OpenRaiser/Intern-Atlas huggingface dataset:
CS-Agent: LLM-based Community Search via Dual-agent Collaboration
Authors: Jiahao Hua, Long Yuan, Qingshuai Feng, Qiang Fan, Shan Huang
Venue: CIKM
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet their application to graph structure analysis, particularly in community search, remains underexplored. Community search, a fundamental task in graph analysis, aims to identify groups of nodes with dense interconnections, which is crucial for understanding the macroscopic structure of graphs. In this paper, we propose GraphCS, a comprehensive benchmark designed to evaluate the performance of LLMs in community search tasks. Our experiments reveal that while LLMs exhibit preliminary potential, they frequently fail to return meaningful results and suffer from output bias. To address these limitations, we introduce CS-Agent, a dual-agent collaborative framework to enhance LLM-based community search. CS-Agent leverages the complementary strengths of two LLMs acting as Solver and Validator. Through iterative feedback and refinement, CS-Agent dynamically refines initial results without fine-tuning or additional training. After the multi-round dialogue, Decider module selects the optimal community. Extensive experiments demonstrate that CS-Agent significantly improves the quality and stability of identified communities compared to baseline methods. To our knowledge, this is the first work to apply LLMs to community search, bridging the gap between LLMs and graph analysis while providing a robust and adaptive solution for real-world applications.
Spatio-Temporal Residual Masked Autoencoder for Urban Rent Estimation
Authors: Chenya Huang, Bin Liang, Zhidong Li, Justin Wang, Fang Chen
Venue: CIKM
Year: 2025

Housing affordability has become a critical issue in many cities, but gaps in rental transaction records hinder accurate rent estimation. Conventional spatial interpolation and time-series regression methods either ignore temporal trends or over-smooth spatial variation, leading to biased rent estimates in under-reported areas. While recent masked autoencoding techniques for tabular imputation address feature-wise missingness, they do not explicitly model the joint spatio-temporal structure of urban rent dynamics. This paper proposes a Spatio-Temporal Residual Masked Autoencoder (ST-ResMAE) that reconstructs masked rental values by integrating continuous covariates with learnable spatio-temporal embeddings and by modelling residuals over both space and time. Case study on the selected Australian urban suburbs rental data from 2020 to 2024 show that ST-ResMAE reduces imputation error by 5% relative to recent masked-autoencoding methods and by 15% relative to traditional regression models. These results demonstrate ST-ResMAE's ability to capture complex spatio-temporal rent dynamics even when data are sparse.
DeepEyeNet: Generating Medical Report for Retinal Images
Authors: Jia-Hong Huang
Venue: CIKM
Year: 2025

The increasing prevalence of retinal diseases poses a significant challenge to the healthcare system, as the demand for ophthalmologists surpasses the available workforce. This imbalance creates a bottleneck in diagnosis and treatment, potentially delaying critical care. Traditional methods of generating medical reports from retinal images rely on manual interpretation, which is time-consuming and prone to errors, further straining ophthalmologists' limited resources. This thesis investigates the potential of Artificial Intelligence (AI) to automate medical report generation for retinal images. AI can quickly analyze large volumes of image data, identifying subtle patterns essential for accurate diagnosis. By automating this process, AI systems can greatly enhance the efficiency of retinal disease diagnosis, reducing doctors' workloads and enabling them to focus on more complex cases. The proposed AI-based methods address key challenges in automated report generation: (1) A multi-modal deep learning approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports; (2) Improved methods for medical keyword representation enhance the system's ability to capture nuances in medical terminology; (3) Strategies to overcome RNN-based models' limitations, particularly in capturing long-range dependencies within medical descriptions; (4) Techniques to enhance the interpretability of the AI-based report generation system, fostering trust and acceptance in clinical practice. These methods are rigorously evaluated using various metrics and achieve state-of-the-art performance. This thesis demonstrates AI's potential to revolutionize retinal disease diagnosis by automating medical report generation, ultimately improving clinical efficiency, diagnostic accuracy, and patient care. DeepEyeNet Project Github: https://github.com/Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation
Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models
Authors: Hongtao Huang, Xiaojun Chang, Lina Yao
Venue: CIKM
Year: 2025

Diffusion models (DMs) are powerful generative models capable of producing high-fidelity images but are constrained by high computational costs due to iterative multi-step inference. While Neural Architecture Search (NAS) can optimize DMs, existing methods are hindered by retraining requirements, exponential search complexity from step-wise optimization, and slow evaluation relying on massive image generation. To address these challenges, we propose Flexiffusion, a training-free NAS framework that jointly optimizes generation schedules and model architectures without modifying pre-trained parameters. Our key insight is to decompose the generation process into flexible segments of equal length, where each segment dynamically combines three step types: full (complete computation), partial (cache-reused computation), and null (skipped computation). This segment-wise search space reduces the candidate pool exponentially compared to step-wise NAS while preserving architectural diversity. Further, we introduce relative FID (rFID), a lightweight evaluation metric for NAS that measures divergence from a teacher model's outputs instead of ground truth, slashing evaluation time by over 90%. In practice, Flexiffusion achieves at least 2× acceleration across LDMs, Stable Diffusion, and DDPMs on ImageNet and MS-COCO, with FID degradation under 5%, outperforming prior NAS and caching methods. Notably, it attains 5.1× speedup on Stable Diffusion with near-identical CLIP scores. Our work pioneers a resource-efficient paradigm for searching high-speed DMs without sacrificing quality.
Hyperspherical Dynamic Multi-Prototype with Arguments Dependencies and Role Consistency for Event Argument Extraction
Authors: Xiaojia Huang, Ruifang He, Fei Huang, Bo Wang, Sen Yao, Xiaohong Li
Venue: CIKM
Year: 2025

Event Argument Extraction (EAE) aims to identify arguments and assign them to predefined roles within a document. Existing methods face challenges in modeling intra-class variance and inter-class ambiguity, hindering accurate role assignment. Inspired by how humans dynamically adjust classification criteria while maintaining category consistency (e.g., distinguishing ''Victim'' and ''Attacker'' roles based on contextual relationships), we propose a HDMAR (Hyperspherical Dynamic Multi-Prototype with Arguments Dependencies and Role Consistency) method, where three innovations tackle these challenges: (1) Hyperspherical dynamic multi-prototype learning is used to capture intra-role diversity and enforce inter-role separation via hyperspherical optimization and optimal transport, (2) cross-event role consistency is used to align role representations across events, and (3) an arguments dependencies-guided encoding module enhances contextual understanding of intra-event and inter-event dependencies. Experiments on RAMS and WikiEvents demonstrate gains in accuracy, with further analysis validating the contributions of each module.
OFIA: An Object-centric Fine-grained Alignment Enhancement for Video-Text Retrieval
Authors: Zhengqi Huang, Wei Li, Chuang Dong, Mingxin Liu
Venue: CIKM
Year: 2025

Text-video alignment is crucial for text-video retrieval. Empirical studies have suggested that coarse-grained alignment overlooks the rich cross-modal details and therefore performs worse than fine-grained alignment. In the current transfer learning paradigm, although researchers primarily utilize patch-level or frame-level embedding as the fine-grained video representation for alignment, the frame embeddings miss the informative visual information, while a single patch only captures limited local details. These defects hinder the potential improvement in video-text retrieval. To address the defects of the existing fine-grained alignment approach, this paper proposes the Object-centric Fine-grained Alignment Enhancement for Video-Text Retrieval, namely OFIA, which consists of a text-guided object-text alignment module and a similarity-wise frame aggregation module to enhance video-text alignment. The text-guided object-text alignment module leverages the textual descriptions to detect and extract more relevant objects, enabling more precise local similarities within video frames. However, not all frames contribute equally to effective alignments. The similarity-wise frame aggregation module assigns greater importance to informative frames, overcoming the challenge of insignificant ones and optimizing the similarity of matched video-text pairs. The empirical evaluation on benchmark datasets including MSRVTT, MSVD, DiDeMo, and ActivityNet demonstrate state-of-the-art performance of the proposed method.
XDNet: Disentangled Time Series Forecasting via Exponential Decomposition and 2D Periodic Modeling
Authors: Kening Huang, Qianqian Ren, Xingfeng Lv
Venue: CIKM
Year: 2025

In time series analysis, disentangling long-term trends and seasonal patterns is crucial for capturing multi-scale temporal structures and improving both interpretability and forecasting accuracy. Recently, 2D modeling techniques have been incorporated into multivariate forecasting frameworks to better exploit periodic patterns. However, conventional decomposition methods often rely on simplistic moving averages that obscure critical patterns, while 2D modeling may entangle global trends with local variations and fail to normalize seasonal amplitudes-ultimately impairing both interpretability and forecast accuracy. To overcome these limitations, we propose XDNet (Exponential-Dimensional Network), a principled forecasting framework that explicitly disentangles trend and seasonal dynamics. At its core lies the Exponentially Weighted Decomposition (XWD), which applies decaying weights to past observations to preserve the integrity of long-term trends while adaptively normalizing seasonal fluctuations. The trend component is modeled using Temporal Kolmogorov-Arnold Networks (KAN) to capture intricate nonlinear dynamics, while the seasonal component is processed through a refined Inception-based module that robustly extracts fine-grained periodic dependencies. Extensive experiments on multiple benchmark datasets demonstrate that XDNet achieves state-of-the-art forecasting performance, delivering up to a 2.79% improvement in average accuracy over leading baselines, particularly in long-horizon prediction tasks.
Enhancing Multimodal Entity Linking via Distillation and Multimodal Large Language Models
Authors: Jintao Huang, Dong Wang, Shasha Li, Yuanxi Peng, Ruochun Jin
Venue: CIKM
Year: 2025

Multimodal entity linking (MEL) aims to link ambiguous multimodal mentions to their corresponding entities in a multimodal knowledge graph. Although many existing methods have been dedicated to exploring fine-grained intra- and cross-modal interactions between mentions and entities and have achieved good results, the discrepancies between the data distributions in training and real-world applications, as well as the noisy onehot labels, still impede the generalization of MEL models, which leads to poor performance when encountering unseen entities. Although general-purpose multimodal large language models (MLLMs) are powerful, it is costly and time-consuming to apply them directly to the MEL task. To address the above issues, we propose a Distillation-Enhanced framework for Multimodal Entity Linking (DEMEL). During training, DEMEL takes the best-trained MEL model so far as the teacher model, and distills the knowledge of the teacher model into the student model, i.e. the MEL model of the current iteration, when training it with onehot labels. This imposes regularization on the model, balances bias and variance in the training process, and improves the generalization ability of the MEL model. Moreover, DEMEL employs an MLLM to selectively rerank predictions for uncertain samples in the inference phase, improving accuracy while minimizing invocation costs. Extensive experiments on three public MEL datasets demonstrate that DEMEL outperforms state-of-the-art baselines, achieving 3.27% improvement with MLLM reranking for just 8.59% of test samples, and up to 4.8% H@1 enhancement in low-resource settings even without using MLLM reranking.
Unlocking the Potential of Smaller Language Models as Superior Instruction Evolvers
Authors: Tingfeng Hui, Lulu Zhao, Guanting Dong, Yaqi Zhang, Sen Su
Venue: CIKM
Year: 2025

Instruction tuning has become a cornerstone for unlocking the full potential of large language models. Among the key factors, complex and diverse instructions play a crucial role in aligning these models with a wide range of downstream tasks. However, current methodologies for constructing large-scale instruction datasets tend to favor powerful models, such as GPT-4, based on the empirical assumption that larger models inherently possess superior capabilities. In this study, we challenge this prevailing assumption and delve into the untapped potential of smaller language models (SLMs) in the context of instruction evolution. Through extensive experiments across three distinct scenarios of instruction evolution, we find that SLMs can generate more effective instructions compared to their larger counterparts. Further analysis reveals that SLMs exhibit a broader output space during instruction evolution, leading to the creation of more complex and diverse instructional variants. Additionally, we observe that existing evaluation metrics fall short in capturing the nuanced impact of instructions. To address this limitation, we propose Instruction Complex-Aware IFD (IC-IFD), an enhanced framework that incorporates instruction complexity into the original IFD score. This approach enables a more accurate assessment of the effectiveness of instruction data, paving the way for more refined instruction tuning strategies.
FASE: Feature-Aligned Scene Encoding for Open-Vocabulary Object Detection in Remote Sensing
Authors: Hyeonsu Hwang, Simon S. Woo
Venue: CIKM
Year: 2025

Open-vocabulary object detection (OVD) in remote sensing (RS) has shown remarkable generalization capabilities across diverse RS imagery through alignment between image and text embeddings. Such methods have further improved detection performance by incorporating additional scene-level context from both visual and textual domains. However, existing methods approximate scene context by simply averaging the text embeddings of the image's object labels, which is insufficient to capture the rich linguistic context present in RS scenes. To address this limitation, we propose a novel Feature-Aligned Scene Encoding (FASE), which constructs comprehensive scene representations through high-quality captions generated by a specialized vision-language model. Our Feature Alignment Module (FAM) creates a robust scene representation by fusing domain-specific caption embeddings with general text features through dual-branch fusion with gating and cross-attention. This resulting representation then facilitates the alignment with visual features. By utilizing enhanced scene encoding only during training, our method internalizes rich contextual knowledge without increasing inference complexity. Experiments on multiple benchmarks demonstrate significant improvements over state-of-the-art methods, validating the effectiveness of our approach for OVD in RS.
Scaling Trust: Veracity-Driven Defect Detection in Entity Search
Authors: Ornella Irrera, Stefano Marchesin, Gianmaria Silvello, Omar Alonso
Venue: CIKM
Year: 2025

Veracity is a critical dimension of data quality that directly impacts a wide range of tasks. In entity search scenarios, Knowledge Graphs (KGs) such as DBpedia and Wikidata serve as core resources for accessing factual content. The veracity of these KGs is therefore essential for ensuring the reliability and trustworthiness of retrieved entities -- factors that directly influence user confidence in the search system. However, ensuring the truthfulness of entities remains a major challenge due to the complexities associated with the scale, development, and maintenance of KGs. This paper critically analyzes the impact of veracity in entity search, using DBpedia as the underlying KG. To this end, we introduce eRank, a veracity-driven re-ranking strategy that enhances entities' trustworthiness without sacrificing the ranking's overall relevance. Furthermore, we propose the Active Learning-based verAcity-Driven Defect IdentificatioN (ALADDIN) system, a lightweight and scalable framework for veracity-driven defect detection. ALADDIN identifies incorrect KG facts and exhibits high effectiveness in downstream entity-centric tasks, such as entity summarization, entity card generation, and defect recommendation.
Multimodal RAG Enhanced Visual Description
Authors: Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Venue: CIKM
Year: 2025

Textual descriptions for multimodal inputs entail recurrent refinement of queries to produce relevant output images. Despite efforts to address challenges such as scaling model size and data volume, the cost associated with pre-training and fine-tuning remains substantial. However, pre-trained large multimodal models (LMMs) encounter a modality gap, characterised by a misalignment between textual and visual representations within a common embedding space. Although fine-tuning can potentially mitigate this gap, it is typically expensive and impractical due to the requirement for extensive domain-driven data. To overcome this challenge, we propose a lightweight training-free approach utilising Retrieval-Augmented Generation (RAG) to extend across the modality using a linear mapping, which can be computed efficiently. Our reproducible code can be found in https://github.com/amitkumarj441/mRAG-gim. During inference, this mapping is applied to images embedded by an LMM enabling retrieval of closest textual descriptions from the training set. These textual descriptions, in conjunction with an instruction, cater as an input prompt for the language model to generate new textual descriptions. In addition, we introduce an iterative technique for distilling the mapping by generating synthetic descriptions via the language model facilitating optimisation for standard utilised image description measures. Experimental results on two benchmark multimodal datasets demonstrate significant improvements.
Deep Modality-Disentangled Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
Authors: Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh
Venue: CIKM
Year: 2025

The growing use of multimodal content on social media has sparked interest in sarcasm detection for better opinion mining. However, current models depend heavily on large datasets, limiting their adaptability to real-world scenarios with limited labeled data. Therefore, an in-depth exploration of the problem in a few-shot setting is necessary. We propose DMDP (Deep Modality-Disentangled Prompt Tuning), a novel approach designed for few-shot multimodal sarcasm detection. Previous few-shot approaches relied on shallow, unified prompts across modalities, limiting their ability to model the nuanced and diverse nature of sarcasm. In contrast, we propose gated modality-weighted dual prompts that are disentangled across text and visual encoders, injected at deeper layers to enable hierarchical feature aggregation and identify distinct sarcasm cases. A prompt-sharing mechanism across the layers of each encoder facilitates the capture of low-level cues to high-level semantics, while a cross-modal prompt alignment facilitates subtle visual-textual interactions, allowing the model to capture complex sarcastic cues better. Extensive experiments on two public datasets show the superiority of our model over baselines in the few-shot and extremely low-resource scenarios. To further validate our model's effectiveness, we conduct a cross-dataset evaluation on two public datasets, where it consistently outperforms baselines, highlighting strong generalization. Our code will be available at https://github.com/mr-perplexed/dmdp.
Streamlining Feature Interactions via Selectively Crossing Vectors for Click-Through Rate prediction
Authors: Byungwoo Jang, Jinhee Park, Eunil Park
Venue: CIKM
Year: 2025

Previous Click-Through Rate (CTR) prediction models rely on enumerating high-order feature combinations up to a fixed order, limiting expressiveness and scalability. Recent studies explored arbitrary-order interaction modeling through two major paradigms: log-based and graph-based methods. However, both paradigms suffer from inherent weaknesses: log-based methods lack stability, and graph-based methods lack generalizability, as both attempt to model overly diverse combinations of features, many of which may be noisy or redundant. This observation provokes a central question: What if only a small set of core interactions is sufficient? To explore this, we progressively mask feature interactions and find that removing up to 90% of them results in negligible performance degradation. This suggests that most interactions are unnecessary. Motivated by this finding, we propose SCV: Selectively Crossing Vectors, a CTR prediction framework that reformulates feature interaction learning as a sparse edge selection task over a globally shared feature-interaction graph. By modeling feature interactions over a globally learned graph and dynamically fusing expert outputs in an instance-aware manner, the SCV effectively leverages global consistency and local adaptability. We further introduce a label-biased self-distillation objective to mitigate the effects of noisy supervision and stabilize training. Experiments on public CTR benchmarks show that SCV achieves state-of-the-art performance while reducing computational cost by up to 66%, validating the effectiveness of globally sparse yet locally adaptive interaction modeling. All codes are available at: https://github.com/bw-99/scv.
An Efficient PIM-Based Graph Engine on a Single Machine
Authors: Myung-Hwan Jang, Min-Kyeong Shin, Taehyeong Park, Yongjun Park, Sang-Wook Kim
Venue: CIKM
Year: 2025

With the increasing size of real-world networks, efficient analysis of large-scale graphs has become an important research area. To this end, we can consider Processing-in-Memory (PIM), which integrates processing units and main memory into a single chip, as a promising solution. Many studies have focused on enabling highly efficient processing of memory-intensive tasks by using PIM's high internal bandwidth. To the best of our knowledge, however, there have been no studies related to the scenarios where the entire graph does not fit in main memory and data movement across storage, memory, and cache should be considered. Motivated by this, we propose RealGraph PIM , a new PIM-based graph engine, that processes large-scale real-world graphs efficiently on top of the original RealGraph, a state-of-the-art CPU-based graph engine. RealGraph PIM employs (1) asynchronous I/O to reduce wasting time in an idle state and (2) column-wise partitioning to reduce CPU workloads, thereby issuing I/O requests more frequently. Experimental results on real-world datasets show that RealGraph PIM outperforms dramatically state-of-the-art graph engines including a naive version of RealGraphPIM.