OpenRaiser/Intern-Atlas huggingface dataset:
CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors
Authors: Sri Durga Sai Sowmya Kadali, Evangelos E. Papalexakis
Venue: CIKM
Year: 2025

The widespread use of Large Language Models (LLMs) in many applications marks a significant advance in research and practice. However, their complexity and hard-to-understand nature make them vulnerable to attacks, especially jailbreaks designed to produce harmful responses. To counter these threats, developing strong detection methods is essential for the safe and reliable use of LLMs. This paper studies this detection problem using the Contextual Co-occurrence Matrix, a structure recognized for its efficacy in data-scarce environments. We propose a novel method leveraging the latent space characteristics of Contextual Co-occurrence Matrices and Tensors for the effective identification of adversarial and jailbreak prompts. Our evaluations show that this approach achieves a notable F1 score of 0.83 using only 0.5% of labeled prompts, which is a 96.6% improvement over baselines. This result highlights the strength of our learned patterns, especially when labeled data is scarce. Our method is also significantly faster, speedup ranging from 2.3 to 128.4 times compared to the baseline models.
GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning
Authors: Dan Kalifa, Uriel Singer, Kira Radinsky
Venue: CIKM
Year: 2025

Proteins are central to biological processes and indispensable for living organisms. Accurate representation of proteins is crucial, especially in drug development. Recent advances have applied machine learning for unsupervised protein representation learning. However, these approaches often focus solely on the amino acid sequence of proteins and lack factual knowledge about proteins and their interactions, thus limiting their performance. In this study, we present GOProteinGNN, a novel architecture that enhances protein language models by integrating protein knowledge graph information during the creation of amino acid level representations. Our approach allows for the integration of information at both the individual amino acid level and the entire protein level, enabling a comprehensive and effective learning process through graph-based learning. By doing so, we can capture complex relationships and dependencies between proteins and their functional annotations, resulting in more robust and contextually enriched protein representations. Unlike previous methods, GOProteinGNN uniquely learns the entire protein knowledge graph during training, which allows it to capture broader relational nuances and dependencies beyond mere triplets as done in previous work. We perform a comprehensive evaluation on several downstream tasks, demonstrating that GOProteinGNN consistently outperforms previous methods, showcasing its effectiveness and establishing it as a state-of-the-art solution for protein representation learning. We discuss the practical integration of GOProteinGNN in a laboratory setting for lipid nanoparticle-based drug delivery, aiming to bypass the blood-brain barrier and discover novel components, with positive results observed in mice.
Towards Equitable Coreset Selection: Addressing Challenges Under Class Imbalance
Authors: Liyana Sahir Kallooriyakath, Anugu Namratha Reddy, B. Srinath Achary, Ashutosh Sharma, Krisha Shah, Sonia Gupta, Siddhartha Asthana
Venue: CIKM
Year: 2025

Coreset selection reduces training cost by constructing compact, representative subsets, but existing methods largely assume balanced class distributions. Under imbalance, this assumption yields biased subsets that discard critical minority samples and degrade accuracy. We propose Equitable Coreset Selection (ECS), a framework tailored for imbalanced data. ECS mitigates these issues through adaptive pruning that preserves minority examples, class-sensitive partitioning aligned with skewed class distributions, and stratified graph-cut selection for diverse sampling. Experiments across multiple imbalanced datasets show that ECS improves generalization and substantially boosts minority-class accuracy compared to standard coreset methods.
Efficiency Boost in Decentralized Optimization: Reimagining Neighborhood Aggregation with Minimal Overhead
Authors: Durgesh Kalwar, Mayank Baranwal, Harshad Khadilkar
Venue: CIKM
Year: 2025

In today's data-sensitive landscape, distributed learning emerges as a vital tool, not only fortifying privacy measures but also streamlining computational operations. This becomes especially crucial within fully decentralized infrastructures where local processing is imperative due to the absence of centralized aggregation. Here, we introduce DYNAWEIGHT, a novel framework to information aggregation in multi-agent networks. DYNAWEIGHT offers substantial acceleration in decentralized learning with minimal additional communication and memory overhead. Unlike traditional static weight assignments, such as Metropolis weights, DYNAWEIGHT dynamically allocates weights to neighboring servers based on their relative losses on local datasets. Consequently, it favors servers possessing diverse information, particularly in scenarios of substantial data heterogeneity. Our experiments on various datasets MNIST, CIFAR10, and CIFAR100 incorporating various server counts and graph topologies, demonstrate notable enhancements in training speeds. Notably, DYNAWEIGHT functions as an aggregation scheme compatible with any underlying server-level optimization algorithm, underscoring its versatility and potential for widespread integration.
Causal Effect Variational Transformer for Public Health Measures and COVID-19 Infection Cluster Analysis
Authors: Jinho Kang, Sungjun Lim, Hojun Park, Jiyoung Jung, Jaehun Jung, Kyungwoo Song
Venue: CIKM
Year: 2025

Recent research increasingly integrates causal inference into deep learning models to enhance the explainability and robustness of medical applications. However, data scarcity remains a fundamental challenge due to privacy constraints and the high cost of data collection. This issue, compounded by complex variable dependencies and unobserved latent confounders, hinders the reliable estimation of causal effects. To address these challenges, we collect two real-world COVID-19 infection cluster datasets, including public health measures, from distinct distributions in collaboration with local governments, a medical university, and a hospital. We also propose a cut-off augmentation method that generates diverse feature-label pairs by slicing time-series sequences at different observation windows, effectively simulating partial observations common in real-world settings. We further introduce the Causal Effect Variational Transformer (CEVT), a Transformer-based model that captures temporal structure and addresses the difficulty of causal estimation under scarce data, complex dependencies, and latent confounding by modeling multiple treatments through an iterative conditioning mechanism. We validate the causal modeling capability of CEVT on synthetic datasets and demonstrate that, on two distinct COVID-19 datasets, it consistently outperforms baselines in infection prediction. Notably, the causal effects estimated by CEVT converge with findings from medical studies on infection control, reinforcing its reliability and underscoring its potential to inform public health decision-making.
When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering
Authors: Eojin Kang, Juae Kim
Venue: CIKM
Year: 2025

Multilingual large language models (LLMs) offer promising opportunities for cross-lingual information access, yet their use of factual knowledge remains highly sensitive to the input language. Prior work has addressed this through English prompting and evaluation, assuming that English-based reasoning is universally beneficial. In this work, we challenge that assumption by exploring factual knowledge transfer from non-English to English through the lens of Language and Thought Theory. We introduce Language-to-Thought (L2T) prompting, which aligns the model's internal ''thinking'' language with the source of knowledge. Across three languages and four models, L2T consistently outperforms English-based reasoning, reversing the expected advantage of English prompts. Our code is available at https://github.com/GeomeunByeol/Language2Thought.
Sarcasm Subtype-Specific Reasoning in Dialogue with Multimodal Cues Using Large Language Models
Authors: Choongwon Kang, Wonbyung Lee, Seunghyun Hwang, Sunho Tae, Seungjong Sun, Jang Hyun Kim
Venue: CIKM
Year: 2025

Sarcasm is a nuanced form of human communication characterized by a mismatch between an utterance and the speaker's intent or contextual cues. Recent studies have aimed to advance the understanding of sarcasm by developing systems capable of generating rationales behind sarcastic expressions. In particular, multimodal cues such as facial expressions and vocal tone have been crucial indicators when semantic incongruity with the utterance is prominent. However, existing multimodal sarcasm reasoning approaches fall short of providing fine-grained explanations. Sarcasm can be further categorized into specific subtypes based on the forms of inversion it employs, such as nonverbal cues, dialogue context, and exaggerated word emphasis. To address this, we introduce a novel task called Sarcasm Subtype-specific Reasoning Generation (SSRG). To facilitate research on this task, we present the Sarcasm Subtype-specific Reasoning Dataset (SSRD), which establishes a new benchmark for fine-grained sarcasm reasoning. Through extensive experiments, we demonstrate that leveraging multimodal cues significantly enhances subtype-specific sarcasm reasoning. Moreover, we show that integrating these multimodal cues into textual representations enables strong performance even when using only large language models (LLMs).
Curriculum Guided Personalized Subgraph Federated Learning
Authors: Minku Kang, Hogun Park
Venue: CIKM
Year: 2025

Subgraph Federated Learning (FL) aims to train Graph Neural Networks (GNNs) across distributed private subgraphs, but it suffers from severe data heterogeneity. To mitigate data heterogeneity, weighted model aggregation methods personalize each local GNN by assigning larger weights to parameters from clients with similar subgraph characteristics inferred from their current model states. However, the sparse and biased subgraphs often trigger rapid overfitting, causing the estimated client similarity matrix to stagnate or even collapse. As a result, the aggregation loses effectiveness as clients reinforce their own biases instead of exploiting diverse knowledge otherwise available. To this end, we propose a novel personalized subgraph FL framework called Curriculum Guided Personalized SUbgraph Federated Learning (CUFL). On the client side, CUFL adopts Curriculum Learning (CL) that adaptively selects edges for training according to their reconstruction scores, exposing each GNN first to easier, generic cross-client substructures and only later to harder, client-specific ones. This paced exposure prevents early overfitting to biased patterns and enables gradual personalization. By regulating personalization, the curriculum also reshapes server aggregation from exchanging generic knowledge to propagating client-specific knowledge. Further, CUFL improves weighted model aggregation by estimating client similarity using fine-grained structural indicators reconstructed on a random reference graph. Extensive experiments on six benchmark datasets confirm that CUFL achieves superior performance compared to relevant baselines. Code is available at https://github.com/Kang-Min-Ku/CUFL.git.
CausalBench-ER: Causally-Informed Explanations and Recommendations for Reproducible Benchmarking
Authors: Ahmet Kapkiç, Pratanu Mandal, Abhinav Gorantla, Shu Wan, Ertugrul Çoban, Paras Sheth, Huan Liu, K. Selçuk Candan
Venue: CIKM
Year: 2025

Due to the critical role causality plays in decision-making, the state of-the-art in machine learning for causality is rapidly evolving. With rapid development and deployment of new models, datasets, and metrics, it is increasingly difficult for researchers and practitioners to identify the most suitable approach for their problem. Models exhibit different performances when they train on different data or even when they are used under different hardware/software platforms, making it challenging for users to select the appropriate setup pertinent to their problem. To address these difficulties, we present a computing framework, CausalBench-ER that serves, not only as a benchmarking platform for causal machine learning models, but also as a resource that can explain benchmarking results across different metrics, software, and hardware setups. Furthermore, CausalBench-ER recommends additional scenarios to consider to help pave the way towards more robust benchmarking.
Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation
Authors: Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang, Zijun Yao
Venue: CIKM
Year: 2025

Medical ontology graphs map external knowledge to medical codes in electronic health records (EHRs) via structured relationships. By leveraging domain-approved connections (e.g., parent-child), predictive models can generate richer medical concept representations by incorporating contextual information from related concepts. However, existing literature primarily focuses on incorporating domain knowledge from a single ontology system, or from multiple ontology systems (e.g., diseases, drugs, and procedures) in isolation, without integrating them into a unified learning structure. Consequently, concept representation learning often remains limited to intra-ontology relationships, overlooking cross-ontology connections that could enhance the richness of healthcare representations. In this paper, we propose LINKO, a large language model (LLM)-augmented integrative ontology learning framework that leverages multiple ontology graphs simultaneously by enabling dual-axis knowledge propagation both within and across heterogeneous ontology systems to enhance medical concept representation learning. Specifically, LINKO first employs LLMs to provide a graph-retrieval-augmented initialization for ontology concept embedding, through an engineered prompt that includes concept descriptions, and is further augmented with ontology graph relations and task-specific details. Second, our method jointly learns the medical concepts in diverse ontology graphs by performing knowledge propagation in two axes: (1) intra-ontology vertical propagation across hierarchical ontology levels and (2) inter-ontology horizontal propagation within every level in parallel. Last, through extensive experiments on two public datasets, we validate the superior performance of LINKO over state-of-the-art baselines. As a plug-in encoder compatible with existing EHR predictive models, LINKO further demonstrates enhanced robustness in scenarios involving limited data availability and rare disease prediction.
Reasoning over Incomplete Knowledge Graphs
Authors: Ola El Khatib
Venue: CIKM
Year: 2025

Incomplete knowledge graphs present a fundamental challenge for reliable multi-hop knowledge graph question answering (KGQA), causing reasoning failures when key factual triples are missing. While large language models (LLMs) offer strong reasoning capabilities for KGQA, they are prone to hallucinations and often assume complete knowledge graphs (KGs). This research identifies key bottlenecks in current LLM-KGQA pipelines caused by KG incompleteness. We propose targeted remedies, centered on integrating link prediction tools, to enhance performance in sparse KGs. We explore two main directions: (1) improving the robustness of LLM-KGQA methods under KG sparsity, and (2) leveraging advanced link prediction techniques to recover missing graph connections. Preliminary experiments on benchmark datasets demonstrate significant improvements in both answer accuracy and link prediction performance under simulated KG sparsity. These results bridge the gap between LLM-based reasoning and incomplete KGs, laying the foundation for more faithful and interpretable KGQA systems.
Datasets for Supervised Adversarial Attacks on Neural Rankers
Authors: Amir Khosrojerdi, Amin Bigdeli, Radin Hamidi Rad, Morteza Zihayat, Charles L. A. Clarke, Ebrahim Bagheri
Venue: CIKM
Year: 2025

We introduce a novel dataset for adversarial rank attacks against neural rankers, enabling systematic research on robustness. Unlike prior unsupervised or surrogate-based methods, our approach uses Retrieval-Augmented Generation (RAG) with a Large Language Model (LLM) to create high-quality adversarial examples that subtly alter rankings while maintaining coherence and relevance. Built via a self-refining LLM-Ranker feedback loop, the dataset includes two tiers: Gold and Diamond, based on attack strength, along with rich metadata, ranking labels, and quality metrics. Released with code and prompts, it supports training, evaluation, and benchmarking of robust ranking systems.
Online Activation Value-aware Clustering and Aggregation for Faithful Argumentative Explanations
Authors: Ungsik Kim, Jiho Bae, Sang-Min Choi, Suwon Lee
Venue: CIKM
Year: 2025

Argumentative explainable artificial intelligence employs argumentation theory to explain the mechanisms of machine learning. Previous approaches for explaining deep learning models collectively compressed layers via clustering. However, this resulted in accumulated information loss across layers, thereby degrading the fidelity of explanations. We propose online activation value-aware clustering and aggregation, a compression algorithm that preserves the inference structure of the original neural network with greater fidelity. The proposed method sequentially compresses each layer, immediately recalculates activation values following compression, and rectifies inter-layer information loss using a singular-value-scaled ridge alignment approach. To evaluate the effectiveness of the proposed method, we introduce four novel quantitative metrics. Input-output fidelity and structural fidelity measure how accurately the compressed model preserves the original model predictions and internal activations. Input-output perturbation consistency and structural perturbation consistency assess the similarity of the changes induced by Gaussian-perturbed input data. Experiments on three benchmark datasets (Breast Cancer, California Housing, and HIGGS) demonstrate that our method achieves performance improvements ranging from 12.9% to 53.7% across the four metrics, demonstrating significantly higher explanation fidelity than existing approaches.
Upcycling Candidate Tokens of Large Language Models for Query Expansion
Authors: Jinseok Kim, Sukmin Cho, Soyeong Jeong, Sangyeop Kim, Sungzoon Cho
Venue: CIKM
Year: 2025

Query Expansion (QE) improves retrieval performance by enriching queries with related terms. Recently, Large Language Models (LLMs) have been used for QE, but existing methods face a trade-off: generating diverse terms boosts performance but increases computational cost. To address this challenge, we propose Candidate Token Query Expansion (CTQE), which extracts diverse and relevant terms from a single LLM decoding pass by leveraging unselected candidate tokens. These tokens, though not part of the final output, are conditioned on the full query and capture useful information. By aggregating them, CTQE achieves both relevance and diversity without extra inference, reducing overhead and latency. Experiments show that CTQE delivers strong retrieval performance with significantly lower cost, outperforming or comparable to more expensive methods. Code is available at: https://github.com/bluejeans8/CTQE
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
Authors: Jongwoo Kim, SeongYeub Chu, Hyeongmin Park, Bryan Wong, Keejun Han, Mun Yong Yi
Venue: CIKM
Year: 2025

Recent advancements in heterogeneous GNNs have enabled significant progress in embedding nodes and learning relationships across diverse tasks. However, traditional methods rely heavily on meta-paths grounded in node types, which often fail to encapsulate the full complexity of node interactions, leading to inconsistent performance and elevated computational demands. To address these challenges, we introduce MF2Vec, a novel framework that shifts focus from rigid node-type dependencies to dynamically exploring shared facets across nodes, regardless of type. MF2Vec constructs multi-faceted paths and forms homogeneous networks to learn node embeddings more effectively. Through extensive experiments, we demonstrate that MF2Vec achieves superior performance in node classification, link prediction, and node clustering tasks, surpassing existing baselines. Furthermore, it exhibits reduced performance variability due to meta-path dependencies and achieves faster training convergence. These results highlight its capability to analyze complex networks comprehensively. The implementation of MF2Vec is publicly available at https://github.com/kimjongwoo-cell/MF2Vec.
Jailbreaking LLMs Through Cross-Cultural Prompts
Authors: Damin Kim, Minseok Hur, Jeongin Lee, Moohong Min
Venue: CIKM
Year: 2025

We examine how linguistic and cultural framing affect jailbreak success in three commercial LLMs (GPT-4, Claude 3, Gemini), using semantically equivalent prompts in direct, indirect, and metaphorical styles across four high-resource languages. Indirect prompts most effectively bypassed filters, with framing and style significantly influencing alignment. GPT-4 was especially vulnerable to indirect framing, Claude 3 remained consistently robust, and Gemini showed high sensitivity to cultural and linguistic variation. Our findings highlight the need for alignment strategies resilient to diverse expression styles and cultural contexts.
The 1st International Workshop on Retrieval-driven Generative AI & ScienceON AI Challenge: RDGENAI 2025
Authors: Eunhui Kim, Caren Han, KeyongTae Lim, Yesim Selcuk, Moonki Back, Sangjun Han, Kyong-Ha Lee
Venue: CIKM
Year: 2025

Retrieval augmented generation (RAG) has rapidly emerged as a cornerstone for building trustworthy and efficient generative AI systems, spanning from unimodal question answering to complex multimodal reasoning. The 1st international workshop on retrieval-driven generative AI gathers researchers and practitioners focused on the applied side of RAG-particularly for visually rich document understanding (VRDU) where text, layout, and images intertwine. New in 2025, this workshop is co located with the ScienceON AI Challenge, an open competition benchmarking the reliability of AI generated summaries over ScienceON's public OpenAPI search results. By combining a research workshop with a hands on challenge, we provide a complete pipeline from algorithms to real world evaluation. The half day event features invited keynotes, peer reviewed papers, challenge finalist talks, and a poster/demo session, catalyzing collaboration toward robust, explainable, and domain adapted generativeAI.