OpenRaiser/Intern-Atlas huggingface dataset:
Advances in Financial AI: Innovations, Risk, and Responsibility in the Era of LLMs
Authors: Yongjae Lee, Nazanin Mehrasa, Chanyeol Choi, Chung-Chi Chen, Dhagash Mehta, Stefan Zohren, Yoon Kim, Chulheum Lee, Yeonhee Lee, Eunsook Oh
Venue: CIKM
Year: 2025

The finance sector is seeing a rapid increase in the application of machine learning and AI, with Large Language Models (LLMs), ESG (Environmental, Social, and Governance) investing, and AI Safety significantly reshaping the field. This workshop focuses on how these advancements intersect with core financial AI applications. We will foster interdisciplinary discussion on applying LLMs to finance, addressing challenges in multilingual and non-English markets like Korea. The event will also highlight the integration of ESG signals into algorithmic decision-making and explore AI Safety, emphasizing reliability, fairness, and explainability for AI systems in regulated financial environments. By bringing together experts from academia, industry, and regulatory bodies, the workshop aims to stimulate discussions on practical issues, ethical dilemmas, and cutting-edge research shaping financial AI's future. We welcome submissions that combine technical rigor with societal relevance in AI-driven financial decisions.
Mitigating Knowledge Degradation Caused by Knowledge Editing on Identical Subjects through Two-Step Editing
Authors: Seonghee Lee, Geon Park, Geunyeong Jeong, Juoh Sun, Harksoo Kim
Venue: CIKM
Year: 2025

Large Language Models (LLMs) acquire extensive factual knowledge from large-scale datasets and demonstrate remarkable performance across various tasks. However, since real-world knowledge is constantly changing, it is necessary to modify or expand the model's knowledge. To achieve this, knowledge editing techniques are employed to correct inaccurate or outdated information and inject new knowledge, thereby ensuring that the model remains current. However, in existing subject-centered editing approaches, repeatedly editing the same subject can lead to knowledge degradation, where previously edited knowledge is forgotten. In this paper, we analyze the causes of this knowledge degradation phenomenon and propose a two-step editing method that independently edits subjects and relations to mitigate this issue. Our method effectively alleviates knowledge degradation compared to existing knowledge editing techniques, achieving average performance improvements of 22.9% in multi-edit scenarios and 7.2% in sequential editing.
SwaGNER: Leveraging Span-aware Grid Transformers for Accurate Nested Named Entity Recognition
Authors: Seungjoo Lee, Yong-chan Park, U Kang
Venue: CIKM
Year: 2025

How can we accurately recognize overlapping entity spans in text while effectively capturing global context among spans? Nested Named Entity Recognition (nested NER) becomes challenging in the presence of nested or overlapping entity spans. Traditional span-based methods enumerate all possible spans, resulting in high computational costs and severe label imbalance from excessive negative spans which are non-entities. Additionally, they often fail to fully capture global context among overlapping entities. In this paper, we propose SwaGNER, a novel approach that dynamically selects span candidates via boundary detection, and encodes their interactions using a contextual grid. By filtering out low-confidence spans early, SwaGNER focuses on high-quality candidates, arranging them in a grid to explicitly model global context and span relationships. This integrated boundary detection and span classification approach reduces error propagation and effectively leverages sentence-wide context. Experiments demonstrate that SwaGNER achieves the state-of-the-art performance on both nested NER and flat NER.
Amortized Baseline Selection via Rank-Revealing QR for Efficient Model Explanation
Authors: Chanwoo Lee, Youngjin Park, Hyeongeun Lee, Yeeun Yoo, Daehee Han, Junho Choi, Geonhyeong Kim, Nari Kim, Jaesik Choi
Venue: CIKM
Year: 2025

Model-agnostic explanation methods are essential for interpreting machine learning models, but suffer from prohibitive computational costs that scale with the number of baselines. Existing acceleration approaches either lack a theoretical base or provide no principled guidance for baseline selection. To address this gap, we present ABSQR (Amortized Baseline Selection via Rank-Revealing QR). This framework exploits the low-rank structure of value matrices to accelerate multi-baseline attribution methods. Our approach combines deterministic baseline selection via SVD-guided QR decomposition with an amortized inference mechanism that utilizes cluster-based retrieval. We reduce computational complexity from O (m • 2d) to O (k • 2d), where k ≪ m. Experiments demonstrate that ABSQR achieves a 91.2% agreement rate with full baseline methods while providing 8.5× speedup across diverse datasets. As the first acceleration approach that preserves explanation error guarantees under computational speedup, ABSQR makes the practical deployment of interpretable AI systems feasible at scale.
Waypoint POI Recommendation for Vehicle Navigation Services using Hierarchical Graphs and Contrastive Learning
Authors: Jongsoo Lee, Heejun Shin, Namhyuk Kim, Dong-Kyu Chae
Venue: CIKM
Year: 2025

Modern vehicle navigation systems can greatly benefit from waypoint point-of-interest (POI) recommendation, which suggests personalized intermediate stops along a driving route. This paper defines the novel waypoint POI recommendation problem: given a starting point and a destination, recommend one or more personalized POIs to visit en route. This scenario (e.g., suggesting a lunch stop during a road trip) differs from the conventional ''next POI'' recommendation in that it infers waypoint POIs from only two (origin and destination) inputs and predicts multiple intermediate stops rather than a single next location. To solve this problem, we propose WayPOI, a novel recommender model for Waypoint POI suggestion based on hierarchical graph based contrastive learning (WayPOI). WayPOI constructs a hierarchical graph that captures both individual and group-level behavioral patterns of users and POIs, and it employs a contrastive learning strategy to learn effective user and POI representations from sparse data. Through experiments on real-world driving data provided by Hyundai as well as on three public datasets, we demonstrate that WayPOI significantly outperforms several recent POI recommendation models, even though these baselines were carefully re-formed and retrained to perform waypoint recommendation for a fair comparison. Our ablation study confirms the benefit of each proposed component.
PlaceSim: An LLM-based Interactive Platform for Human Behavior Simulation in Physical Facilities
Authors: Suhyeon Lee, Youngjun Yu, Donghyuk Shin, Rita Singh
Venue: CIKM
Year: 2025

Physical facility design faces a fundamental cold-start problem: predicting human behavior in non-existent spaces. Traditional surveys and observational studies create gaps between stated preferences and actual usage, while existing simulation tools require significant technical expertise, limiting accessibility. We introduce PlaceSim, a web-based platform leveraging Large Language Models (LLMs) to simulate realistic human behavior in facilities through a zero-code interface. PlaceSim employs a Persona-Environment-Scenario (P.E.S.) framework that structures LLM reasoning through context-aware AI personas with transparent decision-making processes. The platform provides interactive facility design, AI-driven persona generation, live simulation with reasoning visualization, and what-if analysis for scenario comparison. Evaluated on 18 months of real-world apartment facility data (789,238 usage records from 8,435 residents), our zero-shot approach achieves Jensen-Shannon Divergence scores as low as 0.006, outperforming both supervised learning methods and existing LLM-based tools like SocioVerse without requiring training data. PlaceSim establishes new benchmarks for spatial behavior prediction while providing immediate, actionable insights for architects, urban planners, and facility managers through systematic simulation. The platform is available at https://simulation-viewer.vercel.app/
What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems
Authors: Jens Leysen, Marco Favier, Bart Goethals
Venue: CIKM
Year: 2025

Data minimization is a legal principle requiring personal data processing to be limited to what is necessary for a specified purpose. Operationalizing this principle for recommender systems, which rely on extensive personal data, remains a significant challenge. This paper conducts a feasibility study on minimizing implicit feedback inference data for such systems. We propose a novel problem formulation, analyze various minimization techniques, and investigate key factors influencing their effectiveness. We demonstrate that substantial inference data reduction is technically feasible without significant performance loss. However, its practicality is critically determined by two factors: the technical setting (e.g., performance targets, choice of model) and user characteristics (e.g., history size, preference complexity). Thus, while we establish its technical feasibility, we conclude that data minimization remains practically challenging and its dependence on the technical and user context makes a universal standard for data 'necessity' difficult to implement.
Linking Ordered and Orderless Modeling for Sequential Recommendation
Authors: Sijia Li, Min Gao, Zongwei Wang, Yibing Bai, Wuhan Chen
Venue: CIKM
Year: 2025

Sequential recommendation is pivotal to personalized services by modeling the temporal dynamics of user behavior. However, existing methods often rely on abundant interactions, making it unreliable under sparse user interactions. Recent attempts to integrate sequential signals with orderless structural cues (e.g., global co-occurrence) help alleviate this issue but typically adopt tight fusion, which can dilute order-aware signals. To address this, we propose LOOM (Loosely-Coupled Ordered-Orderless Modeling), a structure-agnostic guidance module for sequential recommenders. LOOM is sequence-first: The sequential backbone acts as a teacher, guiding orderless carriers via one-way KL divergence, with recency-aware weighting and confidence-modulated strength to filter stale or uncertain relations. This preserves temporal modeling while selectively incorporating complementary orderless knowledge. Experiments on four public datasets and various sequential architectures show that LOOM outperforms state-of-the-art methods. Code is available at https://github.com/cqu-jia/LOOM.
BordaRAG: Resolving Knowledge Conflict in Retrieval-Augmented Generation via Borda Voting Process
Authors: Yuxin Li, Chen Xu, Jun Xu, Ji-Rong Wen
Venue: CIKM
Year: 2025

Recently, research found that the documents retrieved from the Retrieval-Augmented Generation (RAG) may contain conflicting knowledge with each other, leading Large Language Models (LLMs) to generate incorrect responses. To solve such a problem, existing approaches usually only keep the most frequently mentioned knowledge from these documents, since they assume that the most representative knowledge aligns best with the true answer. Although effective in certain scenarios, these approaches often underperform when the most frequent knowledge is not the correct one. From the voting perspective, these methods can be regarded as a Majority Voting (MV) process, which chooses the most frequent candidates among different candidate knowledge. However, we show that the underperformance of such methods stems from that MV is only effective with a small number of candidates and binary voting scores. In contrast, in the RAG scenario, the candidates (knowledge) are very diverse, and the voting scores (document relevance scores) are typically continuous. Simply adapting MV in RAG will result in poor performance of LLMs. In voting theory, on the other hand, the preference-based voting methods represented by the Borda Voting (BV) consider the whole preference order of voters over all candidates, enabling the selection of candidates that better represent the collective viewpoint. Inspired by such an insight, we propose BordaRAG, a model designed to better select the most appropriate documents from conflicting documents. Specifically, BordaRAG first computes the preference scores of the documents over the candidate answers. After that, a BV component is designed to select the winning documents according to the preference scores. Finally, the chosen documents are provided to LLMs, which will generate the final response. Experimental results on three open-domain QA datasets show that BordaRAG can outperform all baselines.
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Authors: Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, Jiaqi Zhang, Fajie Yuan
Venue: CIKM
Year: 2025

Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent items. However, the current landscape of TCF models mainly relies on the utilization of relatively small or medium-sized LMs. The potential impact of using larger, more powerful language models (such as these with over 100 billion parameters) as item encoders on recommendation performance remains uncertain. Can we anticipate unprecedented results and discover new insights? To address this question, we undertake a comprehensive series of experiments aimed at exploring the performance limits of the TCF paradigm. Specifically, we progressively augment the scale of item encoders, ranging from one hundred million to one hundred billion parameters, in order to reveal the scaling limits of the TCF paradigm. Moreover, we investigate whether these exceptionally large LMs have the potential to establish a universal item representation for the recommendation task, thereby revolutionizing the traditional ID paradigm, which is considered a significant obstacle to developing transferable ''one model fits all'' recommender models. Our study not only demonstrates positive results but also uncovers unexpected negative outcomes, illuminating the current state of the TCF paradigm within the community. These findings will evoke deep reflection and inspire further research on text-based recommender systems.
QGCMA: A Framework for Knowledge-Based Visual Question Answering
Authors: Wei Li, Zhixin Li
Venue: CIKM
Year: 2025

Visual Question Answering (VQA) systems encounter formidable challenges when tackling complex queries that demand external knowledge integration and multi-modal reasoning. Current methodologies often grapple with the effective alignment of visual and textual features, as well as the utilization of structured knowledge bases, which limits their performance in handling intricate semantic and inferential tasks. To address these critical issues, this paper presents a framework based on three key innovations. Firstly, the Question-Guided Attention (QGA) mechanism adaptively steers the model's focus towards visual regions and knowledge entities that are semantically congruent with the query. By doing so, it ensures that contextually relevant information is prioritized during the feature extraction process, enhancing the model's ability to capture pertinent visual and knowledge cues. Secondly, the Cross-Modal Alignment (CMA) module employs a contrastive learning strategy to enforce precise alignment across visual, textual, and knowledge modalities. This approach effectively mitigates the detrimental effects of spurious correlations by enhancing semantic consistency among heterogeneous data sources, thereby improving the overall quality of multi-modal feature integration. Thirdly, the Dynamic Knowledge Integration (DKI) component empowers the model to dynamically select and fuse knowledge information from external graph structures. This functionality significantly augments the model's reasoning capacity, enabling it to handle questions that necessitate compositional inference over structured knowledge. Comprehensive experimental evaluations conducted on the OK-VQA and VQA v2 benchmarks demonstrate the superiority of our proposed method over existing state-of-the-art methods.
Taming Ultra-Long Behavior Sequence in Session-wise Generative Recommendation
Authors: Wuchao Li, Shiyao Wang, Kuo Cai, Jiaxin Deng, Xingmei Wang, Qigen Hu, Defu Lian, Guorui Zhou
Venue: CIKM
Year: 2025

Generative recommendation has emerged as a transformative paradigm in recommender systems, enabling modeling user behavior autoregressively without explicit target conditioning. While this approach eliminates the need for target signals, it necessitates compressing extensive historical interactions-potentially spanning lifelong sequences-into coherent interest representations. Conventional methods for handling long sequences typically rely on target-guided search mechanisms (e.g., SIM) to efficiently filter and compress behaviors. However, this strategy is incompatible with generative frameworks due to their target-agnostic nature. To address these challenges, we propose a novel encoder-decoder model named HiCoGen (Hierarchical Compression-based Session-wise Generative Model), which efficiently models long-term interests in generative models. In the encoder, HiCoGen compresses behavior sequences using hierarchical content similarity clustering and employs a hierarchical attention architecture to reduce sequence length while preserving information integrity. In the decoder, HiCoGen uses session-wise generation instead of point-wise generation to better align with industrial short-video applications. To enhance the stability of session-wise generation, we introduce an auxiliary Hierarchical Multi-Token Prediction module. Extensive experiments on public and industrial datasets show significant performance gains over state-of-the-art methods (21.2% in ML-1M and 35.6% in industrial datasets on NDCG@3). We also conducted visualization and performance analysis to explore the advantages of long sequence modeling.
Dynamic Reserve Price Design with Distributed Solving Algorithm
Authors: Mang Li
Venue: CIKM
Year: 2025

Unexpected advertising items in sponsored search may reduce users' reliance on organic search, resulting in hidden cost for the e-commerce platform. To address this problem and promote sustainable growth, we propose a dynamic reserve price design that incorporates the hidden cost into the auction mechanism to determine whether to sell the traffic, thereby ensuring a balanced relationship between revenue and user experience. Our dynamic reserve price design framework optimizes traffic sales by minimizing impacts on user experience while maintaining long-term incentives for advertisers to reveal their valuations truthfully. Furthermore, we introduce a distributed algorithm capable of computing reserve prices with billion-scale data in the production environment. Experiments involving offline evaluations and online A/B testing demonstrate that this method is simple and efficient, making it suitable for use in industrial production. This method has already been fully deployed in the production environment.
Fast Outlier Detection in Oblique Subspaces
Authors: Bowen Li, Charu C. Aggarwal, Peixiang Zhao
Venue: CIKM
Year: 2025

Subspace outlier detection is a fundamental data mining task for high-dimensional data with diverse applications across widely varying domains. Existing subspace hashing methods have been capable of identifying accurate and interpretable outliers in axis-parallel subspaces in linear time. However, these methods simply fail when applied to arbitrary-shaped, schema-less data that lack well-defined attributes or dimensions, such as time series and graphs. In this paper, we introduce a new notion of oblique subspaces defined on pairwise object proximity functions without requiring explicit multidimensional representations of the underlying data. By hashing data objects into arbitrarily oriented oblique subspaces, we can construct subspace hashing histograms for efficient and cost-effective outlier detection. Our proposed solution, OS-Hash (Oblique Subspace Hashing), is a linear-time, constant-space method applicable to not only multidimensional but also arbitrary-shaped data, and it can further be extended to the data stream setting for subspace outlier detection. Our experimental studies on real-world multidimensional, time series, and graph data demonstrate the efficiency and efficacy of OS-Hash, which outperforms state-of-the-art outlier detection methods in terms of both runtime performance and accuracy.
Bridging Queries and Tables through Entities in Open-Domain Table Retrieval
Authors: Da Li, Keping Bi, Jiafeng Guo, Xueqi Cheng
Venue: CIKM
Year: 2025

Open-domain table retrieval plays a vital role in accessing information from structured formats on the web, yet it remains less explored than text retrieval. Table cells primarily consist of phrases and words, which include numerous entities, such as times, locations, persons, and organizations. While emphasizing entities in text retrieval has been extensively studied, there is a significant lack of research on their applications in table retrieval. In this work, we explore how to leverage entities in tables to improve retrieval performance. We investigate the important role of entities in table retrieval from a statistical perspective and propose an Entity-Centric Alignment framework for Table retrieval (ECAT). Specifically, we use entity types to highlight entities appearing in queries and tables. Then, we propose an entity-driven late interaction paradigm based on entity representations for dense and sparse retrievers, respectively. Our proposed framework is plug-and-play and flexible, making it easy to integrate into existing table retrievers. Empirical results on table retrieval benchmarks, NQ-TABLES and OTT-QA, show that our proposed ECAT is effective in enhancing existing retrievers. Extensive analyses confirm the efficacy of ECAT's different components. Our code and dataset are available at https://github.com/Trustworthy-Information-Access/ECAT.
A Comprehensive Toolkit for Generalized Robust Vision
Authors: Zhao Li, Yuefeng Chen, Hui Xue, Xiaofeng Mao
Venue: CIKM
Year: 2025

While deep neural networks (DNNs) excel in computer vision tasks, their real-world deployment is hindered by robustness limitations compared to human perception. Adversarial attacks and data distribution shifts remain critical vulnerabilities, degrading model performance under practical conditions. To address these challenges and advance robustness research, we introduce a comprehensive, user-friendly toolkit for training, evaluating, and analyzing robust vision models. It targets two key dimensions of robustness: 1) Adversarial robustness-defending against malicious worst-case perturbations (adversarial examples); 2) Natural robustness-maintaining performance under real-world corruptions and distribution shifts. Through extensive image classification benchmarks, our toolkit enables precise model assessment. We envision this toolkit accelerating the development of practically robust models and bridging the gap between machine and human vision capabilities.