GenAI4ELab/papercli-papers huggingface dataset:
Rethinking Irregular Time Series Forecasting: A Simple Yet Effective Baseline
Authors: Xvyuan Liu; Xiangfei Qiu; Xingjian Wu; Zhengyu Li; Chenjuan Guo; Jilin Hu; Bin Yang
Venue: AAAI 2026 (main)

Abstract: The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.
Quality-aware and Soft Consistency Driven Representation Fusion for Incomplete Multi-view Multi-label Classification
Authors: Yadong Liu; Waikeung Wong; Yulong Chen; Jie Wen
Venue: AAAI 2026 (main)

Abstract: Multi-view multi-label classification aims to utilize the rich information contained in multiple views for accurate classification. However, in real-world applications, its performance is often severely constrained by the concurrent missingness of both views and labels. To address this problem, this paper first targets the drawback of representation degradation in traditional feature disentanglement methods caused by strong consistency constraints and proposes a soft consistency constraint. This constraint not only effectively aligns the shared information and maximally avoids the compression of information beneficial to the classification task, but it also enhances the aggregation effect of high-quality representations on other representations. Furthermore, to address the coarse-grained problem of traditional fusion strategies, we designed a quality assessment network that achieves instance-level dynamic weighted fusion in a data-driven manner. Extensive experiments on multiple benchmark datasets demonstrate that our method achieves state-of-the-art performance in both incomplete and complete data scenarios, showcasing its robustness and generality.
Who Should I Trust? Explicit Confidence-Focused Multimodal Intent Recognition
Authors: Yi Liu; Qimeng Yang; Lanlan Lu
Venue: AAAI 2026 (main)

Abstract: Multimodal intent recognition is aimed at understanding user intentions by integrating information from multiple modalities. It has attracted increasing attention in recently developed dialog systems. The existing studies have focused mainly on modeling semantic interactions within and across modalities, but they often overlook the reliability of each modality. In real-world scenarios, inputs may be corrupted by noisy audio, blurred or occluded videos, or ambiguous text, making it difficult for the employed model to determine who to trust and how much to trust. To address this challenge, we propose a method called explicit confidence-focused multimodal intent recognition (ECFMIR). The core idea of this approach is to assign each modality and each cross-modal associations feature a dedicated confidence lens (CLens) that explicitly estimates the confidence level in a hypothetical manner. This design helps reduce the degree of uncertainty and mitigate the risk of incorrect predictions when addressing conflicting inputs. Comprehensive experiments conducted on two benchmark multimodal intent recognition datasets demonstrate the effectiveness of our method. A further analysis reveals that ECFMIR achieves significant advantages for high-conflict categories and under low-resource conditions.
Sparse-Scale Transformer with Bidirectional Awareness for Time Series Forecasting
Authors: Ying Liu; Bo Liu; Sheng Huang; Gang Luo; Wenbo Hu; Meng Wang; Richang Hong
Venue: AAAI 2026 (main)

Abstract: Time series forecasting (TSF) plays a crucial role in many real-world applications, such as weather prediction and economic planning. While Transformer-based models have shown strong capabilities in modeling long-range dependencies, effectively capturing the multi-scale temporal dynamics inherent in time series remains a major challenge. Existing methods often adopt time-windows of varying sizes, which may introduce noisy or irrelevant representations when mismatched with the underlying temporal patterns, potentially leading to overfitting. In this paper, we propose Sparse-Scale Transformer (SSformer) with Bidirectional Awareness for Time Series Forecasting to enhance the multi-scale modeling for time series. Specifically, we propose a novel Sparse-Scale Convolution (SSC) block that imposes sparsity on scales to obtain the informative representations by evaluating the intra-scale segment similarity of time series, and utilizes scale-specific convolutions to extract local patterns. Furthermore, we design a Bidirectional-Scale Interaction (BSI) block to explicitly model scale correlations in both coarse-to-fine and fine-to-coarse directions. Finally, scale predictions are ensembled to fully exploit the complementary forecasting capabilities across scales. Extensive experiments on various real-world datasets demonstrate that SSformer achieves state-of-the-art performance with superior efficiency.
Neighbor-aware Instance Refining with Noisy Labels for Cross-Modal Retrieval
Authors: Yizhi Liu; Ruitao Pu; Shilin Xu; Yingke Chen; Quan-Hui Liu; Yuan Sun
Venue: AAAI 2026 (main)

Abstract: In recent years, Cross-Modal Retrieval (CMR) has made significant progress in the field of multi-modal analysis. However, since it is time-consuming and labor-intensive to collect large-scale and well-annotated data, the annotation of multi-modal data inevitably contains some noise. This will degrade the retrieval performance of the model. To tackle the problem, numerous robust CMR methods have been developed, including robust learning paradigms, label calibration strategies, and instance selection mechanisms. Unfortunately, they often fail to simultaneously satisfy model performance ceilings, calibration reliability, and data utilization rate. To overcome the limitations, we propose a novel robust cross-modal learning framework, namely Neighbor-aware Instance Refining with Noisy Labels (NIRNL). Specifically, we first propose Cross-modal Margin Preserving (CMP) to adjust the relative distance between positive and negative pairs, thereby enhancing the discrimination between sample pairs. Then, we propose Neighbor-aware Instance Refining (NIR) to identify pure subset, hard subset, and noisy subset through cross-modal neighborhood consensus. Afterward, we construct different tailored optimization strategies for this fine-grained partitioning, thereby maximizing the utilization of all available data while mitigating error propagation. Extensive experiments on three benchmark datasets demonstrate that NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.
Time Series Class-Incremental Learning via Confidence-guided Mask Distillation and Prototype-guided Contrastive Learning
Authors: Yu Liu; Haoqin Yang; Jinping Sui; Hui Wang; Haipeng Li; Weimin Wang; Qi Jia
Venue: AAAI 2026 (main)

Abstract: Class-incremental learning (CIL) has recently gained great attention in the field of time series classification.
Existing CIL methods based on knowledge distillation exhibit impressive ability to retain prior knowledge and overcome catastrophic forgetting, however, their effectiveness faces  major challenges posed by time series data.
Since temporal data is more susceptible to sensor errors and electronic noise,
the distillation process may be significantly affected by noisy knowledge transfer.
To address this issue, we propose a novel confidence-guided mask distillation (CMD) framework,
to prevent the noisy inheritance during distillation. 
The core of CMD lies in a dynamic masking mechanism guided by prediction confidence, 
capable of allocating higher weights to high-confidence time series and substantially suppressing
the influence of low-confidence ones.
Additionally, different from prior work simply passing a set of feature prototypes to the classifier,
we develop prototype-guided contrastive learning (PCL) to alleviate the classifier bias on new
classes, through extra contrastive constraints to push away the feature distributions of old feature prototypes from those of new classes features.
Extensive experiments on three time-series datasets demonstrate that, our method significantly outperforms other replay-free CIL approaches in raising average accuracy, as well as decreasing forgetting rate.
Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs
Authors: Yuxuan Liu; Wenchao Xu; Haozhao Wang; Zhiming He; Zhaofeng Shi; Chongyang Xu; Peichao Wang; Boyuan Zhang
Venue: AAAI 2026 (main)

Abstract: Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly assume that all features are equally transferable across clients, overlooking both the spatial and temporal heterogeneity and the presence of client-specific knowledge in real-world graphs. In this work, we identify that such assumptions create a vicious cycle of spurious representation entanglement, client-specific interference, and negative transfer, degrading generalization performance in Federated Learning over Dynamic Spatio-Temporal Graphs (FSTG). To address this issue, we propose a novel causality-inspired framework named SC-FSGL, which explicitly decouples transferable causal knowledge from client-specific noise through representation-level interventions. Specifically, we introduce a Conditional Separation Module that simulates soft interventions through client conditioned masks, enabling the disentanglement of invariant spatio-temporal causal factors from spurious signals and mitigating representation entanglement caused by client heterogeneity. In addition, we propose a Causal Codebook that clusters causal prototypes and aligns local representations via contrastive learning, promoting cross-client consistency and facilitating knowledge sharing across diverse spatio-temporal patterns. Experiments on five diverse heterogeneity Spatio-Temporal Graph (STG) datasets show that SC-FSGL outperforms state-of-the-art methods.
SHADOW: Dynamic-Aware Credit Assignment Against Long-Horizon Tasks
Authors: Yuze Liu; Chaochao Lu; Chao Yang
Venue: AAAI 2026 (main)

Abstract: Reinforcement learning (RL) has emerged as the predominant paradigm for training large language model (LLM) agents to solve complex, multi-step tasks through environmental interaction. A fundamental challenge in such long-horizon scenarios is credit assignment, as delayed rewards provide inadequate signals for evaluating individual action contributions. 
Existing methods typically neglect trajectory transition dynamics, which leads to coarse-grained or biased credit assignment.
To address these limitations, we introduce SHADOW, a novel framework that systematically incorporates transition dynamics for improved credit assignment. Our framework makes two primary contributions: (i) a dynamics-aware state grouping mechanism that mitigates misleading action comparisons between dynamically inconsistent states, and (ii) a local dynamic advantage estimator that leverages Generalized Advantage Estimation (GAE) to precisely quantify individual action contributions through a fine-grained analysis of transition patterns. 
Comprehensive experiments conducted with the Qwen2.5-1.5/7B-Instruct agent model demonstrate that our method achieves success rate improvements of 9.4%/7.6% on the ALFworld benchmark and a performance gain of over 5% on WebShop.
Redundancy-optimized Multi-head Attention Networks for Multi-view Multi-label Feature Selection
Authors: Yuzhou Liu; Jiarui Liu; Wanfu Gao
Venue: AAAI 2026 (main)

Abstract: Multi-view multi-label data offers richer perspectives for artificial intelligence, but simultaneously presents significant challenges for feature selection due to the inherent complexity of interrelations among features, views and labels. Attention mechanisms provide an effective way for analyzing these intricate relationships. They can compute importance weights for information by aggregating correlations between Query and Key matrices to focus on pertinent Values. However, existing attention-based feature selection methods predominantly focus on intra-view relationships, neglecting the complementarity of inter-view features and the critical feature-label correlations. Moreover, they often fail to account for feature redundancy, potentially leading to suboptimal feature subsets. To overcome these limitations, we propose a novel method based on Redundancy-optimized Multi-head Attention Networks for Multi-view Multi-label Feature Selection (RMAN-MMFS). Specifically, we employ each individual attention head to model intra-view feature relationships and use the cross-attention mechanisms between different heads to capture inter-view feature complementarity. Furthermore, we design static and dynamic feature redundancy terms: the static term mitigates redundancy within each view, while the dynamic term explicitly models redundancy between unselected and selected features across the entire selection process, thereby promoting feature compactness. Comprehensive evaluations on six real-world datasets, comparing against six multi-view multi-label feature selection methods, demonstrate the superior performance of the proposed method.
GLOBA: Rethinking Parameter Conflicts in Model Merging
Authors: Zehao Liu; Kun Li; Wei Zhou
Venue: AAAI 2026 (main)

Abstract: Model merging serves as a training-free technique that combines multiple task-specific models into a unified multi-task model, but parameter conflicts often lead to performance drops. Previous methods flatten weight matrices into one-dimensional vectors, losing the inherent structural information of their row and column spaces. We mathematically prove and experimentally validate that parameter conflicts arise from non-orthogonal components of task vectors, while orthogonal components are conflict-free. Furthermore, we find that non-orthogonal components can contain both harmful conflicts and beneficial synergies. To precisely locate parameter conflicts and extract orthogonal components, we propose GLOBA (GLObal Basis Analysis Framework), which projects task vectors onto a global basis to align them within a unified coordinate system and construct a task interaction matrix. Following energy-based pruning, we divide parameters into five types based on the orthogonal relationships between the row spaces and column spaces of task vectors. Experiments on three fine-tuned models (mathematics, coding, and instruction-following) using LLaMA-2-7B and LLaMA-2-13B demonstrate significant performance gains through selective retention of beneficial parameters and removal of conflicting ones.
DA-DFGAS:Differentiable Federated Graph Neural Architecture Search with Distribution-Aware Attentive Aggregation
Authors: Zhaowei Liu; Yihao Jiang; Rufei Gao; Jinglei Liu; Dong Yang
Venue: AAAI 2026 (main)

Abstract: Graph Neural Networks (GNNs) have demonstrated superior performance in processing centralized graph-structured data. However, real-world privacy and security concerns hinder data centralization and shareing, leading to severe data isolation (data silos). While Federated Learning (FL) offers a distributed solution to mitigate these obstacles, existing Federated Graph Neural Network (FedGNN) frameworks struggle to effectively address data heterogeneity. To address this, this paper proposes DA-DFGAS, a federated graph neural architecture search algorithm. Specifically, DA-DFGAS facilitates model personalization via a directed tree topology and path constraint mechanisms, while simultaneously employing a joint self-attention mechanism based on predicted probability distributions to capture distributional variations across multiple clients. Furthermore, it integrates a bi-level global-local objective optimization strategy to ensure global model consistency while preserving local adaptability. Experimental results on multiple datasets demonstrate that DA-DFGAS outperforms state-of-the-art methods, achieving 0.5–3.0% accuracy improvements over centralized baselines and 0.5–5.0% over federated counterparts.
A Unified Shape-Aware Foundation Model for Time Series Classification
Authors: Zhen Liu; Yucheng Wang; Boyuan Li; Junhao Zheng; Emadeldeen Eldele; Min Wu; Qianli Ma
Venue: AAAI 2026 (main)

Abstract: Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelets that capture class-discriminative temporal features. To bridge this gap,  we propose UniShape, a unified shape-aware foundation model designed for time series classification. UniShape incorporates a shape-aware adapter that adaptively aggregates multiscale discriminative subsequences (shapes) into class tokens,  effectively selecting the most relevant subsequence scales to enhance model interpretability. Meanwhile, a prototype-based pretraining module is introduced to jointly learn instance- and shape-level representations, enabling the capture of transferable shape patterns. Pre-trained on a large-scale multi-domain time series dataset comprising 1.89 million samples, UniShape exhibits superior generalization across diverse target domains. Experiments on 128 UCR datasets and 30 additional time series datasets demonstrate that UniShape achieves state-of-the-art classification performance, with interpretability and ablation analyses further validating its effectiveness.
Sample Weighted Incomplete Multimodal Clustering Based on Graph Coarsening Label Extraction
Authors: Zhenjiao Liu; Xue Xiao; Yao Chen; Jiao Xue; Shubin Ma; Liang Zhao
Venue: AAAI 2026 (main)

Abstract: Multimodal data is typically collected through heterogeneous sensors and processing pipelines. However, due to variations in acquisition environments, device capabilities, and feature extraction methods, such data often suffers from incompleteness and inconsistent quality across modalities. To address these challenges, prior studies have explored modality selection and data completion strategies to improve information fusion. Nevertheless, these approaches face two main limitations: (1) they struggle to simultaneously ensure computational efficiency for large-scale graph data and maintain structural and semantic consistency across heterogeneous modality graphs; and (2) most of them operate at the modality level and fail to capture fine-grained, sample-specific quality variations.

To overcome these issues, we propose a novel clustering framework, Sample Weighted Incomplete Multimodal Clustering Based on Graph Coarsening Label Extraction (IMC-GCSW). The proposed method introduces a graph coarsening-based label extraction strategy. It significantly reduces the computational cost of multimodal graph processing, while preserving key node information and local topological structures. Furthermore, a quality-aware sample weighting strategy is designed to enable fine-grained modeling of modality-specific data quality, allowing the model to dynamically suppress the influence of low-quality modalities on individual samples. Experiments on both general-purpose datasets and the Fructus Aurantii Disease and Pest Datasets demonstrate that the proposed method exhibits superior performance and strong adaptability in handling multimodal data with incompleteness and quality inconsistency.
Perceiving the Knowledge Boundary: Uncertainty-Guided Exploration and Imagination for World Models
Authors: Zhenxian Liu; Peixi Peng; Yangru Huang; Yonghong Tian
Venue: AAAI 2026 (main)

Abstract: World-model-based reinforcement learning achieves high sample efficiency by learning from imagined rollouts. However, its success critically depends on the accuracy of the learned world model, which is prone to producing unrealistic or hallucinated rollouts when queried beyond its domain of competence. These flawed predictions can trap the agent in a vicious cycle: by misleading exploration toward implausible or uninformative regions, they degrade the quality of collected data, which in turn corrupts policy learning with inaccurate rollouts. To break this cycle, we introduce the notion of a knowledge boundary—the region within which the world model provides reliable predictions—and propose a unified framework that both identifies and leverages this boundary. Concretely, we approximate the boundary using model uncertainty, quantified via disagreement across an ensemble of lightweight predictors, which serves as a practical proxy. This uncertainty signal is used in two complementary ways: as an intrinsic reward to guide exploration toward under-explored yet learnable regions, and as a dynamic filter to exclude unreliable imagined rollouts from policy optimization. Extensive experiments across diverse benchmarks—including CARLA, DeepMind Control Suite, Atari, and MemoryMaze—demonstrate that our approach consistently outperforms prior state-of-the-art methods.
Beyond Immediate Activation: Temporally Decoupled Backdoor Attacks on Time Series Forecasting
Authors: Zhixin Liu; Xuanlin Liu; Sihan Xu; Yaqiong Qiao; Ying Zhang; Xiangrui Cai
Venue: AAAI 2026 (main)

Abstract: Existing backdoor attacks on multivariate time series (MTS) forecasting enforce strict temporal and dimensional coupling between triggers and target patterns, requiring synchronous activation at fixed positions across variables. However, realistic scenarios often demand delayed and variable-specific activation. We identify this critical unmet need and propose TDBA, a temporally decoupled backdoor attack framework for MTS forecasting. By injecting triggers that encode the expected location of the target pattern, TDBA enables the activation of the target pattern at any positions within the forecasted data, with the activation position flexibly varying across different variable dimensions. TDBA introduces two core modules: (1) a position-guided trigger generation mechanism that leverages smoothed Gaussian priors to generate triggers that are position-related to the predefined target pattern;  and (2) a position-aware optimization module that assigns soft weights based on trigger completeness, pattern coverage, and temporal offset, facilitating targeted and stealthy attack optimization. Extensive experiments on real-world datasets show that TDBA consistently outperforms existing baselines in effectiveness while maintaining good stealthiness. Ablation studies confirm the controllability and robustness of its design.