OpenRaiser/Intern-Atlas huggingface dataset:
X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents
Authors: Lin Tian, Xiuzhen Zhang, Maria Myung-Hee Kim, Jennifer Biggs, Marian-Andrei Rizoiu
Venue: CIKM
Year: 2025

State-sponsored trolls, malicious actors who deploy sophisticated linguistic manipulation in coordinated information campaigns, posing threats to online discourse integrity. While Large Language Models (LLMs) achieve strong performance on general natural language processing (NLP) tasks, they struggle with subtle propaganda detection and operate as ``black boxes'', providing no interpretable insights into manipulation strategies. This paper introduces X-Troll, a novel framework that bridges this gap by integrating explainable adapter-based LLMs with expert-derived linguistic knowledge to detect state-sponsored trolls and provide human-readable explanations for its decisions. X-Troll incorporates appraisal theory and propaganda analysis through specialized LoRA adapters, using dynamic gating to capture campaign-specific discourse patterns in coordinated information operations. Experiments on real-world data demonstrate that our linguistically-informed approach shows strong performance compared with both general LLM baselines and existing troll detection models in accuracy while providing enhanced transparency through expert-grounded explanations that reveal the specific linguistic strategies used by state-sponsored actors. X-Troll source code is available at: https://github.com/ltian678/xtroll_source/.
Selective Mixup for Debiasing Question Selection in Computerized Adaptive Testing
Authors: Mi Tian, Kun Zhang, Fei Liu, Jinglong Li, Yuxin Liao, Chenxi Bai, Zhengtao Tan, Le Wu, Richang Hong
Venue: CIKM
Year: 2025

Computerized Adaptive Testing is a widely used technology for evaluating examinees' proficiency in online education platforms. By leveraging prior estimates of proficiency to select questions and updating the estimates iteratively based on responses, it enables personalized examinee modeling and has attracted substantial attention. Despite this progress, most existing works focus primarily on improving proficiency estimation accuracy, while overlooking the selection bias inherent in the adaptive process. Selection bias arises because the question selection is strongly influenced by the estimated proficiency, such as assigning easier questions to examinees with lower proficiency and harder ones to examinees with higher proficiency. Since the selection depends on prior estimation, this bias propagates into the diagnostic model, which is further amplified during iterative updates, leading to misaligned and biased predictions. Moreover, the imbalance in examinees' historical interactions often exacerbates bias in diagnostic models. To address this issue, we propose a debiasing framework consisting of two key modules: Cross-Attribute Examinee Retrieval and Selective Mixup-based Regularization. First, we retrieve balanced examinees with relatively even distributions of correct and incorrect responses and use them as neutral references for biased examinees. Then, Mixup is applied between each biased examinee and its matched balanced counterpart under label consistency. This augmentation enriches the diversity of bias-conflicting samples and smooths selection boundaries. Finally, extensive experiments on two benchmark datasets with multiple advanced diagnosis models have been conducted. The results demonstrate that our method substantially improves the generalization ability of question selection.
CoHN: Context-Aware Hawkes Graph Network for Temporal Knowledge Graph Reasoning
Authors: Xiaowei Tian, Xiaoyan Zhang, Xiaofeng Du, Tianbo Lu
Venue: CIKM
Year: 2025

Temporal Knowledge Graphs (TKGs) model dynamic events, and understanding temporal evolution is crucial for effective reasoning. While existing methods leverage Graph Neural Networks (GNNs) to model structural dependencies, they often rely on Recurrent Neural Networks (RNNs) to process sequence of graph structures. They struggle to (1) incorporate contextual information, (2) explicitly model long-term effects, and (3) handle in-equidistant data. To address these challenges, we propose Context-Aware Hawkes Graph Network (CoHN), a novel TKG reasoning approach based on the Hawkes process. CoHN features a tailored conditional intensity function that models TKG event occurrences. It is characterized by two additive terms: base intensity and historical influence, representing spontaneous tendency and influence of past events at in-equidistant time intervals. Firstly, we design a Contextual Encoder (CE) to encode contextual information for all entities and compute the base intensity. We then present an attention-based Evolutionary Encoder that captures local structural information and explicitly models long-term dependencies across the TKG. A self-exciting fusion module further aggregates historical evolutionary dependencies at all timestamps to quantify the final historical influence. Extensive experiments on common benchmarks demonstrate the superiority, robustness, and efficiency of our method.
Locale-Aware Product Type Prediction for E-commerce Search Queries
Authors: Anna Tigunova, Thomas Ricatte, Ghadir Eraisha
Venue: CIKM
Year: 2025

Search query understanding (QU) is an important building block of the modern e-commerce search engines. QU extracts multiple intents from customer queries, including intended color, brand, etc. One of the most important tasks in QU is predicting which product category the user is interested in. In our work we are tapping into query product type classification (Q2PT) task. Compared to classification of full-fledged texts, Q2PT is more complicated because of the ambiguity of short search queries, which is aggravated by language and cultural differences in worldwide online stores. Moreover, the span and variety of product categories in modern marketplaces pose a significant challenge. We focus on Q2PT inference in the global multi-locale e-commerce markets, which need to deliver high quality user experience in both large and small local stores alike. The common approach of training Q2PT models for each locale separately shows significant performance drops in low-resource stores and prevents from easily expanding to a new country, where the Q2PT model has to be created from scratch. We use transfer learning to address this challenge, augmenting low-resource locales through the vast knowledge of the high-resource ones. We introduce a unified, locale-aware Q2PT model, sharing training data and model structure across worldwide stores. We show that the proposed unified locale-aware Q2PT model has superior performance over the alternatives by conducting extensive quantitative and qualitative analysis on the large-scale multilingual e-commerce dataset across 20 worldwide locales. Our online A/B tests have shown that using locale-aware model improves over the previous user experience, increasing customer satisfaction.
Frozen in the Middle: Hidden States Remain Unchanged Across Intermediate Layers of Language Models
Authors: Pavel Tikhonov, Dmitry Ilvovsky
Venue: CIKM
Year: 2025

This paper investigates the internal mechanisms of large language models (LLMs) through the lens of Mechanistic Interpretability (MI). We present novel findings on how information is processed and propagated within these models. Our key contributions include: (1) providing evidence for the localized nature of fact storage and information propagation from subject tokens; (2) introducing a new observation that hidden states remain largely unchanged across multiple middle layers, which we call the ''plateau'' phenomenon; and (3) developing a manually crafted diagnostic dataset of factual prompts. Our work complements and extends prior research on transformer information flow by demonstrating that, contrary to the prevailing assumption of sequential representation enrichment across layers, subject token states stabilize early and remain functionally static throughout multiple middle layers while containing all necessary information for the final prediction. These insights advance our understanding of how transformers process factual information and suggest a more complex pattern of layer specialization than previously identified.
DESERE: The 2nd Workshop on Decentralized Search and Recommendation
Authors: Thanassis Tiropanis, George Roussos, Mohammad Bahrani, Mohamed Ragab
Venue: CIKM
Year: 2025

The growing demand for data ownership and privacy is reshaping how information is accessed, managed, integrated, and recommended. Building on the inaugural DESERE workshop at The Web Conference 2024, this second edition advances research on Decentralised Search and Recommendation platforms such as Personal Online Datastores (PODs), where users retain control of their data and explicitly manage permissions. As ecosystems decentralise, traditional information retrieval must be revisited while standards for new techniques and system designs are developed to ensure efficient, accurate, and privacy-preserving search. The Second DESERE workshop at CIKM 2025 focuses on infrastructures and retrieval algorithms for user-controlled data. It convenes a cross-disciplinary community spanning data retrieval, management and integration, semantic technologies, recommendation systems, privacy-aware computing, and search efficiency to explore approaches that prioritize user agency, data ownership, and scalable retrieval across PODs and related architectures. Through paper presentations, panels, and interactive sessions, the workshop will highlight challenges, opportunities, and solutions for privacy-preserving IR. These discussions are especially relevant to domains where user-centric design and data stewardship are critical-such as personal finance, education, and high-stakes areas like criminal justice and health.
GeoIndia V2: A Unified Graph and Language Model for Context-Aware Geocoding
Authors: Arpit Tiwari, Bhavuk Singhal, Anshu Aditya, Shubham Jain, Debashis Mukherjee, Debdoot Mukherjee
Venue: CIKM
Year: 2025

Geocoding in India presents unique challenges due to the unstructured, multilingual and diverse nature of its address systems. While recent advances in geospatial AI have explored the combination of spatial and semantic cues, existing methods often fall short in effectively integrating both dimensions for robust address resolution. In this work, we propose GeoIndia-V2, an enhanced version of GeoIndia [21], that unifies geospatial and semantic modeling through a novel fusion framework. Our unified model combines the Graphormer architecture [27] and a Pre-trained Transformer based Language Model (PTLM) that is trained from scratch on proprietary Indian address data, using our proposed Key Modulated Cross-Attention (KMCA) mechanism. KMCA enables deep cross-modal interaction between geospatial topology and linguistic structure and allows the model to reason contextually across both geographic and textual dimention, effectively handling the semantic intricacies of Indian addresses-including colloquial usage, inconsistent formatting, and multilinguality. We leverage last-mile e-commerce delivery data to construct a fine-grained graph of neighbourhood connectivity, enabling Graphormer to capture rich spatial relationships. Unlike prior methods that rely on self-loops, we generate graphs dynamically at inference time to exploit Graphormer's topological strength. Additionally, we introduce a generative decoding strategy for predicting hierarchical H3 cells. https://www.uber.com/en-IN/blog/h3/, moving beyond conventional bit-wise classification approaches. To the best of our knowledge, this is the first method to explicitly fuse graph-based geospatial learning with language-driven semantic modeling via cross-attention in the Indian geocoding context. Our approach significantly outperforms existing solutions and marks a substantial advancement toward building scalable real-world geocoding systems for complex address ecosystems like India.
A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models
Authors: Yu Tokutake, Kazushi Okamoto, Kei Harada, Atsushi Shibata, Koki Karube
Venue: CIKM
Year: 2025

Serendipity in recommender systems (RSs) has attracted increasing attention as a concept that enhances user satisfaction by presenting unexpected and useful items. However, evaluating serendipitous performance remains challenging because its ground truth is generally unobservable. The existing offline metrics often depend on ambiguous definitions or are tailored to specific datasets and RSs, thereby limiting their generalizability. To address this issue, we propose a universally applicable evaluation framework that leverages large language models (LLMs) known for their extensive knowledge and reasoning capabilities, as evaluators. First, to improve the evaluation performance of the proposed framework, we assessed the serendipity prediction accuracy of LLMs using four different prompt strategies on a dataset containing user-annotated serendipitous ground truth and found that the chain-of-thought prompt achieved the highest accuracy. Next, we re-evaluated the serendipitous performance of both serendipity-oriented and general RSs using the proposed framework on three commonly used real-world datasets, without the ground truth. The results indicated that there was no serendipity-oriented RS that consistently outperformed across all datasets, and even a general RS sometimes achieved higher performance than the serendipity-oriented RS.
iMask: Towards a Smart Mask Network Prototype for Monitoring Respiratory Viruses
Authors: Emma Tong, Cynthia Smyser, Chen Chen
Venue: CIKM
Year: 2025

Although the impact of COVID-19 pandemic has largely withered away over the years, the enduring presence of face masks continues to linger within our society. Still today, they're a familiarity, a crutch to fall back upon when sickness makes its rounds. However, it is often the case that we wear masks when they are not necessary, and, more concerningly, fail to wear them when they are truly necessary. Inefficient viral tracking methods further exacerbate the issue as they often do not alert the public and healthcare officials to oncoming or currently happening outbreaks fast enough. To combat these issues, new smart masks have been developed recently, equipped with biosensors to detect respiratory viruses in the air. However, these masks have their own drawbacks, including limited detection accuracy, detection scope and beneficial population. In response, this work presents the first of its kind prototype named iMask to augment current smart masks. By connecting the smartphone of the mask wearer to the Internet, iMask improves the detection accuracy. At its core, iMask employs multi-variate time series (1) imputation algorithms to alleviate the data scarcity issue and (2) forecasting algorithms to predict future viral levels. Based on shared, imputed and forecast viral data, iMask further leverages map services to create a viral concentration map that significantly expands the beneficiaries.
Densest Subgraph Discovery on Decentralized Graphs with Local Edge Differential Privacy
Authors: Wenping Tong, Yi Zhou, Yanhao Wang, Cen Chen, Minghao Zhao
Venue: CIKM
Year: 2025

Various real-world graphs, such as social and transaction networks, are typically distributed across users, each of whom holds a local view of the graph (i.e., their own relationships with others). Densest Subgraph Discovery (DSD) on such decentralized graphs is a fundamental task that can uncover valuable insights for downstream applications, including fraud detection, community identification, and user behavior mining. Additionally, in many scenarios, due to privacy concerns, sensitive original local views cannot be collected for DSD. Although there have been extensive studies on DSD, most existing algorithms either do not take user privacy into account or are specific to the centralized privacy setting that requires a (trusted) curator to collect all local views from users and then analyze the entire graph privately. To address these issues, we consider DSD under Local Edge Differential Privacy (LEDP), which allows users to perturb their local graph views via a randomizer such that any single edge is indistinguishable from the data transmitted to the server. We propose a new LEDP algorithm for DSD that utilizes the Randomized Response (RR) mechanism for user-side perturbation and extends greedy peeling with degree correction to find the densest subgraph on the server-side noisy global graph. Our proposed algorithm provides provable privacy and approximation guarantees. Finally, we perform experimental evaluations on real-world graphs to show that our proposed algorithm achieves better privacy-utility trade-offs than state-of-the-art LEDP baselines for DSD.
SupLID: Geometrical Guidance for Out-of-Distribution Detection in Semantic Segmentation
Authors: Nimeshika Udayangani, Sarah M. Erfani, Christopher Leckie
Venue: CIKM
Year: 2025

Out-of-Distribution (OOD) detection in semantic segmentation aims to localize anomalous regions at the pixel level, advancing beyond traditional image-level OOD techniques to better suit real-world applications such as autonomous driving. Recent literature has successfully explored the adaptation of commonly used image-level OOD methods-primarily based on classifier-derived confidence scores (e.g., energy or entropy)-for this pixel-precise task. However, these methods inherit a set of limitations, including vulnerability to overconfidence. In this work, we introduce SupLID, a novel framework that effectively guides classifier-derived OOD scores by exploiting the geometrical structure of the underlying semantic space, particularly using Linear Intrinsic Dimensionality (LID). While LID effectively characterizes the local structure of high-dimensional data by analyzing distance distributions, its direct application at the pixel level remains challenging. To overcome this, SupLID constructs a geometrical coreset that captures the intrinsic structure of the in-distribution (ID) subspace. It then computes OOD scores at the superpixel level, enabling both efficient real-time inference and improved spatial smoothness. We demonstrate that geometrical cues derived from SupLID serve as a complementary signal to traditional classifier confidence, enhancing the model's ability to detect diverse OOD scenarios. Designed as a post-hoc scoring method, SupLID can be seamlessly integrated with any semantic segmentation classifier at deployment time. Our results demonstrate that SupLID significantly enhances existing classifier-based OOD scores, achieving state-of-the-art performance across key evaluation metrics, including AUR, FPR, and AUP. Code is available at https://github.com/hdnugit/SupLID.
Neural Instrumented Factorization: Learning Dynamic Asset Pricing Factors and Loadings through Characteristics Control
Authors: Ajim Uddin
Venue: CIKM
Year: 2025

Asset pricing theory rests on the principle that differences in expected returns across assets are driven by their exposures to systematic risk factors. Identifying the ''right'' factors-whether observable or latent-remains a central challenge in empirical finance. Traditional latent factor models offer a parsimonious framework for summarizing information from hundreds of observable firm characteristics; however, they are typically estimated solely from return matrices, which limits their ability to capture time-varying, firm-specific dynamics. This study proposes a novel framework---Neural Instrumented Factorization (NeurIF)---that leverages firm characteristics as instruments to learn economically meaningful and time-varying latent factors. NeurIF integrates spatial and temporal attention mechanism to capture nonlinear relationships between firm characteristics and asset returns, jointly learning both the latent factors and their dynamic loadings. The model incorporates orthogonality constraints and deviation-based penalties to ensure the interpretability and alignment of latent factors with observed firm characteristics. Empirical evaluations on real-world asset pricing data reveal that NeurIF consistently outperforms several state-of-the-art transformer based models in return prediction, with improvements ranging from 1% to 18% in test data. Furthermore, the learned factor loadings can generate statistically significant long-short portfolio returns and are not subsumed by other observable factors. The embedded latent factors also exhibit strong explanatory power across several cross-sectional asset pricing anomalies, highlighting their economic relevance and robustness.
ReportGRI: Automating GRI Alignment and Report Assessment
Authors: Aida Usmanova, Rana Abdullah, Debayan Banerjee, Markus Leippold, Ricardo Usbeck
Venue: CIKM
Year: 2025

Organisations disclose their sustainability performance in corporate sustainability reports (CSRs). CSRs vary widely in structure and depth depending on the reporting framework. Such disparity, together with report complexity and volume, poses significant challenges to transparency, comparability and standardisation. To address this problem, we introduce ReportGRI, an automated system for Global Reporting Initiative (GRI) indexing and qualitative assessment of CSRs. The interactive framework leverages information retrieval techniques and zero-shot prompting to enable GRI disclosure-based report indexing and report coverage assessment by visualising well-covered topics and reporting gaps. The tool facilitates scalable and explainable benchmarking of Environmental, Social and Governance (ESG) reporting quality, enhancing report interpretation, transparency, and corporate accountability. The system is open-sourced on GitHub with an introduction video