GenAI4ELab/papercli-papers huggingface dataset:
Revisiting Contrastive Learning in Collaborative Filtering via Parallel Graph Filters
Authors: Fang Kai; Yu Zhang; Kaibin Wang; Lei Sang; Yiwen Zhang
Venue: AAAI 2026 (main)

Abstract: Graph Contrastive Learning (GCL) has recently emerged as a powerful paradigm for modeling user–item interactions and learning high-quality representations in recommender systems. While existing GCL-based methods benefit from data augmentation and sampling strategies, they often overlook the inherent limitations of the contrastive objectives: 1) Stacking multiple Graph Convolutional Network layers to capture high-order information often causes the over-smoothing phenomenon, where node representations become overly similar. 2) Structurally similar negative sample pairs may exhibit high cosine similarity, causing gradient saturation during representation optimization. To address the above challenges, we revisit matrix factorization in recommendation models and uncover its implicit connection to a parallel graph filter bank. This perspective reveals how overly aggressive low-pass or high-pass filtering distorts feature distributions, contributing to gradient saturation. Building on this insight, we propose Light Cosine Similarity Collaborative Filtering (LightCSCF), a margin-constrained method that improves gradient optimization in contrastive learning by focusing on structurally hard examples, alleviating both gradient saturation and boundary over-smoothing. Extensive experiments on three real-world datasets demonstrate that LightCSCF consistently outperforms state-of-the-art baselines in recommendation accuracy and robustness to data sparsity.
Memorize Early, Then Query: Inlier-Memorization-Guided Active Outlier Detection
Authors: Minseo Kang; Seunghwan Park; Dongha Kim
Venue: AAAI 2026 (main)

Abstract: Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime--without any information about anomalous instances in the training data--is challenging. A recently observed phenomenon, known as the inlier-memorization (IM) effect, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that rely solely on the IM effect still struggle when inliers and outliers are not well-separated or when outliers form dense clusters. To address these limitations, we incorporate active learning to selectively acquire informative labels, and propose IMBoost, a novel framework that explicitly reinforces the IM effect to improve outlier detection. Our method consists of two stages: 1) a warm-up phase that induces and promotes the IM effect, and 2) a polarization phase in which actively queried samples are used to maximize the discrepancy between inlier and outlier scores. In particular, we propose a novel query strategy and tailored loss function in the polarization phase to effectively identify informative samples and fully leverage the limited labeling budget. We provide a theoretical analysis showing that the IMBoost consistently decreases inlier risk while increasing outlier risk throughout training, thereby amplifying their separation. Extensive experiments on diverse benchmark datasets demonstrate that IMBoost not only significantly outperforms state-of-the-art active OD methods but also requires substantially less computational cost.
PEOCH: Online Cross-Modal Hashing with Semi-Supervised Streaming Data Driving Prototype Evolution
Authors: Xiao Kang; Xingbo Liu; Shuo Pan; Xuening Zhang; Xiushan Nie; Yilong Yin
Venue: AAAI 2026 (main)

Abstract: The exponential growth of streaming multi-modal data presents critical challenges for cross-modal retrieval: distribution shifts, modality gap, and scarce labels. Semi-supervised online cross-modal hashing has gained increasing interest due to its ability to encode complex streaming data and update hash functions simultaneously. Nevertheless, existing methods can hardly generate high-quality unsupervised hash codes, which fundamentally limits diversity and flexibility during the retrieval process. To this end, we propose a novel method named Prototype Evolution Online Cross-modal Hashing (PEOCH). By driving prototype evolution with semi-supervised streaming data, precise and stable hash codes are generated for both labeled and unlabeled data. Specifically, two prototype updates with stability guarantee are conducted: labeled samples push semantic knowledge into the supervised prototypes, while unlabeled samples perform clustering to generate unsupervised prototypes. Simultaneously, a co-optimization mechanism is designed to ensure the prototypes continuously evolve and preserve the consistency of the entire streaming data. Besides, an elasticity regularizer integrates discriminability and smoothness constraints, improving the reliability of prototypes. Extensive experiments on three benchmark datasets demonstrate that PEOCH outperforms state-of-the-art methods, achieving an average improvement of 6.7% in mAP@all across various retrieval tasks.
CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
Authors: HyunGi Kim; Jisoo Mok; Hyungyu Lee; Juhyeon Shin; Sungroh Yoon
Venue: AAAI 2026 (main)

Abstract: Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in real-world deployments, distribution shifts are ubiquitous and cause severe performance degradation in pre-trained anomaly detector. Test-time adaptation (TTA) updates a pre-trained model on-the-fly using only unlabeled test data, making it promising for addressing this challenge. In this study, we propose CANDI (Curated test-time adaptation for multivariate time-series ANomaly detection under DIstribution shift), a novel TTA framework that selectively identifies and adapts to potential false positives while preserving pre-trained knowledge. CANDI introduces a False Positive Mining (FPM) strategy to curate adaptation samples based on anomaly scores and latent similarity, and incorporates a plug-and-play Spatiotemporally-Aware Normality Adaptation (SANA) module for structurally informed model updates. Extensive experiments demonstrate that CANDI significantly improves the performance of MTSAD under distribution shift, improving AUROC up to 14% while using fewer adaptation samples.
SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce
Authors: Li Kong; Bingzhe Wang; Zhou Chen; Suhan Hu; Yuchao Ma; Qi Qi; Suoyuan Song; Bicheng Jin
Venue: AAAI 2026 (main)

Abstract: Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this marketing scenario, we propose a novel marketing framework, named Sequence-Aware Constrained Optimization (SACO) framework, to directly devise coupon distribution policy for long-term revenue boosting. SACO framework enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework.
MovSemCL: Movement-Semantics Contrastive Learning for Trajectory Similarity
Authors: Zhichen Lai; Hua Lu; Huan Li; Jialiang Li; Christian S. Jensen
Venue: AAAI 2026 (main)

Abstract: Trajectory similarity computation is fundamental functionality that is used for, e.g., clustering, prediction, and anomaly detection. However, existing learning-based methods exhibit three key limitations: (1) insufficient modeling of trajectory semantics and hierarchy, lacking both movement dynamics extraction and multi-scale structural representation; (2) high computational costs due to point-wise encoding; and (3) use of physically implausible augmentations that distort trajectory semantics. To address these issues, we propose MovSem, a movement-semantics contrastive learning framework for trajectory similarity computation. MovSem first transforms raw GPS trajectories into movement-semantics features and then segments them into patches. Next, MovSem employs intra- and inter-patch attentions to encode local as well as global trajectory patterns, enabling efficient hierarchical representation and reducing computational costs. Moreover, MovSem includes a curvature-guided augmentation strategy that preserves informative segments (e.g., turns and intersections) and masks redundant ones, generating physically plausible augmented views. Experiments on real-world datasets show that MovSem is capable of outperforming state-of-the-art methods, achieving mean ranks close to the ideal value of 1 at similarity search tasks and improvements by up to 20.3% at heuristic approximation, while reducing inference latency by up to 43.4%.
Think Then Rewrite: Reasoning Enhanced Query Rewriting for Domain Specific Retrieval
Authors: Ang Li; Yufei Shi; Yuxuan Si; Yiquan Wu; Ming Cai; Xu Tan; Yi Wang; Changlong Sun; Xiaozhong Liu; Kun Kuang
Venue: AAAI 2026 (main)

Abstract: Query rewriting is a crucial task for improving retrieval, especially in professional domains such as law and medicine, where user queries are often underspecified and ambiguous. While large language models (LLMs) offer strong understanding and generation capabilities, existing LLM-based approaches reduce the task to text transformation or expansion, neglecting reasoning to disambiguate queries, which fails to bridge the cognitive gap between user queries and specialized documents. In this paper, we propose Think-Then-Rewrite (TTR), a reinforcement learning based framework that unleashes LLMs' reasoning ability for domain-specific query rewriting. TTR introduces a contrastive mutual information reward to encourage the LLM to generate reasoning processes that effectively distinguish confusing distractors. To boost early-stage training, TTR also constructs golden query rewrites as off‑policy data, providing strong guidance for RL learning. A mixed-policy optimization then combines on-policy and off-policy signals, ensuring both effectiveness and stability. Extensive experiments on legal and medical retrieval benchmarks demonstrate that TTR achieves state-of-the-art performance.
SGP4SR: Separated-Modality Guided User Preference Learning for Multimodal Sequential Recommendation
Authors: Changhong Li; Zhiqiang Guo; Guohui Li; Zhong Yang; Chuhang Hong
Venue: AAAI 2026 (main)

Abstract: With the booming development of multimodal data (e.g., image, text) on internet platforms, multimodal sequential recommendation methods continue to emerge. Most existing methods incorporate item modal features as auxiliary information, typically concatenating them to learn unified user representations. However, these methods directly use modal features for representation learning, neglecting the impact of inherent modal noise. We argue that internal-modal noise and cross-modal noise hinder the acquisition of more accurate user representations. 
	To address this problem, we propose SGP4SR - Separated-modality Guided user Preference learning for multimodal Sequential Recommendation. Globally, the user preference modeling is carried out from a separated-modality perspective to alleviate cross-modal noise. Locally, for each individual modality, we use item relationship graphs and user interest centers, aggregated with ID embeddings, to replace direct modal features, thereby mitigating internal-modal noise. Finally, user representations from both separated-modality and multimodal perspectives participate in prediction independently.
	In experiments conducted on four real-world datasets, our method outperforms state-of-the-art approaches, achieving an average performance improvement of up to 8.84% over the best baseline. The comprehensive experiments further validate the superior noise tolerance and robustness of our method.
FreqTAD: Multi-scale Frequency Encoding and Time-Frequency Attention for Anomaly Detection in Dynamic Graphs
Authors: Chao Li; Runshuo Liu; Zhongying Zhao; Hui Zhou; Qingtian Zeng
Venue: AAAI 2026 (main)

Abstract: Anomaly detection in dynamic graphs aims to capture the dynamic evolution characteristics of graphs, and then identify abnormal behaviors that deviate from normal patterns. However, previous studies fail to decouple periodic and bursty information during the time encoding process, which hinders their performances. In addition, most existing methods use attention mechanisms to capture the importance of time points. They fail to leverage the normal and abnormal characteristics in the frequency domain. To address the above issues, we propose a model that integrates multi-scale Frequency encoding with Time-frequency Attention for Anomaly Detection in dynamic graphs, named FreqTAD. We design a multi-scale frequency encoder that decomposes time series into distinct periodic and bursty components. Moreover, we present an effective time-frequency attention mechanism that focuses on frequency components to differentiate frequency-domain features of normal and abnormal behaviors. Experimental results on four datasets demonstrate the superior performance of FreqTAD in both anomaly detection accuracy and computational efficiency.
CL-DMDF: Dynamic Multimodal Data Fusion Model Based on Contrastive Learning
Authors: Dong Li; Lingling Zhang; Binghao Han; Linlin Ding; Yue Kou
Venue: AAAI 2026 (main)

Abstract: Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patterns, thereby enhancing data processing and decision-making. While existing methods for structured multimodal inputs are typically designed around specific tasks and assume fully observed modalities, realworld applications often suffer from uncertain or missing modality inputs due to various factors. Some traditional models overly emphasize local interactions within missing modalities, neglecting the global complementary cues embedded in multimodal representations. To overcome these limitations, we propose a Dynamic Multimodal Data Fusion model based on Contrastive Learning (CL-DMDF). CL-DMDF introduces a novel attention mechanism that operates across both feature and modality dimensions to compute reliable attention scores, effectively reflecting importance at each level. The CL-DMDF further incorporates an entity-centroid contrastive learning module that constructs centroid-based positive samples from entity features to enhance discriminative learning. Additionally, an adaptive fusion module is employed to improve the efficiency and accuracy of dynamic fusion strategies. Extensive experiments conducted on three datasets demonstrate the effectiveness of the CL-DMDF across diverse multimodal fusion tasks.
Interest-Shift-Aware Logical Reasoning for Efficient Long-Sequence Recommendation
Authors: Fei Li; Qingyun Gao; Enneng Yang; Jianzhe Zhao; Guibing Guo
Venue: AAAI 2026 (main)

Abstract: Logical reasoning-based recommendation methods formulate logical expressions to characterize user-item interaction patterns, incorporating regularization constraints to ensure consistency with logical rules. However, these methods face two critical challenges: (1) As sequence length increases, they cannot effectively capture the dynamic transfer of user interests across subsequences (i.e., subsequence interest drift), thereby degenerating logical expressions to single-subsequence inference. (2) The time complexity of logical reasoning and rule learning scales quadratically with the sequence length, severely constraining computational efficiency in long-sequence recommendation. To address these challenges, we propose ELECTOR, an intErest-shift-aware long-sequence Logical reasoning for EffiCienT lOng-sequence Recommendation method. Specifically, we design a Subsequence Interest Learning Module (SIL) to model cross-subsequence interest drifts in long sequences. SIL employs a local attention mechanism to extract subsequence interests effectively and a global attention mechanism to capture the correlations among subsequence interests. Subsequently, we propose an Interest-aware Logical Reasoning (ILR) mechanism that performs logical reasoning using a limited set of subsequence and short-term interests, rather than reasoning over the entire sequence, significantly reducing time complexity. Additionally, ILR employs interest logical reasoning contrastive loss to ensure the model simultaneously considers multiple interests. Experiments on four real-world datasets demonstrate that our method significantly outperforms all baselines regarding computational efficiency and recommendation accuracy, confirming its effectiveness.
Knowledge-Enhanced Image Captioning with Adaptive Graph-based Multimodal Alignment and LLM
Authors: Guoyi Li; Die Hu; Haozhe Li; Zhongjiang Yao; Wei Mi; Zongzhen Liu; Xiaodan Zhang; Honglei Lyu
Venue: AAAI 2026 (main)

Abstract: Image captioning is crucial for multimodal understanding, bridging visual content and natural language. Despite recent advancements in Large Multimodal Models (LMMs), when faced with unseen entities or scenes in the open world, even when attempting to leverage learned knowledge, models still struggle with vague and inaccurate descriptions, and may even generate knowledge hallucinations. A key reason is that the model fails to effectively integrate knowledge with visual information, limiting its understanding of visual content. Thus, we propose Adaptive Knowledge Graph-guided Multimodal Alignment (AKGMA) for image captioning, which enhances semantic understanding in open-world scenes through visual knowledge reasoning, reducing knowledge hallucinations and improving caption quality. It consist three key components: Entity-guided Knowledge Aligner (EKA), Adaptive Knowledge Graph Construction (AKGC), and Scene-Context Knowledge Adapter (SCKA). EKA connects visual entities to knowledge graphs, providing structured knowledge to a small language model, which interacts with a visual encoder to acquire visual knowledge. AKGC uses reinforcement learning to build image-relevant subgraphs to optimize knowledge prompts and improve knowledge hallucinations. SCKA leverages scene graph annotations to extract visual contextual knowledge and inject it into Large Language Models (LLMs), ensuring the generated descriptions are consistent with the image's details. Additionally, we introduce UniKnowCap, a new image knowledge description dataset spanning various open-world knowledge domains, designed to evaluate the knowledge accuracy and detail consistency of model-generated descriptions. Extensive experiments show our model outperforms baselines across multiple metrics.
Subspace-Aware Graph Construction and Contrastive Alignment for Multimodal Recommendation with Large Language Models
Authors: Haodong Li; Lianyong Qi; Weiming Liu; Fan Wang; Chong Li; Shengye Pang; Wenwen Gong; Yanwei Xu; Xiaoxiao Chi; Yang Zhang; Xiaokang Zhou
Venue: AAAI 2026 (main)

Abstract: Multimedia content offers additional context for recommender systems to better understand user interests. Existing studies on multimodal recommendation primarily focus on constructing item-item semantic graphs. However, most of these methods capture only shallow semantic structures based on feature similarity and struggle to model more complex or cross-entity semantic relationships (e.g., user-item). Moreover, in these methods, collaborative signals often dominate and suppress semantic knowledge, which limits its role in representation learning. To address these issues, we propose SCALE, a novel framework that combines subspace-aware graph construction and contrastive alignment for multimodal recommendation with large language models. Specifically, we first use large language models and encoders to extract user and item features. Following the subspace clustering assumption, we apply the Orthogonal Matching Pursuit algorithm to mine complex semantic structures within the item-item, user-user, and user-item spaces, and integrate them into a unified semantic graph. We then perform graph convolution on both the semantic and interaction graphs, and aggregate the results for recommendation. Furthermore, contrastive losses are employed to enhance semantic fusion and alignment. Extensive experiments on five real-world datasets demonstrate that SCALE significantly outperforms state-of-the-art multimodal recommendation models, highlighting its effectiveness in modeling complex relationships and integrating semantic knowledge with collaborative signals.
Can Molecular Evolution Mechanism Enhance Molecular Representation?
Authors: Kun Li; Longtao Hu; Jiameng Chen; Hongzhi Zhang; Yida Xiong; Xiantao Cai; Wenbin Hu; Jia Wu
Venue: AAAI 2026 (main)

Abstract: Molecular evolution is the process of simulating the natural evolution of molecules in chemical space to explore potential molecular structures and properties. The relationships between similar molecules are often described through transformations such as adding, deleting, and modifying atoms and chemical bonds, reflecting specific evolutionary paths. Existing molecular representation methods mainly focus on mining data, such as atomic-level structures and chemical bonds directly from the molecules, often overlooking their evolutionary history. Consequently, we aim to explore the possibility of enhancing molecular representations by simulating the evolutionary process. We extract and analyze the changes in the evolutionary pathway and explore combining it with existing molecular representations. Therefore, this paper proposes the molecular evolutionary network (MEvoN) for molecular representations. First, we construct the MEvoN using molecules with a small number of atoms and generate evolutionary paths utilizing similarity calculations. Then, by modeling the atomic-level changes, MEvoN reveals their impact on molecular properties. Experimental results show that the MEvoN-based molecular property prediction method significantly improves the performance of traditional end-to-end algorithms by approximately 33% on both the QM7 and QM9 datasets.