OpenRaiser/Intern-Atlas huggingface dataset:
Guess the Age of Photos: An Interactive Web Platform for Historical Image Age Estimation
Authors: Hasan Yücedag, Adam Jatowt
Venue: CIKM
Year: 2025

This paper introduces Guess the Age of Photos, a web platform engaging users in estimating the years of historical photographs through two gamified modes: Guess the Year (predicting a single image's year) and Timeline Challenge (comparing two images to identify the older). Built with Python, Flask, Bootstrap, and PostgreSQL, it uses a 10,150-image subset of the Date Estimation in the Wild dataset (1930-1999). Features like dynamic scoring and leaderboards boost engagement. Evaluated with 113 users and 15,473 gameplays, the platform earned a 4.25/5 satisfaction rating. Users excelled in relative comparisons (65.9% accuracy) over absolute year guesses (25.6% accuracy), with older decades easier to identify. The platform serves as an educational tool, fostering historical awareness and analytical skills via interactive exploration of visual heritage. Furthermore, the platform provides a valuable resource for studying human perception of temporal cues in images and could be used to generate annotated data for training and evaluating computer vision models.
Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval
Authors: Jungmin Yun, Youngbin Kim
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have been increasingly employed for query expansion. However, their generative nature often undermines performance on complex multi-hop retrieval tasks by introducing irrelevant or noisy information. To address this challenge, we propose DeCoR (Decompose and Compress for Retrieval), a framework grounded in structured information refinement. Rather than generating additional content, DeCoR strategically restructures the query's underlying reasoning process and distills supporting evidence from retrieved documents. It consists of two core components tailored to the challenges of multi-hop retrieval: (1) Query Decomposition, which decomposes a complex query into explicit reasoning steps, and (2) Query-aware Document Compression, which synthesizes dispersed evidence from candidate documents into a concise summary relevant to the query. This structured design ensures that the final query representation remains both robust and comprehensive. Experimental results demonstrate that, despite utilizing a relatively small LLM, DeCoR outperforms strong baselines that rely on larger models. This finding underscores that, in complex retrieval scenarios, sophisticatedly leveraging the reasoning and summarization capabilities of LLMs offers a more efficient and effective solution than relying solely on their generative capability.
Robust Handwritten Text Recognition via Multi-Source Adversarial Domain Adaptation for Low-Resource Scripts
Authors: Bustami Yusuf, Hen-Hsen Huang
Venue: CIKM
Year: 2025

Transformer-based Optical Character Recognition (OCR) models perform well on common tasks but struggle to generalize to low-resource settings, handwritten text, or diverse scripts such as Jawi. Traditional adaptation methods often require labeled target data or fall short under severe domain shifts. We tackle this challenge with Unsupervised Multi-Source Heterogeneous Domain Adaptation (UMSHDA) for Handwritten Text Recognition (HTR). We adapt Domain-Adversarial Neural Networks (DANN) and Prototype Learning for Adversarial Domain Adaptation (PLADA) within a Transformer-based OCR architecture. We introduce two novel multi-adversarial adaptation strategies: (1) an Additive strategy that jointly optimizes a domain classification loss and a prototype-based alignment loss, and (2) an Integrative strategy that uses a prototype-driven adversarial signal to augment the domain classifier with semantic constraints. Experimental results show that our methods significantly outperform non-adaptive and DANN baselines on challenging handwriting tasks. For Jawi handwriting, our models achieve a relative error reduction of up to 22%. We also demonstrate superior cross-script generalization to German handwriting, achieving near-perfect performance, with a CER reduced by more than 50% relative to the DANN baseline. This work demonstrates that tailored multi-adversarial domain adaptation can effectively bridge significant domain gaps, enabling robust recognition accuracy for complex, low-resource HTR.
Ultra Fast Warm Start Solution for Graph Recommendations
Authors: Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov
Venue: CIKM
Year: 2025

In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the simple yet effective low-rank approximation approach to the graph-based model. Our method delivers instantaneous recommendations that are up to 30 times faster than conventional methods, with gains in recommendation quality, and demonstrates high scalability even on the large catalogue datasets.
Maneno Yetu: Dynamic Corpus Construction and Pretraining for Swahili NLP
Authors: Chaddy Anthony Zawuya, Alfred Malengo Kondoro, Diana Rwegasira, Juma H. Lungo
Venue: CIKM
Year: 2025

Swahili occupies a central place in African linguistic landscapes, yet it is significantly under-resourced in NLP, reflecting a mismatch between speaker population and data availability. We introduce Maneno Yetu, a dynamic and extensible corpus designed to address this gap. It is continuously updated with diverse sources such as news articles, blogs, literature, and educational content. This structure enables robust pretraining and fine-tuning for Swahili NLP. The evolving nature of the corpus allows for longitudinal linguistic analysis, providing a unique opportunity to track language change over time. It also serves as a foundation for creating niche, task-specific datasets in low-resource settings. Building on Maneno Yetu, we present the Swahili Language Foundational Model (SLFM), a transformer-based model trained to support core NLP tasks including tokenization, part-of-speech tagging, machine translation, and abusive language detection. Both the corpus and model are released publicly to support reproducible research and foster community-driven development in African language technologies.
A Comparative Analysis of Linguistic and Retrieval Diversity in LLM-Generated Search Queries
Authors: Oleg Zendel, Sara Fahad Dawood Al Lawati, Lida Rashidi, Falk Scholer, Mark Sanderson
Venue: CIKM
Year: 2025

Large Language Models (LLMs) are increasingly used to generate search queries for various Information Retrieval (IR) tasks. However, it remains unclear how these machine-generated queries compare to human-written ones, particularly in terms of diversity and alignment with real user behavior. This paper presents an empirical comparison of LLM- and human-generated queries across multiple dimensions, including lexical diversity, linguistic variation, and retrieval effectiveness. We analyze queries produced by several LLMs and compare them with human queries from two datasets collected five years apart. Our findings show that while LLMs can generate diverse queries, their patterns differ from those observed in human behavior. LLM queries typically exhibit higher surface-level uniqueness but rely less on stopword use and word form variation. They also achieve lower retrieval effectiveness when judged against human queries, suggesting that LLM-generated queries may not always reflect real user intent. These differences highlight the limitations of current LLMs in replicating natural querying behavior. We discuss the implications of these findings for LLM-based query generation and user behavior simulation in IR. We conclude that while LLMs hold potential, they should be used with caution.
Uncertainty Quantification for Multiple-Choice Questions is Just One-Token Deep
Authors: Qingcheng Zeng, Mingyu Jin, Qinkai Yu, Zhenting Wang, Wenyue Hua, Guangyan Sun, Yanda Meng, Shiqing Ma, Qifan Wang, Felix Juefei-Xu, Fan Yang, Kaize Ding, Ruixiang Tang, Yongfeng Zhang
Venue: CIKM
Year: 2025

Multiple-choice question (MCQ) benchmarks such as MMLU and GPQA are widely used to assess the capabilities of large language models (LLMs). While accuracy remains the standard evaluation metric, recent work has introduced uncertainty quantification (UQ) methods, such as entropy, conformal prediction, and verbalized confidence, as complementary measures of model reliability and calibration. However, we find that these UQ methods, when applied to MCQ tasks, are unexpectedly fragile. Specifically, we show that fine-tuning a model on just 1,000 examples to adjust the probability of the first generated token, under the common prompting setup where the model is instructed to output only a single answer choice, can systematically distort a broad range of UQ methods across models, prompts, and domains, all while leaving answer accuracy unchanged. We validate this phenomenon through extensive experiments on five instruction-tuned LLMs, tested under standard prompting, zero-shot chain-of-thought reasoning, and a biomedical question answering setting. In all cases, models retain similar accuracy but exhibit significantly degraded calibration. These results suggest that current UQ practices for MCQs are ''one-token deep'', driven more by first-token decoding behavior than by any deeper representation of uncertainty, and are easily manipulated through minimal interventions. Our findings call for more robust and interpretable approaches to uncertainty estimation, particularly in structured formats like MCQs, where confidence signals are often reduced to token-level heuristics.
Querier-Aware LLM: Generating Personalized Responses to the Same Query from Different Queriers
Authors: Hang Zeng, Chaoyue Niu, Fan Wu, Chengfei Lv, Guihai Chen
Venue: CIKM
Year: 2025

Existing work on large language model (LLM) personalization assigned different responding roles to LLMs, but overlooked the diversity of queriers. In this work, we propose a new form of querier-aware LLM personalization, generating different responses even for the same query from different queriers. We design a dual-tower model architecture with a cross-querier general encoder and a querier-specific encoder. We further apply contrastive learning with multi-view augmentation, pulling close the dialogue representations of the same querier, while pulling apart those of different queriers. To mitigate the impact of query diversity on querier-contrastive learning, we cluster the dialogues based on query similarity and restrict the scope of contrastive learning within each cluster. To address the lack of datasets designed for querier-aware personalization, we also build a multi-querier dataset from English and Chinese scripts, as well as WeChat records, called MQDialog, containing 173 queriers and 12 responders. Extensive evaluations demonstrate that our design significantly improves the quality of personalized response generation, achieving relative improvement of 8.4% to 48.7% in ROUGE-L scores and winning rates ranging from 54% to 82% compared with various baseline methods.
CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control
Authors: Jinwei Zeng, Chao Yu, Xinyi Yang, Wenxuan Ao, Qianyue Hao, Jian Yuan, Yong Li, Yu Wang, Huazhong Yang
Venue: CIKM
Year: 2025

City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particularly challenging. Given the prohibitive computational cost of learning individual policies for each intersection, some researchers explore learning a universal policy to control each intersection in a decentralized manner, where the key challenge is to construct a universal representation method for heterogeneous intersections. However, existing methods are limited to universally representing information of heterogeneous ego intersections, neglecting the essential representation of influence from their heterogeneous neighbors. Universally incorporating neighborhood information is nontrivial due to the intrinsic complexity of traffic flow interactions, as well as the challenge of modeling collective influences from neighbor intersections. To address these challenges, we propose CityLight, which learns a universal policy based on representations obtained with two major modules: a Neighbor Influence Encoder to explicitly model neighbor's influence with specified traffic flow relation and connectivity to the ego intersection; a Neighbor Influence Aggregator to attentively aggregate the influence of neighbors based on their mutual competitive relations. Extensive experiments on five city-scale datasets, ranging from 97 to 13,952 intersections, confirm the efficacy of CityLight, with an average throughput improvement of 11.68% and a lift of 22.59% for generalization. Our codes and datasets are released: https://github.com/tsinghua-fib-lab/CityLight.
UXSim: Towards a Hybrid User Search Simulation
Authors: Saber Zerhoudi, Michael Granitzer
Venue: CIKM
Year: 2025

Simulating nuanced user experiences within complex interactive search systems poses distinct challenge for traditional methodologies, which often rely on static user proxies or, more recently, on standalone large language model (LLM) agents that may lack deep, verifiable grounding. The true dynamism and personalization inherent in human-computer interaction demand a more integrated approach. This work introduces UXSim. https://searchsim.org/uxsim, a novel framework that integrates both approaches. It leverages grounded data from traditional simulators to inform and constrain the reasoning of an adaptive LLM agent. This synthesis enables more accurate and dynamic simulations of user behavior while also providing a pathway for the explainable validation of the underlying cognitive processes.
SGPT: Few-Shot Prompt Tuning for Signed Graphs
Authors: Zian Zhai, Qing Sima, Xiaoyang Wang, Wenjie Zhang
Venue: CIKM
Year: 2025

Signed Graph Neural Networks (SGNNs) are effective in learning expressive representations for signed graphs but typically require substantial task-specific labels, limiting their applicability in label-scarce industrial scenarios. In contrast, unsigned graph structures are abundant and can be readily leveraged to pre-train Graph Neural Networks (GNNs), offering a promising solution to reduce supervision requirements in downstream signed graph tasks. However, transferring knowledge from unsigned to signed graphs is non-trivial due to the fundamental discrepancies in graph types and task objectives between pre-training and downstream phases. To address this challenge, we propose Signed Graph Prompt Tuning (SGPT), a novel graph prompting framework that adapts pre-trained unsigned GNNs to few-shot signed graph tasks. We first design a graph template based on balance theory to disentangle mixed node relationships introduced by negative links, mitigating the structural mismatches between unsigned and signed graphs. We further introduce a task template that reformulates downstream signed tasks into a unified link prediction objective, aligning their optimization goals with the pre-training task. Furthermore, we develop feature prompts that align downstream semantic spaces with the feature spaces learned during pre-training, and semantic prompts to integrate link sign semantics in a task-aware manner. We conduct extensive experiments on seven benchmark signed graph datasets, demonstrating that SGPT significantly outperforms existing state-of-the-art methods, establishing a powerful and generalizable solution for few-shot signed graph learning.
A Privacy-preserving Spatial Dataset Joinable Search in Cloud
Authors: Zhengkai Zhang, Hua Dai, Hao Zhou, Mingfeng Jiang, Pengyue Li, Geng Yang
Venue: CIKM
Year: 2025

In the era of big data, the demand for spatial dataset search has become increasingly urgent. Leveraging the powerful storage and computing capabilities of cloud platforms, the cloud has become a common choice for deploying dataset search services. However, under risks of untrusted cloud environment and malicious attacks, protecting the privacy of sensitive location information during spatial dataset search becomes particularly critical. This paper focuses on the problem of privacy-preserving spatial datasets joinable search in cloud, which has not been addressed in existing research. We first propose a grid-based joinable coverage distinction model to measure the joinability of spatial datasets, and further present a baseline scheme (PDJDS). To further enhance efficiency and reduce storage cost, we propose an optimized scheme (PDJDS+), which constructs a coarse-grained grid-based inverted index to filter candidate datasets and integrates a joinable coverage distinction check table to expedite the evaluation of spatial dataset coverage distinction. Experiments conducted on three real-world spatial data repositories demonstrate that our scheme achieves superior performance in terms of search accuracy, efficiency, and storage cost.
DSETA: Driving Style-Aware Estimated Time of Arrival
Authors: Bolin Zhang, Zhidan Liu
Venue: CIKM
Year: 2025

The accurate estimated time of arrival (ETA) is crucial for mobility and transportation applications. Although significant efforts have been made to improve ETA prediction, most existing approaches ignore the influence of individual driving habits and preferences, known as the driving style. Since different drivers may prefer specific routes and speeds based on their experience and familiarity with traffic conditions, driving styles play a crucial role in determining the actual ETA. To fill this gap, we present a novel approach, DSETA, which leverages deep learning to learn and then integrate driving style representations for personalized and precise ETA predictions. Our method employs a diffusion model that captures nuanced driving styles by generating driving speed distribution. We also utilize attention mechanisms to dynamically adjust the impacts of various spatio-temporal factors and driving styles on ETA predictions. Additionally, we introduce a Multi-View Multi-Task framework that incorporates auxiliary tasks, including segment-view driving style classification and route-view speed distribution prediction, to enhance the ETA learning process. A route-level speed prior regularization strategy further improves the model's generalization capabilities. Extensive experiments conducted on a large real-world trip trajectory dataset demonstrate that DSETA achieves high effectiveness and outperforms various baselines across multiple evaluation metrics.
SyLeR: A Framework for Explicit Syllogistic Legal Reasoning in Large Language Models
Authors: Kepu Zhang, Weijie Yu, Zhongxiang Sun, Jun Xu
Venue: CIKM
Year: 2025

Syllogistic reasoning is a fundamental aspect of legal decision-making, enabling logical conclusions by connecting general legal principles with specific case facts. Although existing large language models (LLMs) can generate responses to legal questions, they fail to perform explicit syllogistic reasoning, often producing implicit and unstructured answers that lack explainability and trustworthiness. To address this limitation, we propose SyLeR, a novel framework that empowers LLMs to engage in explicit syllogistic legal reasoning. SyLeR integrates a tree-structured hierarchical retrieval mechanism to effectively combine relevant legal statutes and precedent cases, forming comprehensive major premises. This is followed by a two-stage fine-tuning process: supervised fine-tuning warm-up establishes a foundational understanding of syllogistic reasoning, while reinforcement learning with a structure-aware reward mechanism refines the model's ability to generate diverse logically sound and well-structured reasoning paths. We conducted extensive experiments across various dimensions, including in-domain and cross-domain user groups (legal laypersons and practitioners), multiple languages (Chinese and French), and different LLM backbones (legal-specific and open-domain LLMs). The results show that SyLeR significantly improves response accuracy and consistently delivers explicit, explainable, and trustworthy legal reasoning.
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
Authors: Qianru Zhang, Xinyi Gao, Haixin Wang, Dong Huang, Siu-Ming Yiu, Hongzhi Yin
Venue: CIKM
Year: 2025

Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, a key challenge lies in the noisy and sparse nature of spatial-temporal data, which limits existing neural networks' ability to learn meaningful region representations in the spatial-temporal graph. To overcome these limitations, we propose HGAurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation. Our framework introduces a spatial-temporal heterogeneous graph encoder that extracts region-wise dependencies from multi-source data, enabling comprehensive modeling of diverse spatial relationships. Within our self-supervised learning paradigm, we implement a masked autoencoder that jointly processes node features and graph structure. This approach automatically learns heterogeneous spatial-temporal patterns across regions, significantly improving the representation of dynamic temporal correlations. Comprehensive experiments across multiple spatiotemporal mining tasks demonstrate that our framework outperforms state-of-the-art methods and robustly handles real-world urban data challenges, including noise and sparsity in both spatial and temporal dimensions.
Interpretable Meta-weighting Sparse Neural Additive Networks for Datasets with Label Noise and Class Imbalance
Authors: Xuelin Zhang, Hong Chen, Lingjuan Wu
Venue: CIKM
Year: 2025

Black-box neural networks are inherently inscrutable, and their widespread use has triggered significant societal issues in crucial areas such as healthcare, finance and safety. In these high-stakes decision-making domains, the deployment of machine learning algorithms requires not only prediction accuracy but also their interpretability and robustness against data distribution shifts, such as outliers, label noise, and category imbalance. In this work, we propose a novel Meta-weighted Sparse Neural Additive Model (MSpNAM), which offers robustness through an efficient bilevel weighting policy and inherits strong explainability and representation capabilities from the additive modeling strategy. Furthermore, empirical results across multiple synthetic and real datasets, under various distribution shifts, demonstrate that MSpNAM can scale effectively and achieve superior performance in terms of robustness, interpretability, and anti-forgetting compared to some of the latest baselines.