GenAI4ELab/papercli-papers huggingface dataset:
GUIDER: Uncertainty Guided Dynamic Re-ranking for Large Language Models Based Recommender Systems
Authors: Cai Xu; Xujing Wang; Ziyu Guan; Wei Zhao; Meng Yan
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) are increasingly integral to recommendation systems, offering sophisticated language understanding and generation capabilities. However, their practical application is often hindered by challenges such as data sparsity, the generation of unreliable or hallucinated recommendations, and a general lack of transparency in their decision-making processes. Existing mitigation strategies frequently introduce significant complexity or computational overhead. To address these limitations, particularly the critical gap in quantifying the confidence of LLM-generated recommendations, we propose GUIDER: Uncertainty Guided Dynamic Re-ranking for Large Language Models based Recommender Systems. This new framework innovatively leverages the logits produced by LLMs as evidence for recommended items. By employing a Dirichlet distribution, GUIDER decomposes the total predictive uncertainty into distinct Data Uncertainty (DU), reflecting inherent data ambiguity, and Model Uncertainty (MU), indicating the model's own conviction. This principled decomposition, achieved with a single inference pass, enhances transparency and trustworthiness. Based on the quantified DU and MU levels, our system dynamically adapts its recommendation strategy---adjusting output diversity---through a four-quadrant analysis that tailors responses to specific uncertainty profiles. Extensive experiments conducted in zero-shot recommendation settings validate the effectiveness of our approach. GUIDER consistently outperforms existing methods in reliability-aware scenarios, demonstrably improving recommendation quality. This framework not only advances the practical deployment of LLM-based recommenders by making them more dependable but also provides a robust foundation for future research into uncertainty-aware generative systems.
Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation
Authors: Huayang Xu; Huanhuan Yuan; Guanfeng Liu; Junhua Fang; Lei Zhao; Pengpeng Zhao
Venue: AAAI 2026 (main)

Abstract: Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users’ historical interaction data. Given that users’ complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. However, current frequency-domain-based methods suffer from two key limitations: (i) They primarily employ static filters with fixed characteristics, overlooking the personalized nature of behavioral patterns; (ii) While the global discrete Fourier transform excels at modeling long-range dependencies, it can blur non-stationary signals and short-term fluctuations. To overcome these limitations, we propose a novel method called Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation (WEARec). Specifically, it consists of two vital modules: dynamic frequency-domain filtering and wavelet feature enhancement. The former is used to dynamically adjust filtering operations based on behavioral sequences to extract personalized global information, and the latter integrates wavelet transform to reconstruct sequences, enhancing blurred non-stationary signals and short-term fluctuations. Finally, these two modules work synergistically to achieve comprehensive performance and efficiency optimization in long sequential recommendation scenarios. Extensive experiments on four widely-used benchmark datasets demonstrate the superiority of WEARec.
Bridging Optimization and Neural Networks for Efficient Multi-view Clustering
Authors: Huilang Xu; Xiang-Xiang Su; Simin Chen; Guang-Yong Chen; Xing Chen
Venue: AAAI 2026 (main)

Abstract: Multi-view clustering (MVC) seeks to uncover the intrinsic group structures embedded in multi-view data, which has attracted considerable attention in recent years. Existing approaches predominantly concentrate on incorporating suitable model priors to capture consistency across views. However, these explicit constraints often fail to hold in scenarios involving significant modal differences between views or the presence of noise, thereby limiting the efficacy of these methods in more complex contexts. To address these issues, this paper introduces BONE, a lightweight and interpretable MVC framework that Bridges Optimization and Neural networks for Efficient MVC. By leveraging learnable parameters to extract high-level features from low-level features derived through classical optimization, BONE integrates the consistency information across views without the need for explicit prior constraints, while eliminating the necessity for pre-training or post-processing. Extensive experiments show that BONE achieves clustering performance comparable to or even better than existing deep MVC methods, while using only 1% of the parameters, offering a new perspective for designing efficient MVC algorithms.
IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection
Authors: Yang Xu; Yixiao Ma; Kaifeng Zhang; Zuliang Yang; Kai Ming Ting
Venue: AAAI 2026 (main)

Abstract: Anomaly detection on data streams presents significant challenges, requiring methods to maintain high detection accuracy among evolving distributions while ensuring real-time efficiency. Here we introduce IDK-S, a novel Incremental Distributional Kernel for Streaming anomaly detection that effectively addresses these challenges by creating a new dynamic representation in the kernel mean embedding framework. The superiority of IDK-S is attributed to two key innovations. First, it inherits the strengths of the Isolation Distributional Kernel, an offline detector that has demonstrated significant performance advantages over foundational methods like Isolation Forest and Local Outlier Factor due to the use of a data-dependent kernel. Second, it adopts a lightweight incremental update mechanism that significantly reduces computational overhead compared to the naive baseline strategy of performing a full model retraining. This is achieved without compromising detection accuracy, a claim supported by its statistical equivalence to the full retrained model. Our extensive experiments on thirteen benchmarks demonstrate that IDK-S achieves superior detection accuracy while operating substantially faster, in many cases by an order of magnitude, than existing state-of-the-art methods.
SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection
Authors: Yang Xu; Hang Zhang; Yixiao Ma; Ye Zhu; Kai Ming Ting
Venue: AAAI 2026 (main)

Abstract: The core problem in multi-view anomaly detection is to represent local neighborhoods of normal instances consistently across all views. Recent approaches consider a representation of local neighborhood in each view independently, and then capture the consistent neighbors across all views via a learning process. They suffer from two key issues. First, there is no guarantee that they can capture consistent neighbors well, especially when the same neighbors are in regions of varied densities in different views, resulting in inferior detection accuracy. Second, the learning process has a high computational cost of O(N^2), rendering them inapplicable for large datasets. To address these issues, we propose a novel method termed Spherical Consistent Neighborhoods Ensemble (SCoNE). It has two unique features: (a) the consistent neighborhoods are represented with multi-view instances directly, requiring no intermediate representations as used in existing approaches; and (b) the neighborhoods have data-dependent properties, which lead to large neighborhoods in sparse regions and small neighborhoods in dense regions. The data-dependent properties enable local neighborhoods in different views to be represented well as consistent neighborhoods, without learning. This leads to O(N) time complexity. Empirical evaluations show that SCoNE has superior detection accuracy and runs orders-of-magnitude faster in large datasets than existing approaches.
A Novel Retrieve-Read-Group Paradigm for Open Knowledge Base Canonicalization
Authors: Binhan Yang; Wei Shen; Han Tian
Venue: AAAI 2026 (main)

Abstract: Noun phrases (NPs) in open knowledge bases (OKBs) are not canonicalized, leading to scattered knowledge that necessitates the exploration of the OKB canonicalization task (i.e., clustering synonymous noun phrases into the same group and assigning them a unique identifier). However, existing OKB canonicalization methods typically adhere to a traditional embedding-centered pipeline, which fails to exploit the direct interaction between NPs for pairwise NP similarity calculations, resulting in suboptimal performance and instead relying extensively on external resources. To address these limitations, we introduce a groundbreaking retrieve-read-group paradigm that enables fine-grained pairwise NP similarity calculations by effectively leveraging the direct NP interaction via the reading stage, thereby relieving the reliance on external resources. As an instantiation of this paradigm, we propose DUVK, a novel self-supervised framework that fully integrates the dual-view knowledge involved in OKBs from the relational view and the semantic view. In the retriever component of DUVK, a dual-view cross-training strategy is designed to make two view-specific encoders mutually reinforce each other by capitalizing on the complementary knowledge delivered from both views. Experimental results demonstrate that, even without the need of any external resources, DUVK outperforms all state-of-the-art competitors that rely on such resources.
DRSoRec: Dual-Rectification of Social Networks for Recommendation
Authors: Liangxun Yang; Tianzi Zang; Jiayi Sun; Juan Li; Yicong Li
Venue: AAAI 2026 (main)

Abstract: Leveraging social homophily to enhance user preference modeling, social recommendation has become a cornerstone of modern recommender systems. However, the raw social network contains inherent unreliability as it teems with noise---misclicks, bot-generated and transient ties---while many meaningful links remain unobserved. In this study, we propose DRSoRec, a dual-rectification model to rectify the raw social networks by simultaneously removing noisy signals and preserving useful information. Specifically, the invariant social rationale discovery module distills each user's influential core social circle of the current recommendation, whereas the adaptive social connection refinement module employs a mixture-of-experts structure learner to prune spurious edges and uncover latent links. A contrastive optimization objective is designed to align and mutually enhance these two modules, and the refined user representations are fused with collaborative representations generated from interactions for the final recommendation. Experiments on three public datasets confirm that DRSoRec consistently gains over state-of-the-art baselines.
GIER: Addressing Class Imbalance in GNNs Through Experience Replay
Authors: Liu Yang; Chuyao Liu; Zidong Wang; Tingxuan Chen; Mengni Chen; Hongyu Zhang
Venue: AAAI 2026 (main)

Abstract: The prevalent class imbalance in real-world graphs significantly affects the performance of Graph Neural Networks (GNNs). Existing methods for analyzing graph imbalance ignore the influence of minority nodes during the dynamic model training process, resulting in performance limitations. In this paper, we focus on minority class information during model training, identifying and defining the minority class forgetting phenomenon that exists in graph imbalanced method training processes. To address this issue, we propose Graph Imbalance Experience Replay(GIER) framework. On one hand, the method enhances the model's ability to mine minority node information in historical data, thereby achieving feature completion for minority class nodes. On the other hand, the proposed short-term confidence mechanism allows the model to adaptively calibrate the topological relationships in high-confidence nodes, thereby mitigating the model's tendency to propagate erroneous information about minority classes during training. GIER is a unified framework consisting of two synergistic components: Long-term Subgraph Memory (LSM) constructs multi-period feature-representative subgraphs to address distribution imbalance, and Short-term Confidence Calibration (SCC) dynamically reconstructs graph topology through degree-aware node selection and confidence-based filtering to address topological imbalance. The extensive experimental results demonstrate that GIER effectively improves the classification performance of GNNs on imbalanced graphs, achieving up to a 3.44% improvement in BAcc over the state-of-the-art, and is particularly effective in extreme scenarios with very small minority classes.
Structural Entropy Guided Incremental Learning for Open-World Multimodal Social Event Detection
Authors: Zhiwei Yang; Haimei Qin; Xiaoyan Yu; Hao Peng; Lei Jiang; Li Sun; Zhiqin Yang
Venue: AAAI 2026 (main)

Abstract: With the explosive growth of multimodal data streams on social media, the timely detection of emerging social events has become increasingly important. 
As a result, Multimodal Social Event Detection in open-world settings is receiving growing attention.
However, most existing methods face two major limitations:
(1) They overlook the dynamic nature of open-world social media data and fail to design dedicated incremental learning frameworks.
(2) They ignore the impact of noise in streaming data, leading to performance degradation over long-term detection.
To overcome these limitations, we propose SeInEvent (**S**tructural **E**ntropy Guided **In**cremental Learning for Open-World Multimodal Social **Event** Detection).
Our innovations are as follows:
**First**, considering data dynamics, we design a self-supervised alternating incremental contrastive learning mechanism.
Through knowledge distillation, historical event clusters were reviewed and consolidated, and contrastive learning was combined to absorb knowledge of unknown events, ultimately achieving incremental learning without labels.
**Second**, addressing the impact of noise, we propose a Pointwise Structural Entropy-based noise filter, which quantifies each sample’s informational contribution to the event clustering structure. 
It enables automatic removal of noisy data and supports robust long-term detection.
Extensive experiments on two public datasets demonstrate that SeInEvent achieves superior performance.
Bipartite Mode Matching for Vision Training Set Search from a Hierarchical Data Server
Authors: Yue Yao; Ruining Yang; Tom Gedeon
Venue: AAAI 2026 (main)

Abstract: We explore a situation in which the target domain is accessible, but real-time data annotation is not feasible. Instead, we would like to construct an alternative training set from a large-scale data server so that a competitive model can be obtained. For this problem, because the target domain usually exhibits distinct modes (i.e., semantic clusters representing data distribution), if the training set does not contain these target modes, the model performance would be compromised. While prior existing works improve algorithms iteratively, our research explores the often-overlooked potential of optimizing the structure of the data server. Inspired by the hierarchical nature of web search engines, we introduce a hierarchical data server, together with a bipartite mode matching algorithm (BMM) to align source and target modes. For each target mode, we look in the server data tree for the best mode match, which might be large or small in size. Through bipartite matching, we aim for all target modes to be optimally matched with source modes in a one-on-one fashion. Compared with existing training set search algorithms, we show that the matched server modes constitute training sets that have consistently smaller domain gaps with the target domain across object re-identification (re-ID) and detection tasks. Consequently, models trained on our searched training sets have higher accuracy than those trained otherwise. BMM allows data-centric unsupervised domain adaptation (UDA) orthogonal to existing model-centric UDA methods. By combining the BMM with existing UDA methods like pseudo-labeling, further improvement is observed.
DiM-TS: Bridge the Gap Between Selective State Space Models and Time Series for Generative Modeling
Authors: Zihao Yao; Jiankai Zuo; Yaying Zhang
Venue: AAAI 2026 (main)

Abstract: Time series data plays a pivotal role in a wide variety of fields but faces challenges related to privacy concerns. Recently, synthesizing data via diffusion models is viewed as a promising solution. However, existing methods still struggle to capture long-range temporal dependencies and complex channel interrelations. In this research, we aim to utilize the sequence modeling capability of a State Space Model called Mamba to extend its applicability to time series data generation. We firstly analyze the core limitations in State Space Model, namely the lack of consideration for correlated temporal lag and channel permutation. Building upon the insight, we propose Lag Fusion Mamba and Permutation Scanning Mamba, which enhance the model's ability to discern significant patterns during the denoising process. Theoretical analysis reveals that both variants exhibit a unified matrix multiplication framework with the original Mamba, offering a deeper understanding of our method. Finally, we integrate two variants and introduce Diffusion Mamba for Time Series (DiM-TS), a high-quality time series generation model that better preserves the temporal periodicity and inter-channel correlations. Comprehensive experiments on public datasets demonstrate the superiority of DiM-TS in generating realistic time series while preserving diverse properties of data.
Making Visual Dialogue More Engaging: A New Task, Method, and Metric
Authors: Guanghui Ye; Huan Zhao; Yingxue Gao; Zhixue Zhao; Kehan Wang; Xupeng Zha; Zhihua Jiang
Venue: AAAI 2026 (main)

Abstract: Large language model (LLM)-based visual dialogue (VD) systems have made response generation for image-grounded conversations more correct and coherent. However, user engagement - the extent to which a user is interested, emotionally involved, and willing to continue the conversation - remains a challenge. To fully explore engaging VD, we propose: (i) a new task named Audio-enhanced VD (AVD), which introduces additional audio dialogue contexts that can more vividly convey the speaker's emotions as input, with the aim of generating correct but more engaging dialogue responses. Specifically, we employ a text-to-speech model as the modality translator to generate the paired acoustic utterances from the inputting textual utterances; (ii) an accompanying approach named Visually-grounded and Interleaved Text-Audio  Dialogue Modeling (VITA-DM), which utilizes both image-grounded information and interleaved text-audio utterances for visual dialogue modeling, differentiating from previous multi-modal LLM (MLLM)-based methods that normally model text and audio modalities separately. We also present three pre-training tasks to better learn multi-modal interactions across language, vision, and audio; (iii) a novel metric named Multi-Modal Engagement (MME), which fills the gap of engagement estimation in VD and can provide a fine-grained assessment along emotional, attentional, and reply engagement dimensions (EE, AE, RE). We experiment on two popular datasets and provide extensive evaluations (automatic, engagement-specific, and human), supporting the validity of our approach. Furthermore, based on empirical results that reveal that emotions contribute the most to engagement, we justify our emphasis on the emotional aspect throughout the definition, solution, and evaluation of our task.
Align³GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation
Authors: Wencai Ye; Mingjie Sun; Shuhang Chen; Wenjin Wu; Peng Jiang
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) demonstrate significant advantages in leveraging structured world knowledge and multi-step reasoning capabilities. However, fundamental challenges arise when transforming LLMs into real-world recommendation systems due to semantic and behavioral misalignment. 
To bridge this gap, we propose Align³GR, a novel framework that unifies token-level, behavior modeling-level, and preference-level alignment. Our approach introduces: Dual tokenization fusing user-item semantic and collaborative signals. Enhanced behavior modeling with bidirectional semantic alignment. Progressive DPO strategy combining self-play (SP-DPO) and real-world feedback (RF-DPO) for dynamic preference adaptation. Experiments show Align³GR outperforms the SOTA baseline by +17.8% in Recall@10 and +20.2% in NDCG@10 on the public dataset, with significant gains in online A/B tests and full-scale deployment on an industrial large-scale recommendation platform.
FairGSE: Fairness-Aware Graph Neural Network Without High False Positive Rates
Authors: Zhenqiang Ye; Jinjie Lu; Tianlong Gu; Fengrui Hao; Xuemin Wang
Venue: AAAI 2026 (main)

Abstract: Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also amplifies biases inherent in graph topology, raising fairness concerns. Existing fairness-aware GNNs provide satisfactory performance on fairness metrics such as Statistical Parity and Equal Opportunity while maintaining acceptable accuracy trade-offs. Unfortunately, we observe that this pursuit of fairness metrics neglects the GNN's ability to predict negative labels, which renders their predications with extremely high False Positive Rates (FPRs), resulting in negative effects in high-risk scenarios. To this end, we advocate that classification performance should be carefully calibrated while improving fairness, rather than simply constraining accuracy loss. Furthermore, we propose Fair GNN via Structural Entropy (FairGSE), a novel framework that maximizes two-dimensional structural entropy (2D-SE) to improve fairness without neglecting false positives. Experiments on several real-world datasets show FairGSE reduces FPR by 39% vs. state-of-the-art fairness-aware GNNs, with comparable fairness improvement.