metrics.
DiRW: Path-Aware Digraph Learning for Heterophily
Authors: Daohan Su, Xunkai Li, Zhenjun Li, Yinping Liao, Rong-Hua Li, Guoren Wang
Venue: CIKM
Year: 2025

Recently, graph neural network (GNN) has emerged as a powerful representation learning tool for graph-structured data. However, most approaches are tailored for undirected graphs, neglecting the abundant information in the edges of directed graphs (digraphs). In fact, digraphs are widely applied in the real world and confirmed to address heterophily challenges. Despite recent advancements, existing spatial- and spectral-based DiGNNs have limitations due to their complex learning mechanisms and reliance on high-quality topology, resulting in low efficiency and unstable performance. To address these issues, we propose Directed Random Walk (DiRW), a plug-and-play strategy for most spatial-based DiGNNs and also an innovative model which offers a new digraph learning paradigm. Specifically, it utilizes a direction-aware path sampler optimized from the perspectives of walk probability, length, and number in a weight-free manner by considering node profiles and topologies. Building upon this, DiRW incorporates a node-wise learnable path aggregator for generalized node representations. Extensive experiments on 9 datasets demonstrate that DiRW: (1) enhances most spatial-based methods as a plug-and-play strategy; (2) achieves SOTA performance as a new digraph learning paradigm. The source code and data are available at https://github.com/dhsiuu/DiRW.
A Demonstration of PKGem: Secure Enrichment of Personal Knowledge Graphs
Authors: Junzhou Su, Sriram Nutulapati, Chang Ge
Venue: CIKM
Year: 2025

We present PKGem, a system that provides an end-to-end secure solution to enrich personal knowledge graphs in mobile environments. This task faces two core challenges: First, the proprietary, user-centric, and locally stored nature of personal knowledge graphs makes collaborative enrichment with socially connected peers a privacy concern. Moreover, the mobile environment has strict constraints on resource and computation cost, requiring lightweight and efficient design. PKGem addresses both challenges by leveraging cryptographic techniques to enable secure data enrichment across personal knowledge graphs, while remaining practical under mobile constraints. The system is implemented as an Android application and supports a variety of real-world usage scenarios. The code of PKGem is available at https://github.com/golden-eggs-lab/pkgem, with a demonstration video link included in the repository.