GenAI4ELab/papercli-papers huggingface dataset:
Adaptive Diffusion-based Augmentation for Recommendation
Authors: Na Li; Fanghui Sun; Yan Zou; Yangfu Zhu; Xiatian Zhu; Ying Ma
Venue: AAAI 2026 (main)

Abstract: Recommendation systems often rely on implicit feedback, where only positive user-item interactions can be observed. Negative sampling is therefore crucial to provide proper negative training signals. However, existing methods tend to mislabel potentially positive but unobserved items as negatives and lack precise control over negative sample selection. We aim to address these by generating controllable negative samples, rather than sampling from the existing item pool. In this context, we propose Adaptive Diffusion-based Augmentation for Recommendation (ADAR), a novel and model-agnostic module that leverages diffusion to synthesize informative negatives. Inspired by the progressive corruption process in diffusion, ADAR simulates a continuous transition from positive to negative, allowing for fine-grained control over sample hardness. To mine suitable negative samples, we theoretically identify the transition point at which a positive sample turns negative and derive a score-aware function to adaptively determine the optimal sampling timestep. By identifying this transition point, ADAR generates challenging negative samples that effectively refine the model's decision boundary. Experiments confirm that ADAR is broadly compatible and boosts the performance of existing recommendation models substantially, including collaborative filtering and sequential recommendation, without architectural modifications.
Multiplex Heterogeneous Graph Neural Networks with Euclidean-Riemannian Mutual Space Synergy
Authors: Xiang Li; Yuan Cao; Zhongying Zhao; Guoqing Chao; Yanwei Yu
Venue: AAAI 2026 (main)

Abstract: Multiplex heterogeneous networks are common in real-world scenarios, where entities interact through diverse types of relations across multiple semantic layers. Recent advances in multiplex heterogeneous graph neural networks have achieved remarkable results by incorporating node and relation types into message passing and designing relation-aware architectures. However, most existing methods either decouple relations and risk losing complex semantics or require handcrafted relation patterns, which limit scalability. Moreover, prevailing models are typically restricted to Euclidean space, making it difficult to capture non-Euclidean topologies and to distinguish complex interactions among heterogeneous nodes and relations. Standard GNN message passing, grounded in the homophily assumption, also proves inadequate for the intricate, coupled structures in multiplex heterogeneous graphs. To address these challenges, we propose MRiemGNN, a novel multiplex heterogeneous graph neural network that synergizes Euclidean and Riemannian spaces through a geometry-aware, relation-specific message passing scheme and cross-space mutual learning. Experiments on multiple real-world datasets show that MRiemGNN achieves superior performance, efficiency, and scalability on both node classification and link prediction tasks.
Self-Improving Sparse Retrieval Through Heuristic Representation Refinement and Representation-Focused Learning
Authors: Xiaojing Li; Bin Wang; Xiaochun Yang; Meng Luo
Venue: AAAI 2026 (main)

Abstract: Learnable sparse retrieval (LSR) models encode texts into high-dimensional sparse representations, supporting token-level expansion beyond the original text and addressing the vocabulary mismatch problem in traditional bag-of-words retrieval.
However, in the absence of representation-level supervision, these representations usually overemphasize irrelevant tokens while neglecting truly relevant ones.
We term this phenomenon the Representation Hallucination problem in LSR models, a critical bottleneck impeding accurate retrieval.
To address this challenge, we introduce SiRe, a self-improving training framework for sparse retrieval that integrates two core strategies: Heuristic Representation Refinement and Representation-Focused Learning.
Specifically, SiRe first identifies and corrects representation hallucinations in the outputs of the current LSR model using heuristic methods.
The resulting representations serve as the primary supervision signals, guiding a pretrained language model (e.g., BERT) to mitigate the problem directly at the representation level.
This process can be iterated, enabling progressive model improvement.
Extensive experiments on both in-domain and out-domain benchmarks show that SiRe produces higher-quality sparse representations, significantly enhancing retrieval performance over strong baselines.
Exploring Domain Generalization and Subpopulation Shift for Generalizable Graph-Level Anomaly Detection
Authors: Xiaoxiang Li; Xihe Xie; Hai Wan; Xibin Zhao
Venue: AAAI 2026 (main)

Abstract: Graph-level anomaly detection (GLAD), which identifies rare or atypical graphs within a graph set, is crucial for applications such as image analysis, industrial defect inspection and fraud detection. However, existing GLAD approaches typically rely on the in-distribution hypothesis while lacking generalization capability for out-of-distribution (OOD) scenarios (e.g., different graph sizes), which largely limits the application in the real world. For the first time, we formulate the OOD generalization problem for GLAD, where testing graph data exhibit significant distributional shifts from training data.  To tackle two common types of distributional shifts, domain generalization and subpopulation shift, we propose the Fine-Grained Subpopulation Graph-Level Anomaly Detection (FGS-GLAD). First, we propose a Graph Information Bottleneck-based Anomaly Detection Module (GIB4AD) that implements graph reverse distillation and graph information bottleneck on the graph to enhance task-relevant feature extraction for domain generalization. Second, We propose a Fine-Grained Subpoulation Inference Module (FGSI) to predict fine-grained subpopulations and focus on critical inter-subpopulation features through a supervised contrastive mechanism. Experiments on seven benchmark datasets and ten baselines demonstrate our model's superiority in handling domain generalization and subpopulation shift.
RGMP: Recurrent Geometric-prior Multimodal Policy for Generalizable Humanoid Robot Manipulation
Authors: Xuetao Li; Wenke Huang; Nengyuan Pan; Kaiyan Zhao; Songhua Yang; Yiming Wang; Mengde Li; Mang Ye; Jifeng Xuan; Miao Li
Venue: AAAI 2026 (main)

Abstract: Humanoid robots exhibit significant potential in executing diverse human-level skills. However, current research predominantly relies on data-driven approaches that necessitate extensive training datasets to achieve robust multimodal decision-making capabilities and generalizable visuomotor control. These methods raise concerns due to the neglect of geometric reasoning in unseen scenarios and the inefficient modeling of robot-target relationships within the training data, resulting in a significant waste of training resources. To address these limitations, we present the Recurrent Geometric-prior Multimodal Policy (RGMP), an end-to-end framework that unifies geometric-semantic skill reasoning with data-efficient visuomotor control. For perception capabilities, we propose the Geometric-prior Skill Selector, which infuses geometric inductive biases into a vision language model, producing adaptive skill sequences for unseen scenes with minimal spatial common sense tuning. To achieve data-efficient robotic motion synthesis, we introduce the Adaptive Recursive Gaussian Network, which parameterizes robot-object interactions as a compact hierarchy of Gaussian processes that recursively encode multi-scale spatial relationships, yielding dexterous, data-efficient motion synthesis even from sparse demonstrations. Evaluated on both our humanoid robot and desktop robot, the RGMP framework achieves 87% task success in generalization tests and exhibits 5× greater data efficiency than the state-of-the-art model. This performance underscores its superior cross-domain generalization, paving the way for more versatile and data-efficient robotic systems.
Data-Centric Sequential Recommendation with Relation-Augmented Generation
Authors: Yichen Li; Yichen Tan; Yijing Shan; Haozhao Wang; Rui Zhang; Imran Razzak; Ruixuan Li
Venue: AAAI 2026 (main)

Abstract: Data-Centric Sequential Recommendation (DaCSR) has emerged as a promising technique that enhances dataset quality to better capture user preferences without increasing training complexity. However, mining item relations to improve data quality remains challenging due to the intricate nature of interaction sequences. Existing methods predominantly either: 1) optimize models to learn such item relations from fixed datasets at significant training cost, or 2) employ generative models to adaptively learn only interaction patterns, which lack interpretability and cannot guarantee effective data quality enhancement. In this paper, we pioneer a relation-guided dataset augmentation and regeneration framework for sequential recommendation called \textbf{RaSR}. This framework can significantly improve model performance on original datasets while maintaining training efficiency without modifying the model architecture. Specifically, we first preprocess user interactions to construct standardized sequential data and extract semantic representations via a Large Language Model (LLM). We then build a multi-relation graph with manually predefined metrics and semantic representations to generate augmented datasets. Finally, a relation-aware generator can produce regenerated datasets with both the multi-relation graph and the augmented dataset. To verify the effectiveness of RaSR, we conduct experiments on various backbone models and datasets, and achieve significant performance improvement compared to training the model only on the original dataset.
DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs
Authors: Yuan Li; Jun Hu; Bryan Hooi; Bingsheng He; Cheng Chen
Venue: AAAI 2026 (main)

Abstract: Real-world fraud detection applications benefit from graph learning techniques that jointly exploit node features—often rich in textual data—and graph structural information. Recently, Graph-Enhanced LLMs have emerged as a promising graph learning approach that converts graph information into prompts, exploiting LLMs' ability to reason over both textual and structural information. Among them, text-only prompting, which converts graph information into prompts consisting solely of text tokens, offers a solution that relies only on LLM tuning without requiring additional graph-specific encoders. However, text-only prompting struggles on heterogeneous fraud-detection graphs: multi-hop relations expand exponentially with each additional hop, leading to rapidly growing neighborhoods associated with dense textual information. These neighborhoods may overwhelm the model with long, irrelevant content in the prompt and suppress key signals from the target node, thereby degrading performance. To address this challenge, we propose Dual Granularity Prompting (DGP), which mitigates information overload by preserving fine-grained textual details for the target node while summarizing neighbor information into coarse-grained text prompts. DGP introduces tailored summarization strategies for different data modalities—bi-level semantic abstraction for textual fields and statistical aggregation for numerical features—enabling effective compression of verbose neighbor content into concise, informative prompts. Experiments across public and industry datasets demonstrate that DGP operates within a manageable token budget while improving fraud detection performance by up to 6.8% (AUPRC) over state-of-the-art methods, showing the potential of Graph-Enhanced LLMs for fraud detection.
APT: Affine Prototype-Timestamp for Time Series Forecasting Under Distribution Shift
Authors: Yujie Li; Zezhi Shao; Chengqing Yu; Yisong Fu; Tao Sun; Yongjun Xu; Fei Wang
Venue: AAAI 2026 (main)

Abstract: Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that fails to capture global distribution shift. Methods like RevIN and its variants attempt to decouple distribution and pattern but still struggle with missing values, noisy observations, and invalid channel-wise affine transformation. To address these limitations, we propose Affine Prototype-Timestamp(APT), a lightweight and flexible plug-in module that injects global distribution features into the normalization–forecasting pipeline. By leveraging timestamp-conditioned prototype learning,
APT dynamically generates affine parameters that modulate both input and output series, enabling the backbone to learn from self-supervised, distribution-aware clustered instances.
APT is compatible with arbitrary forecasting backbones and normalization strategies while introducing minimal computational overhead. Extensive experiments across six benchmark datasets and multiple backbone-normalization combinations demonstrate that APT significantly improves forecasting performance under distribution shift.
BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation
Authors: Yupeng Li; Mingyue Cheng; Yucong Luo; Yitong Zhou; Qingyang Mao; Shijin Wang
Venue: AAAI 2026 (main)

Abstract: Multi-behavior sequential recommendation aims to capture users' dynamic interests by modeling diverse types of user interactions over time. Although several studies have explored this setting, the recommendation performance remains suboptimal, mainly due to two fundamental challenges: the heterogeneity of user behaviors and data sparsity. To address these challenges, we propose BLADE, a framework that enhances multi-behavior modeling while mitigating data sparsity. Specifically, to handle behavior heterogeneity, we introduce a dual item-behavior fusion architecture that incorporates behavior information at both the input and intermediate levels, enabling preference modeling from multiple perspectives. To mitigate data sparsity, we design three behavior-level data augmentation methods that operate directly on behavior sequences rather than core item sequences. These methods generate diverse augmented views while preserving the semantic consistency of item sequences. These augmented views further enhance representation learning and generalization via contrastive learning. Experiments on three real-world datasets demonstrate the effectiveness of our approach.
Capturing Dynamic User Interests Under Modality Imbalance for Multimodal Sequential Recommendation
Authors: Zilong Li; Jia Zhu; Chenglei Huang; Zhangze Chen; Hanghui Guo; Guoqing Ma; Jianxia Ling
Venue: AAAI 2026 (main)

Abstract: Multimodal sequential recommender systems leverage diverse modal inputs to enhance the accuracy and relevance of personalized recommendations. However, existing fusion strategies often struggle to capture intricate cross-modal interactions, especially under the evolving dynamics of user intent. Moreover, they frequently neglect modality imbalance issues, leading to suboptimal utilization of multimodal information. To address these challenges, we propose DuAF-MAT, a novel framework for robust multimodal sequential recommendation. Our approach consists of three key components: (1) a Dual-Aware Adaptive Fusion (DuAF) module dynamically calibrates modality contributions by jointly modeling user preferences and temporal information, enabling the extraction of multimodal features aligned with evolving user interests; (2) by integrating Modality Adversarial Training with the Mixture-of-Experts paradigm, MAT-MoE employs an ensemble of expert generators to dynamically reconstruct missing modality representations, effectively mitigating modality imbalance challenges; (3) to address the inherent sparsity of sequential behavior data, we propose a Multi-Supervised Contrastive Learning strategy that integrates cross-modal alignment and virtual sequence augmentation. This approach enhances user interest modeling by leveraging diverse learning signals, resulting in improved model robustness and generalization capability. Extensive experiments on four public datasets demonstrate that DuAF-MAT significantly outperforms state-of-the-art baselines.
Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
Authors: Zuqing Li; Junhao Gan; Jianzhong Qi
Venue: AAAI 2026 (main)

Abstract: Diffusion-based tabular data synthesis models have yielded promising results. However, we observe that when the data dimensionality increases, existing models tend to degenerate and may perform even worse than simpler, non-diffusion-based models. This is because limited training samples in high-dimensional space often hinder generative models from capturing the distribution accurately. To mitigate the insufficient learning signals and to stabilize training under such conditions, we propose CtrTab, a condition-controlled diffusion model that injects perturbed ground-truth samples as auxiliary inputs during training. This design introduces an implicit $L_2$ regularization on the model’s sensitivity to the control signal, improving robustness and stability in high-dimensional, low-data scenarios. Experimental results across multiple datasets show that CtrTab outperforms state-of-the-art models, with a performance gap in accuracy over 90% on average.
Beyond Local Patterns: Multiscale Inconsistency Learning for Graph Anomaly Detection
Authors: Jie Lian; Zhihao Wu; Jielong Lu; Jiajun Yu; Qianqian Shen; Haishuai Wang
Venue: AAAI 2026 (main)

Abstract: Graph anomaly detection is emerging as a critical technology for addressing increasingly complex and dynamic risk environments. 
Although unsupervised graph anomaly detection has advanced under the graph representation learning, directly applying these paradigms remains fundamentally misaligned with anomaly detection objectives.
In this work, we highlight two key insights: graph neural networks are often suboptimal as feature extractors due to neighborhood aggregation diluting anomaly signals, and reliance on local inconsistency mining is inadequate for comprehensive anomaly detection, as it often fails to identify anomalies hidden within camouflaged communities.
Based on these insights, we propose multiscale inconsistency learning for graph anomaly detection (MI-GAD), a novel framework that integrates both local and global anomaly signals. Specifically, individual node representations are projected onto a common hypersphere to ensure uniformity. At the local scale, the graph structure is leveraged for affinity-aware modeling via group discrimination. At the global scale, we introduce node deviation, a metric that distinguishes anomalies by optimizing representation centers. This unified approach enables robust and comprehensive detection of diverse graph anomalies.
Experiments on seven real datasets demonstrate that our method consistently outperforms state-of-the-art baselines in both effectiveness and scalability.
Sign-Aware Multimodal Graph Recommendation
Authors: Yahong Lian; Haotian Tian; Chunyao Song; Tingjian Ge
Venue: AAAI 2026 (main)

Abstract: A multimodal recommendation system (MRS), which leverages rich multimodal information to model user preferences, has recently attracted significant research interest. Most existing MRSs focus primarily on developing sophisticated encoders for feature extraction, typically relying on simple aggregation of interaction-based features for final predictions. However, this conventional paradigm fails to account for the critical semantic difference between high- and low-rating interactions: while high ratings indicate user preference, low ratings explicitly convey dissatisfaction. Such oversight of negative feedback semantics may significantly limit the system’s recommendation performance. Recently, sign graphs—which model positive and negative feedback signals separately—have gained considerable attention. Inspired by this approach, we propose Sign-Aware Multimodal Graph Recommendation (SiMGR), a novel framework incorporating signed graphs into multimodal recommendation systems. SiMGR fuses multimodal features with signed interactions in a unified graph framework by integrating modality-specific representations and applying user-level thresholds to separate positive and negative subgraphs. A balanced pseudo-edge augmentation strategy is introduced to alleviate sparsity and enhance generalization. Experiments on three public multimodal recommendation datasets show that SiMGR outperforms state-of-the-art baselines, achieving an average 4.28% improvement in NDCG@20.
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
Authors: Jingyi Liao; Yongyi Su; Rong-Cheng Tu; Zhao Jin; Wenhao Sun; Yiting Li; Xun Xu; Dacheng Tao; Xulei Yang
Venue: AAAI 2026 (main)

Abstract: While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse domains, their application to specialized anomaly detection (AD) remains constrained by domain adaptation challenges. Existing Group Relative Policy Optimization (GRPO) based approaches suffer from two critical limitations: inadequate training data utilization when models produce uniform responses, and insufficient supervision over reasoning processes that encourage immediate binary decisions without deliberative analysis.
We propose a comprehensive framework addressing these limitations through two synergistic innovations. First, we introduce a multi-stage deliberative reasoning process that guides models from region identification to focused examination, generating diverse response patterns essential for GRPO optimization while enabling structured supervision over analytical workflows. Second, we develop a fine-grained reward mechanism incorporating classification accuracy and localization supervision, transforming binary feedback into continuous signals that distinguish genuine analytical insight from spurious correctness.
Comprehensive evaluation across multiple industrial datasets shows that our method achieves superior accuracy by enabling general-purpose MLLMs to acquire fine-grained visual discrimination for detecting subtle manufacturing defects.
Stepwise Contrastive Reasoning for Retrieval-Augmented Generation over Knowledge Graphs
Authors: Chenxiao Lin; Ye Luo; KunHong Liu; Qingqiang Wu
Venue: AAAI 2026 (main)

Abstract: Retrieval-augmented generation (RAG) enhances the reasoning capabilities of large language models (LLMs) by incorporating external knowledge. Among available sources, knowledge graphs (KGs) offer a structured and reliable foundation for factual information, making them increasingly popular in efforts to improve reasoning faithfulness in RAG. Most existing KG-based RAG methods rely on LLMs to extract knowledge from KGs. However, these approaches often require costly fine-tuning and struggle to navigate deep graph structures, limiting their effectiveness in multi-hop reasoning tasks. To address these challenges, we propose Stepwise Contrastive Reasoning (SCR), a lightweight framework that integrates graph structure and textual context for efficient and interpretable RAG over KGs. SCR combines relational message passing layers to encode KG entities with a Transformer encoder for processing question text. It decomposes reasoning into a series of alignment steps. At each step, SCR compares the current topic entity and its neighbors with the question representation, selecting the most relevant entity as the next topic entity. The question is then updated with this entity's textual description. This process continues until the selected entity no longer changes, indicating that the answer entity has been reached. Through stepwise alignment, SCR enables compact models to perform faithful and interpretable reasoning over large-scale KGs. Extensive experiments on several widely used KGQA benchmarks demonstrate that SCR not only achieves state-of-the-art performance but also effectively boosts the capabilities of smaller language models to match those of LLMs.