GenAI4ELab/papercli-papers huggingface dataset:
Self-Interpretable Subgraph Neural Network with Deep Reinforcement Walk Exploration
Authors: Jianming Huang; Hiroyuki Kasai
Venue: AAAI 2026 (main)

Abstract: Graph neural networks (GNNs) face dual challenges of limited structural expressiveness and opaque decision-making processes. Recent research on Subgraph Neural Networks (SGNNs) enhance model expressiveness through subgraph ensembles. However, their reliance on predefined sampling strategies leads to poor interpretability and computational inefficiency. Meanwhile, post-hoc GNN explainers enhance model interpretability but still struggle to translate their explanations into model improvements. This paper presents a novel framework that fundamentally bridges this gap by developing SGNNs with intrinsic interpretability. Our key innovation lies in constructing a self-interpretable architecture where the explanation generation mechanism is organically integrated with the prediction process. Our proposed Self-Interpretable SGNN introduces a reinforcement walk exploration (RWE-SGNN) as its data-driven sampling strategy, which can dynamically extract discriminative substructures during model training. This reinforcement walk exploration module not only provides inherent interpretability, but also enables: (1) efficient substructure extraction with less candidate number and simper embedding than traditional subgraph generation methods; and (2) provable equivalence in node coverage to traditional subgraph generation methods for connected subgraphs. Experiments on graph classification tasks show accuracy improvements over state-of-the-art GNNs, with case studies validating that the automatically identified subgraphs align with domain-specific knowledge.
Function-on-Function Bayesian Optimization
Authors: Jingru Huang; Haijie Xu; Manrui Jiang; Chen Zhang
Venue: AAAI 2026 (main)

Abstract: Bayesian optimization (BO) has been widely used to optimize expensive and gradient-free objective functions across various domains. However, existing BO methods have not addressed the objective where both inputs and outputs are functions, which increasingly arise in complex systems as advanced sensing technologies. To fill this gap, we propose a novel function-on-function Bayesian optimization (FFBO) framework. Specifically, we first introduce a function-on-function Gaussian process (FFGP) model with a separable operator-valued kernel to capture the correlations between function-valued inputs and outputs. Compared to existing Gaussian process models, FFGP is modeled directly in the function space. Based on FFGP, we define a scalar upper confidence bound (UCB) acquisition function using a weighted operator-based scalarization strategy. Then, a scalable functional gradient ascent algorithm (FGA) is developed to efficiently identify the optimal function-valued input. We further analyze the theoretical properties of the proposed method. Extensive experiments on synthetic and real-world data demonstrate the superior performance of FFBO over existing approaches.
Trimming the Fat: Redundancy-Aware Acceleration Framework for DGNNs
Authors: Renhong Huang; Yuxuan Cao; Yi Li; Junwei Hu; Zihua Xiong; Shuai Fang; Sheng Guo; Bo Zheng; Yang Yang
Venue: AAAI 2026 (main)

Abstract: Temporal graphs are essential for modeling complex real-world systems, such as social interactions, financial transactions, and recommendation systems, but the high computational cost and model complexity of dynamic graph neural networks (DGNNs) pose significant challenges for practical deployment. Although various pruning and sampling techniques have proven effective in accelerating static GNNs, they fall short in dynamic settings due to temporal dependencies in evolving graph structures. To address these challenges, we propose TrimDG, a general framework that accelerates DGNNs by eliminating both static and runtime redundancies. For static redundancy, we introduce a novel node influence metric, Temporal Personalized PageRank (TPP), to prune less informative nodes, and employ temporal binning to remove redundant events. For runtime redundancy during training, we develop an adaptive sampling strategy guided by graph information bottleneck and further reduce sampling frequency through temporal batch selector and sampling cache. Theoretical analysis supports our design, and experiments on real-world datasets show that TrimDG reduces runtime by an average of 83.49% across diverse DGNN backbones, while maintaining strong predictive performance, demonstrating both its efficiency and generalizability.
HMformer: Unleashing Transformer’s Potential for Time Series Forecasting via Hierarchical Multi-Scale Modeling
Authors: Renjun Huang; Han Xiao; Bingqing Li; Baili Zhang; Jianhua Lyu
Venue: AAAI 2026 (main)

Abstract: Time series forecasting plays a critical role across a wide range of domains. Recently, an increasing number of Transformer-based forecasting models have emerged, achieving remarkably competitive performance. However, real-world time series data often exhibit complex multi-scale periodicities, which are not well-suited for modeling by the original Transformer architecture originally developed for NLP tasks. To address this limitation, we propose the Hierarchical Multi-scale Time Series Transformer (HMformer),  employing a novel and sophisticated framework specifically designed for multi-scale time series forecasting. Specifically, HMformer incorporates a hierarchical cross-scale mixing mechanism that progressively aggregates temporal information from fine to coarse granularities, a scale-adaptive feature expansion design enhancing the extraction of high-level temporal semantics, and a multi-branch complementary prediction strategy for effectively integrating diverse temporal patterns. Collectively, these components enable HMformer to capture intricate, multi-scale temporal dynamics while retaining the Transformer’s inherent strength in modeling long-range dependencies. Extensive experiments conducted on multiple real-world benchmark datasets—encompassing both long-term and short-term forecasting tasks—demonstrate that HMformer achieves state-of-the-art performance.
Sparse Poisson Gamma Belief Networks for High-Dimensional Sparse Count Data
Authors: Rui Huang; Dian Meng; Xun Zhou; Sikun Yang
Venue: AAAI 2026 (main)

Abstract: Bayesian networks play a crucial role in various domains for unsupervised feature extraction and data interpretation. The Poisson gamma belief networks (PGBNs), as a type of Bayesian networks, have shown promise in analyzing high-dimensional count data. However, PGBNs encounter significant challenges when applied to sparse data, particularly in achieving accurate feature extraction and avoiding overfitting during missing value prediction. In this paper, we propose the sparse Poisson gamma belief networks (SPGBNs), a Bayesian network model designed to address these limitations. By incorporating sparse graph-structured priors over the weight matrices between adjacent layers, the proposed SPGBNs effectively capture the inherent sparsity and graph structures of latent features. Meanwhile, SPGBNs demonstrate superior generalization on missing data prediction and enable more stable extraction of meaningful latent features compared to existing approaches. Additionally, we develop an efficient Gibbs sampling algorithm that significantly improves the training stability and computational efficiency of SPGBNs. Extensive experiments on real-world datasets are conducted to validate the effectiveness of our approach.
Feature Attribution for Human Sensing with Radio Signals
Authors: Shuokang Huang; Julie McCann
Venue: AAAI 2026 (main)

Abstract: Human sensing with radio signals has emerged as a non-intrusive and occlusion-robust alternative to vision-based approaches, and WiFi signals further support device-free sensing. However, current approaches deeply rely on neural networks, whose black-box nature hinders model transparency and explainability, limiting the use of WiFi-based human sensing in critical fields. For model explainability, recent works have studied saliency methods which attribute model outputs to important features, but they mostly bias in favor of common modalities (e.g., images, time series). This paper proposes a Matryoshka-like saliency method, MatryMask, an initial exploration of feature attribution for human sensing with radio signals. Compared to existing methods that require empirical knowledge about the sparsity of important features, MatryMask regularizes multiple masks to highlight salient areas at different scales, adapting to the uncertain and varying sparsity of important features in radio signals. To effectively perturb radio signals, we devise a novel frequency-removal perturbation beyond existing spatial/time-domain perturbations. Experimentally, MatryMask outperforms state-of-the-art saliency methods and significantly improves the attribution performance by up to 38.1~70.6% for three tasks.
CoLM: Collaborative Large Models via a Client-Server Paradigm
Authors: Siqi Huang; Sida Huang; Hongyuan Zhang
Venue: AAAI 2026 (main)

Abstract: Large models have achieved remarkable performance across a range of reasoning and understanding tasks. Prior work often utilizes model ensembles or multi-agent systems to collaboratively generate responses, effectively operating in a server-to-server paradigm.   However, such approaches do not align well with practical deployment settings, where a limited number of server-side models are shared by many clients under modern internet architectures. In this paper, we introduce CoLM (Collaboration in Large-Models), a novel framework for collaborative reasoning that redefines cooperation among large models from a client-server perspective. Unlike traditional ensemble methods that rely on simultaneous inference from multiple models to produce a single output, CoLM allows the outputs of multiple  models to be aggregated or shared, enabling each client model to independently refine and update its own generation based on these high-quality outputs. This design enables collaborative benefits by fully leveraging both client-side and shared server-side models. We further extend CoLM to vision-language models (VLMs), demonstrating its applicability beyond language tasks. Experimental results across multiple benchmarks show that CoLM consistently improves model performance on previously failed queries, highlighting the effectiveness of collaborative guidance in enhancing single-model capabilities.
FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning
Authors: Xingwei Huang; Zhaobin Sun; Junjie Shi; Xin Yang; Zengqiang Yan
Venue: AAAI 2026 (main)

Abstract: Federated class-incremental learning (FCIL) aims to incrementally learn new classes across decentralized clients under non-IID data distributions. However, the pervasive challenge of label noise in FCIL has been completely overlooked. In this work, we introduce federated noisy class-incremental learning (FNCIL) and, for the first time, identify a novel form of label noise—spatio-temporal label misalignment—where samples from unseen classes are entirely mislabeled as known classes, with their correctly labeled counterparts appearing in latter tasks or other clients. This phenomenon undermines the effectiveness of existing centralized denoising strategies and creates a clear requirement for noise-robust methods in real-world FNCIL scenarios. To tackle this issue, we propose FedRNC, a dual-phase framework that leverages feature-space associations to establish spatio-temporal correspondences between clean global prototypes and noisy cached samples for progressive label correction. Experiments on standard benchmarks demonstrate FedRNC's superiority against existing baselines, along with its plug-and-play capability to upgrade FCIL systems for FNCIL.
PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations
Authors: Xinquan Huang; Paris Perdikaris
Venue: AAAI 2026 (main)

Abstract: Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However, these models suffer from a critical limitation: error accumulation during long-term rollouts, where small inaccuracies compound exponentially, eventually causing complete divergence from physically valid solutions. We present PhysicsCorrect, a training-free correction framework that enforces PDE consistency at each prediction step by formulating correction as a linearized inverse problem based on PDE residuals. Our key innovation is an efficient caching strategy that precomputes the Jacobian and its pseudoinverse during an offline warm-up phase, reducing computational overhead by two orders of magnitude compared to standard correction approaches. Across three representative PDE systems, including Navier-Stokes fluid dynamics, wave equations, and the chaotic Kuramoto-Sivashinsky equation, PhysicsCorrect reduces prediction errors by up to 100× while adding negligible inference time (under 5%). The framework integrates seamlessly with diverse architectures, including Fourier Neural Operators, UNets, and Vision Transformers, effectively transforming unstable neural surrogates into reliable simulation tools that bridge the gap between deep learning's computational efficiency and the physical fidelity demanded by practical scientific applications.
Federated Linear Dueling Bandits
Authors: Xuhan Huang; Yan Hu; Zhiyan Li; Zhiyong Wang; Zhongxiang Dai
Venue: AAAI 2026 (main)

Abstract: Contextual linear dueling bandits have recently garnered significant attention due to their widespread applications in important domains such as recommender systems and large language models. Classical dueling bandit algorithms are typically only applicable to a single agent. However, many applications of dueling bandits involve multiple agents who wish to collaborate for improved performance yet are unwilling to share their data. This motivates us to draw inspirations from federated learning, which involves multiple agents aiming to collaboratively train their neural networks via gradient descent (GD) without sharing their raw data. Previous works have developed federated linear bandit algorithms which rely on closed-form updates of the bandit parameters (e.g., the linear function parameters) to achieve collaboration. However, in linear dueling bandits, the linear function parameters lack a closed-form expression and their estimation requires minimizing a loss function. This renders these previous methods inapplicable. In this work, we overcome this challenge through an innovative and principled combination of online gradient descent (OGD, for minimizing the loss function to estimate the linear function parameters) and federated learning, hence introducing our federated linear dueling bandit with OGD (FLDB-OGD) algorithm. Through rigorous theoretical analysis, we prove that FLDB-OGD  enjoys a sub-linear upper bound on its cumulative regret and demonstrate a theoretical trade-off between regret and communication complexity. We conduct empirical experiments to demonstrate the effectiveness of FLDB-OGD and reveal valuable insights, such as the benefit of a larger number of agents, the regret-communication trade-off, among others.
M2FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting
Authors: Yaohui Huang; Runmin Zou; Yun Wang; Laeeq Aslam; Ruipeng Dong
Venue: AAAI 2026 (main)

Abstract: Forecasting time series with extreme events is critical yet challenging due to their high variance, irregular dynamics, and sparse but high-impact nature. While existing methods excel in modeling dominant regular patterns, their performance degrades significantly during extreme events, constituting the primary source of forecasting errors in real-world applications. Although some approaches incorporate auxiliary signals to improve performance, they still fail to capture extreme events' complex temporal dynamics. To address these limitations, we propose M²FMoE, an extreme-adaptive forecasting model that learns both regular and extreme patterns through multi-resolution and multi-view frequency modeling. It comprises three modules: (1) a multi-view frequency mixture-of-experts module assigns experts to distinct spectral bands in Fourier and Wavelet domains, with cross-view shared band splitter aligning frequency partitions and enabling inter-expert collaboration to capture both dominant and rare fluctuations; (2) a multi-resolution adaptive fusion module that hierarchically aggregates frequency features from coarse to fine resolutions, enhancing sensitivity to both short-term variations and sudden changes; (3) a temporal gating integration module that dynamically balances long-term trends and short-term frequency-aware features, improving adaptability to both regular and extreme temporal patterns. Experiments on real-world hydrological datasets with extreme patterns demonstrate that M²FMoE outperforms state-of-the-art baselines without requiring extreme-event labels.
Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning
Authors: Yi Huang; Qingyun Sun; Yisen Gao; Haonan Yuan; Xingcheng Fu; Jianxin Li
Venue: AAAI 2026 (main)

Abstract: The Information Bottleneck (IB) principle facilitates effective representation learning by preserving label-relevant information while compressing irrelevant information. However, its strong reliance on accurate labels makes it inherently vulnerable to label noise, prevalent in real-world scenarios, resulting in significant performance degradation and overfitting. To address this issue, we propose LaT-IB, a novel Label-Noise ResistanT Information Bottleneck method which introduces a "Minimal-Sufficient-Clean" (MSC) criterion. Instantiated as a mutual information regularizer to retain task-relevant information while discarding noise, MSC addresses standard IB’s vulnerability to noisy label supervision. To achieve this, LaT-IB employs a noise-aware latent disentanglement that decomposes the latent representation into components aligned with to the clean label space and the noise space. Theoretically, we first derive mutual information bounds for each component of our objective including prediction, compression, and disentanglement, and moreover prove that optimizing it encourages representations invariant to input noise and separates clean and noisy label information.
Furthermore, we design a three-phase training framework: Warmup, Knowledge Injection and Robust Training, to progressively guide the model toward noise-resistant representations. Extensive experiments demonstrate that LaT-IB achieves superior robustness and efficiency under label noise, significantly enhancing robustness and applicability in real-world scenarios with label noise.
FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD
Authors: Zhenyuan Huang; Hui Zhang; Wenzhong Tang; Haijun Yang
Venue: AAAI 2026 (main)

Abstract: Amid growing demands for data privacy and advances in computational infrastructure, federated learning (FL) has emerged as a prominent distributed learning paradigm. Nevertheless, differences in data distribution (such as covariate and semantic shifts) severely affect its reliability in real-world deployments. To address this issue, we propose FedSDWC, a causal inference method that integrates both invariant and variant features. FedSDWC infers causal semantic representations by modeling the weak causal influence between invariant and variant features, effectively overcoming the limitations of existing invariant learning methods in accurately capturing invariant features and directly constructing causal representations. This approach significantly enhances FL's ability to generalize and detect OOD data. Theoretically, we derive FedSDWC's generalization error bound under specific conditions and, for the first time, establish its relationship with client prior distributions. Moreover, extensive experiments conducted on multiple benchmark datasets validate the superior performance of FedSDWC in handling covariate and semantic shifts. For example, FedSDWC outperforms FedICON, the next best baseline, by an average of 3.04% on CIFAR-10 and 8.11% on CIFAR-100.
Wasserstein-Aware Transfer: Class-Level Alignment for Robust Diffusion Model Adaptation
Authors: Zixian Huang; Chuan-Xian Ren
Venue: AAAI 2026 (main)

Abstract: Diffusion models have achieved impressive generative performance across diverse domains such as image, video, and scientific data generation. However, fine-tuning these models for new tasks remains challenging due to their large scale, architectural diversity, and high sensitivity to hyperparameters—particularly learning rates. In this work, we propose Wasserstein-Aware Transfer (WAT), a principled and effective fine-tuning strategy grounded in diffusion trajectory analysis and optimal transport theory. Our key insight is that the distributional discrepancies between diffusion trajectories from different datasets decrease progressively over time and converge near the noise end. Based on this observation, we introduce a class-wise matching mechanism that minimizes the Wasserstein distance between class distributions of source and target datasets. This enables alignment at the class level without modifying the standard fine-tuning pipeline. To further enhance knowledge retention, we propose a novel sampling strategy that linearly combines class-conditional outputs from both pretrained and fine-tuned models. This method is simple yet effective, requiring negligible computational overhead while preserving domain-specific and generalizable knowledge. Extensive experiments across seven diverse benchmarks demonstrate that WAT reliably enhances generation quality under distribution shifts, outperforming competitive baselines. These results underscore its robustness and affirm the potential of optimal transport as a principled basis for knowledge transfer in diffusion models.
Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games
Authors: Bingyu Hui; Lebin Yu; Quanming Yao; Yunpeng Qu; Xudong Zhang; Jian Wang
Venue: AAAI 2026 (main)

Abstract: Zero-shot coordination(ZSC), a key challenge in multi-agent game theory, has become a hot topic in reinforcement learning (RL) research recently, especially in complex evolving games. It focuses on the generalization ability of agents, requiring them to coordinate well with collaborators from a diverse, potentially evolving, pool of partners that are not seen before without any fine-tuning. Population-based training, which approximates such an evolving partner pool, has been proven to provide good zero-shot coordination performance; nevertheless, existing methods are limited by computational resources, mainly focusing on optimizing diversity in small populations while neglecting the potential performance gains from scaling population size. To address this issue, this paper proposes the Scalable Population Training (ScaPT), an efficient RL training framework comprising two key components: a meta-agent that efficiently realizes a population by selectively sharing parameters across agents, and a mutual information regularizer that guarantees population diversity. To empirically validate the effectiveness of ScaPT, this paper evaluates it along with representational frameworks in Hanabi cooperative game and confirms its superiority.