OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Bidirectional State Space Model for High-frequency Portfolio Management
Authors: Wei Ding, Hanpeng Jiang, Ruibo Xiong, Yongrong Wu, Jingan Chen, Lifan Chen, Pengfei Ding, Fan Lin
Venue: CIKM
Year: 2025

State space models (SSMs) have recently shown great potential on long-range sequence modeling tasks. Benefiting from SSMs' low spatio-temporal overhead and powerful modeling capabilities, utilizing them for high-frequency portfolio management is an appealing research direction. However, representing financial data is challenging for SSMs due to: 1) the non-stationary nature of financial markets and 2) the requirement of asset correlations for financial understanding. In this paper, under a deep reinforcement learning (DRL) paradigm for high-frequency portfolio management, we propose a novel Adaptive Bidirectional State Space Model (ABSSM) to tackle the above challenges. Specifically, in order to cope with changing market conditions, we design an adaptive linear time-varying structure, which precisely captures domain shifts in temporal patterns through an input-dependent state transition matrix, thereby seizing fleeting arbitrage opportunities. Furthermore, we enhance this framework by constructing a bidirectional state space layer, which extracts asset correlations by compressing the global context. To the best of our knowledge, this is the first work that solves the high-frequency portfolio management problem by devising a specialized state space model in the DRL framework. Through extensive experiments on real-world data from the U.S., China, and cryptocurrency markets, we show that our proposed ABSSM significantly outperforms state-of-the-art benchmark methods in balancing profits and risks.
Unsupervised Adversarial Contrastive Hashing for Cross-Modal Retrieval
Authors: Guohui Ding, Zhonghua Li, Rui Zhou, Qian Gao
Venue: CIKM
Year: 2025

Cross-modal hashing has gained widespread attention in cross-modal retrieval due to its low storage cost and significant computational efficiency. Existing cross-modal hashing methods primarily focus on learning modality invariance by mapping data from different modalities into a shared space and learn unified hash codes. Nevertheless, due to the inherent heterogeneity between different modalities, the common subspace may still exhibit modality discrepancies. This ultimately makes it challenging to achieve semantic alignment, thereby affecting the accuracy of cross-modal retrieval. To address this issue, we propose an Unsupervised Adversarial Contrastive Hashing (UACH) method for cross-modal retrieval. Specifically, we design a cycle generative adversarial network to learn the transformation relationships between different modality feature domains, effectively promoting semantic alignment across modalities. Additionally, we employ dual contrastive learning to simultaneously measure the representation learning and hashing learning components of each specific modality, and learn unified hash codes for each specific modality, thus mitigating the impact of modality discrepancies. Extensive experiments conducted on three cross-modal benchmark datasets demonstrate that our model outperforms the state-of-the-art baselines.
EventPuzzle: A Benchmark for Multi-Perspective Event Prediction Based on Event Arguments
Authors: Guoxuan Ding, Junhao Zhou, Yuqing Li, Xiaobo Guo, Xin Wang, Daren Zha
Venue: CIKM
Year: 2025

Event prediction is a critical task in natural language processing, aimed at reasoning and forecasting future events based on known event texts. This paper introduces EventPuzzle, a benchmark designed to evaluate the event prediction capabilities of large language models based on event arguments. By introducing argument points, we design tasks and evaluation methods to assess models' ability to predict events from different argument perspectives. EventPuzzle consists of both closed-ended and open-ended tasks. In the closed-ended task, models select the correct argument point from causal chains, while in the open-ended task, models generate event descriptions using two strategies: Argument-based Generation and Direct Generation. We construct an argument point dataset and evaluate multiple LLMs, demonstrating the models' performance across various tasks. Our experimental analysis reveals the strengths and limitations of current models and suggests future directions for improving event prediction.
Exploring the Impact of Warnings on User Perception towards AI-Generated Content in Search Results
Authors: Pia Donabauer, David Elsweiler
Venue: CIKM
Year: 2025

Generative-AI answer boxes have become a default element of modern search engines, yet their responses are not always trustworthy. We study whether a simple disclosure can temper the influence of these answers on users' beliefs and behaviour. In a between-subjects online experiment (N=57) participants formed opinions on one of three controversial topics while interacting with a SERP whose featured answer was produced by ChatGPT. Each participant had a 50% chance of seeing a banner that (i) disclosed the answer's AI origin, (ii) listed three key limitations, and (iii) linked to additional details. The banner did not shift overall opinion means, but it changed how people reacted to the AI: participants who saw the warning were 83% more likely to adopt a stance that contradicted the chatbot's answer than those in the no-banner condition. Stance alignment proved even more decisive: when the AI answer matched a user's initial view, the likelihood of attitude change fell by 85% and exploration of opposing results dropped significantly. Conversely, when the AI disagreed with users, 41% of their post-task explanations converged semantically toward the chatbot's wording (vs. 14% when they already agreed), revealing subtle linguistic uptake detected via BERT embeddings. Together these findings show that (1) a lightweight disclosure can promote a more critical stance toward AI output, yet (2) pre-existing agreement with the AI strongly anchors users, suppressing both critical search behaviour and reflective revision. We argue that AI-integrated search interfaces should pair transparency banners with additional bias-mitigation strategies to support informed and balanced information seeking.
PAnDA: Combating Negative Augmentation via Large Language Models for User Cold-Start Recommendations
Authors: Yantong Du, Rui Chen, Xiangyu Zhao, Qilong Han, A. Kai Qin
Venue: CIKM
Year: 2025

The cold-start problem remains a long-standing challenge in recommender systems. Recent advances in large language models (LLMs) have opened new avenues for addressing cold-start scenarios through data augmentation. However, existing cold-start augmentation methods often suffer from negative augmentation, manifesting as incomplete augmentation, where generated interactions fail to comprehensively reflect user preferences, and inaccurate augmentation, where they conflict with user intent. These issues largely stem from two limitations: (1) the inability to effectively incorporate collaborative signals, which are critical for preference alignment, and (2) the lack of awareness of the downstream model's learning dynamics during data augmentation. To the best of our knowledge, the latter has not been studied in the literature. Consequently, we propose a novel framework named PAnDA. To address the incomplete augmentation issue, we propose a model-agnostic preference-aligned augmentation module to iteratively extract and fuse textual information and collaborative information by user-user preference matching and user-item preference coherence, which together form a contextual cue to guide the augmentor to generate high-quality augmented data. To overcome the inaccurate augmentation issue, we propose a model-specific downstream-model-aware adaptation module to adaptively align the augmented data with the model's states during the training process, guided by gradient similarity. Extensive experiments on three public benchmark datasets demonstrate that PAnDA outperforms different groups of state-of-the-art cold-start recommendation methods in all scenarios. The source code is publicly available at https://github.com/YantongDU/PAnDA.
Decoupling Feature Entanglement for Personalized Federated Learning via Neural Collapse
Authors: Haizhou Du, Pengfei Li
Venue: CIKM
Year: 2025

Heterogeneous data is a critical challenge in Personalized Federated Learning (pFL), as it leads to feature entanglement, making it difficult to share knowledge across clients. Addressing this issue requires a deep understanding of the underlying mechanisms of feature distribution, as well as effective strategies for sharing knowledge among participating clients. Motivated by the phenomenon of Neural Collapse (NC) observed in well-trained deep classification models, we propose FedDemux, a novel pFL framework that facilitates the personalization process by explicitly promoting the emergence of NC. FedDemux tackles feature entanglement through the coordination of two key modules: (1) a simplex learnable embedding (SLE) module guided by NC to learn and rectify local features, and (2) a knowledge decoupling module (KDM) that extracts general knowledge to align local features with the global simplex-learnable embeddings, while personalized knowledge further enhances local inference capabilities. We conduct extensive experiments on three real-world datasets with heterogeneous settings, where FedDemux consistently outperforms state-of-the-art methods in all cases. Specifically, FedDemux achieves up to a 13.54% accuracy improvement over baselines on the CIFAR-100 dataset. Scalability and ablation experiments validate FedDemux's effectiveness and SLE's role in accelerating convergence.
MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments
Authors: Ziheng Duan, Xi Li, Zhiqing Xiao, Rex Ying, Jing Zhang
Venue: CIKM
Year: 2025

Recent advances in spatial transcriptomics (ST) and cost reductions have enabled large-scale multi-slice ST data generation, enhancing the statistical power to detect subtle biological signals. However, cross-slice inconsistencies and data quality variability present significant analytical challenges. To overcome these limitations, we developed MUSE, a computational framework designed for multi-slice joint embedding, spatial domain identification, and gene expression imputation. Specifically, MUSE integrates a two-module architecture to ensure robust cross-slice alignment and data harmonization. The alignment module models each slice as a graph and employs optimal transport to align cells across slices while preserving spatial continuity. The optimization module further refines integration by incorporating an alignment loss, allowing lower-quality data to leverage structural information from higher-quality slices. Additionally, MUSE generates virtual neighbors from aligned cells, enriching contextual information and mitigating data sparsity. These design principles enable seamless integration with existing single-slice methods, extending their applicability to multi-slice ST analysis. To comprehensively evaluate its performance, we applied MUSE to 12 real and 48 simulated datasets spanning a range of data qualities. Across all metrics, MUSE consistently outperformed existing methods in cross-slice consistency, spatial domain identification, and gene expression imputation. To promote accessibility and adoption, we provide MUSE as an open-source software package. As multi-slice ST datasets become increasingly prevalent, MUSE provides a robust and extensible framework designed to effectively integrate growing numbers of slices, thereby advancing the analysis of tissue architectures and spatial gene expression in complex biological systems.
YTCommentVerse: A Multi-Category Multi-Lingual YouTube Comment Corpus
Authors: Hridoy Sankar Dutta, Biswadeep Khan
Venue: CIKM
Year: 2025

In this paper, we introduce YTCommentVerse, a large-scale multilingual and multi-category dataset of YouTube comments. It contains over 32 million comments from 178,000 videos contributed by more than 20 million unique users spanning 15 distinct YouTube content categories such as Music, News, Education and Entertainment. Each comment in the dataset includes video and comment IDs, user channel details, upvotes and category labels. With comments in over 50 languages, YTCommentVerse provides a rich resource for exploring sentiment, toxicity and engagement patterns across diverse cultural and topical contexts. This dataset helps fill a major gap in publicly available social media datasets particularly for analyzing video sharing platforms by combining multiple languages, detailed categories and other metadata.
RottenReviews: Benchmarking Review Quality with Human and LLM-Based Judgments
Authors: Sajad Ebrahimi, Soroush Sadeghian, Ali Ghorbanpour, Negar Arabzadeh, Sara Salamat, Muhan Li, Hai Son Le, Mahdi Bashari, Ebrahim Bagheri
Venue: CIKM
Year: 2025

The quality of peer review plays a critical role in scientific publishing, yet remains poorly understood and challenging to evaluate at scale. In this work, we introduce RottenReviews, a benchmark designed to facilitate systematic assessment of review quality. RottenReviews comprises over 15,000 submissions from four distinct academic venues enriched with over 9,000 reviewer scholarly profiles and paper metadata. We define and compute a diverse set of quantifiable review-dependent and reviewer-dependent metrics, and compare them against structured assessments from large language models (LLMs) and expert human annotations. Our human-annotated subset includes over 700 paper-review pairs labeled across 13 explainable and conceptual dimensions of review quality. Our empirical findings reveal that LLMs, both zero-shot and fine-tuned, exhibit limited alignment with human expert evaluations of peer review quality. Surprisingly, simple interpretable models trained on quantifiable features outperform fine-tuned LLMs in predicting overall review quality. We publicly release all data, code, and models at https://github.com/Reviewerly-Inc/RottenReviews to support further research in this area.
Building a Virtual Member of a Community of Practice
Authors: Joshua Eckroth, Dayne Freitag, Jonathan M. Keefe, Timothy Meyer, Karen L. Myers, Eric Schoen, Pedro Sequeira, Reid G. Smith
Venue: CIKM
Year: 2025

We describe a virtual member of a knowledge management Community of Practice (CoP), called ATHENA, that knows an individual, his tasks, his organization, and the community. ATHENA employs an agentic chat capability that combines embeddings with knowledge-based faceted search to provide accurate responses to technical questions along with rationale and citations for efficient validation. ATHENA supports natural, in-the-flow capture of task-related insights to share within a CoP, along with proactive dissemination of information tied to an individual and his current needs. An evaluation involving 75 professionals from the Oil & Gas sector shows that ATHENA dramatically improved outcomes and productivity on a set of well-planning tasks compared to their use of a state-of-the-art RAG baseline. Interestingly, ATHENA also enabled eight non-experts to perform at expert levels.
Hearable Image: On-Device Image-Driven Sound Effect Generation for Hearing What You See
Authors: Deokjun Eom, Nahyun Kim, Woo Hyun Nam, Kyung-Rae Kim, Chaebin Im, Jungwon Park
Venue: CIKM
Year: 2025

There have been various studies in audio generation from image, text, or video. However, the existing approaches have not consider on-device environment because audio generation models are computationally expensive and require heavy storage capacity to save large number of weights. In addition, it is difficult to get stable generation outputs because unexpected results may occur depending on various model inputs. In image-to-audio generation, there are diverse images in smartphones, and too many visual contexts are contained in image features. Therefore, it is sometimes unpredictable which audio categories are generated from images. In this paper, we propose a robust on-device sound effect generation framework that is image-to-audio generation based on latent diffusion. First, to avoid unstable and unpredictable audio generation results, we propose a stable sound generation framework with Audio Feature Dictionary and Audio-Image Matching Pipeline to generate sound effects from predefined sound effect categories. If an image matches to sound effect categories, proposed framework directly generate sound effects from audio features corresponding to the matched categories. Second, we propose Multi-Category Generation and Generation Flow Map to generate robust and diverse sound effects depending on audio categories. Using global and local features of an image, we can select multiple categories of sound effects. Third, the framework can be implemented in smartphone devices because we train the proposed model with low computational cost and small number of model weights under 4-step latent diffusion inference. Various experiments show the proposed framework solves on-device sound generation problem with maintaining generation quality and audio-image matching performances compared to large scale models. Our demo is available at: https://youtu.be/Y5HTr8wwqOA.
Enhancing Multi-Behavior Sequential Recommenders with Behavior-Aware Regularization
Authors: Yongfu Fan, Jin Chen, Yangzixuan Jiao, Ximu Zeng, Liwei Deng, Kai Zheng
Venue: CIKM
Year: 2025

In the realm of multi-behavior sequential recommendation (MBSR), the complexity and heterogeneity of user interactions pose substantial challenges for sequence modeling. Existing studies involve significant efforts in combining different modules to learn more expressive multi-behavior sequence representations or designing strategies to extract user preferences related to the target behavior. Despite their effectiveness, these methods neglect a thorough analysis of how behavioral information shapes the probability distribution for next-item prediction, which is crucial for accurately modeling user preferences. To this end, we first analyze the learning distribution of MBSR, shedding light on the significance of target behavior in next-item prediction. Building upon this insight, we propose a Behavior-Aware Regularization approach for multi-behavior sequential Rec ommendation (BAR4Rec), where we introduce a regularization loss function to preserve the intrinsic constraints of target behavior. In this way, the target probability distribution is extracted from the whole distribution and naturally evolves into a more compatible and tractable form, thus facilitating model design and training. We evaluate the proposed method on three real-world datasets, and the results validate the efficacy of our approach.
LLM-Enhanced Generalized Category Discovery via Iterative Graph Diffusion
Authors: Kangjia Fan, Yilong Zhao, Daifeng Li, Changze Lin, Weijun Zhang, Zhiwen Zhong
Venue: CIKM
Year: 2025

Generalized Category Discovery (GCD) aims to identify both known and unknown categories from unlabeled data using limited labeled samples from known classes. The key challenges lie in the scarcity of supervision signals for unknown categories and the difficulty of modeling relationships among samples. Existing methods that rely solely on clustering uncertainty often result in imperfect hard negative selection, while their use of nearest-neighbor structures hinders the effective utilization of Large Language Model (LLM) annotations for obtaining high-quality supervision. We propose a dynamic optimization model (LIGD) that leverages diffusion graphs and LLM annotations to address these issues. By leveraging the semantic-correlation graph, the method achieves two key capabilities: it selects both hard negatives and unlabeled central samples likely to represent novel categories as high-value samples. In addition, the graph enables effective label propagation through its connected subgraph, significantly reducing computational costs while enhancing the accuracy of category discovery. To further enhance annotation quality, we introduce a two-stage prompting strategy that queries the LLM twice to accurately assign selected samples to either existing or novel categories. The entire process will be repeated iteratively until convergence to update the graph structure and node representations in the graph. Experiments on three GCD datasets demonstrate the significant superiority of LIGD. Most notably, in the challenging scenario where only 25% of categories are labeled, the model achieves substantial improvements while reducing the number of LLM queries by 50%. Code and data are available at https://github.com/wdmmxlbt/LIGD.
MMFair: Fair Learning via Min-Min Optimization
Authors: Kejie Fang, Kun Zhai, Xingjun Ma
Venue: CIKM
Year: 2025

Ensuring group fairness is crucial in applications like facial recognition, medical image analysis, and online comment toxicity classification. A key challenge to achieving group fairness arises from spurious correlations in datasets, where features used by models for predictions are unrelated to the true labels. The widespread use of large-scale pre-trained models as feature extractors, followed by fine-tuning for downstream tasks, can exacerbate this issue. In particular, improper fine-tuning on limited data often leads to overfitting, reinforcing spurious correlations and further undermining group fairness. To address this, we propose MMFair, an algorithm that optimizes perturbations through a min-min optimization approach. These perturbations are applied to the deep embeddings, preventing the model from associating irrelevant features with true labels, thus improving group fairness. Notably, since simple linear classifiers are prone to spurious correlations, we use a linear head in the initial stage to generate perturbations. After optimizing the perturbations in the latent space, we incorporate them into the original embeddings and then train a multi-layer perceptron (MLP) as the final classification head. This two-stage approach helps mitigate the bias problem of linear head, while leveraging the more powerful feature-learning capabilities of MLPs, leading to more stable and accurate classification results. We evaluate MMFair on the Waterbirds, CelebA, and ISIC datasets. The results show that MMFair improves the accuracy of the worst-performing group efficiently.
DP-COMET: A Differential Privacy Contextual Obfuscation MEchanism for Texts in Natural Language Processing
Authors: Francesco Luigi De Faveri, Guglielmo Faggioli, Nicola Ferro
Venue: CIKM
Year: 2025

Protecting sensitive information within textual data strongly depends on the context in which the data is presented. However, current privacy-preserving obfuscation mechanisms based on epsilon-Differential Privacy (DP) produce an obfuscated private text, changing the original phrase term-by-term without considering the context in which such a term is placed. This paper introduces DP-COMET, an epsilon-DP obfuscation mechanism that evaluates a text's context before producing its private version. The mechanism defines a representation of the original text that considers the entire context within the text, producing an obfuscated version after adding noise to this representation and depending on the privacy parameter epsilon. We test DP-COMET on different Natural Language Processing (NLP) and Information Retrieval (IR) downstream tasks, and our findings show that our obfuscation mechanism not only achieves comparable performance results to traditional term-by-term mechanisms but also produces obfuscated texts less similar to the originals. To promote the reproducibility of DP-COMET, we make the code publicly available at https://github.com/Kekkodf/DP-COMET.