近日，国际人工智能领域顶级学术会议AAAI 2026（Association for the Advancement of Artificial Intelligence）公布了论文录用结果，澳门科技大学创新工程学院-计算机科学与工程学院7篇学术论文成功获得接收。AAAI 2026主会议共收到23,680份有效投稿，最终录用4,167篇论文，仅17.6%整体录用率。
AAAI会议由国际人工智能促进协会主办，自1979年创立以来，在全球人工智能研究领域具有极高声誉，也是中国计算机学会（CCF）推荐的A类国际顶级学术会议。澳科大此次入选的7篇论文，聚焦于三维动态场景重建、扩散模型的高效采样、三维点云理解、基于知识蒸馏的三维物体检测、图像内容理解与描述、AI模型脆弱性研究以及用于推荐系统的图神经网络等人工智能前沿关键领域，不仅具有重要的理论创新价值，同时展现出广阔的应用前景。
近年来，澳科大系统构建了人工智能学士课程及博士课程的较完整人才培养体系，并配套设立了人工智能及机器学习实验室等多个专业科研平台，汇聚了包括讲座教授、特聘教授、教授等在内的一支高水平教学科研团队。学科建设的持续深耕已获国际多个主流排名认可：在最新发布的U.S. News 2025/2026全球大学学科排名中，澳科大人工智能学科排名全球第88位；在上海软科发布的2025世界一流学科排名中，澳科大人工智能学科位列全球151-200区间。
澳科大创新工程学院-计算机科学与工程学院院长蔡占川表示，此次多篇高水平论文成功入选AI顶级会议，是学院长期坚持「科研与教学深度融合、理论与实践并重」培养理念的生动体现与重要成果。未来，学校将继续深化在人工智能领域的学科布局，拓展高水平国际合作，依托现有的先进科研平台，为学生提供更前沿、更系统的学术训练与科研实践机会。
AAAI 2026接收的7篇论文简介如下：
论文题目：MCGS: Markov Chain Gaussian Splatting for Dynamic Scenes Reconstruction
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
作者信息：王煜中 博士研究生（第一作者），王文敏 教授（导师，通讯作者）
论文摘要：We present MCGS (Markov Chain Gaussian Splatting), a novel approach for high-fidelity dynamic scene reconstruction via combining Markov chain and 3D Gaussian splatting. Our method addresses the critical challenge of artifact-free temporal consistency in dynamic neural rendering. By integrating a Markov chain-based deformation network with multi-head temporal attention, MCGS effectively captures motion patterns and temporal dependencies, producing more accurate and stable 3D representations over time. The key innovations include: (1) a Markov Deform Network that models state transitions while preserving temporal coherence, (2) a temporal attention mechanism that adaptively weights historical states within a sliding window, and (3) strategic noise injection during training to enhance model robustness and generalization. Experiments on representative dynamic scene datasets demonstrate that MCGS outperforms previous methods in both visual quality and temporal coherence, while maintaining competitive rendering speed and efficiency. These results suggest the practical applicability of our approach to real-world dynamic scene understanding and synthesis.
论文题目：Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
作者信息： 朱爱华 博士研究生（第一作者），赵庆林 教授（导师，通信作者）
论文摘要：Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity,
practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the HierarchicalSchedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upperlevel global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the SpacingPenalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.
论文题目：Distilling Future Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
作者信息：郑皓文 博士研究生（第一作者），梁延研 副教授（导师，通讯作者）
论文摘要：Camera-based temporal 3D object detection has shown impressive results in autonomous driving, with offline models improving accuracy by using future frames. Knowledge distillation (KD) can be an appealing framework for transferring rich information from offline models to online models. However, existing KD methods overlook future frames, as they mainly focus on spatial feature distillation under strict frame alignment or on temporal relational distillation, thereby making it challenging for online models to effectively learn future knowledge. To this end, we propose a sparse query-based approach, Future Temporal Knowledge Distillation (FTKD), which effectively transfers future frame knowledge from an offline teacher model to an online student model. Specifically we present a future-aware feature reconstruction strategy to encourage the student model to capture future features without strict frame alignment. In addition, we further introduce future-guided logit distillation to leverage the teacher’s stable foreground and background context. FTKD is applied to two high-performing 3D object detection baselines, achieving up to 1.3 mAP and 1.3 NDS gains on the nuScenes dataset, as well as the most accurate velocity estimation, without increasing inference cost.
论文题目：PointMC: Multi-view Consistent Encoding and Center-Global Feature Fusion for Point Clouds Understanding
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
第一作者：俞新星 博士研究生（第一作者），刘阿建 澳门青年学者计划博士后（通讯作者），梁延研 副教授（导师， 通讯作者）
论文摘要：Point cloud tasks have recently benefited from Mamba-based architecture, which leverage state space modeling to achieve strong performance. Previous studies have primarily focused on network design while overlooking the importance of position encoding and relying on coarse-grained geometric feature aggregation. The former leads to semantic ambiguity due to inconsistent spatial relationships, while the latter results in geometric feature dispersion by overlooking finegrained local geometric details. To tackle the above problem, we propose a novel framework, PointMC, including Multiview Consistent Learnable Position Encoding (MCLPE) and Center-Global Feature Fusion (CGFF), to provide semantically coherent positional guidance for inter-patch and enable fine-grained geometric structure aggregation within intrapatch regions. Specifically, the proposed MCLPE module is inspired by a spatial structure modeling mechanism guided by physical constraints, leverages multi-view virtual reconstruction and a learnable strategy to dynamically constrain spatial relationships along patch boundaries, thereby enhancing the semantic consistency and representational clarity across inter-patch regions. Furthermore, considering the lack of local structural information within each patch, the CGFF module employs a dual-guidance mechanism based on center and global structures to effectively promote the aggregation of local geometric features. Extensive experiments on multiple benchmark datasets validate the effectiveness of PointMC, consistently outperforming existing state-of-the-art methods, and demonstrating superior capability in capturing both interpatch semantic consistency and intra-patch geometric details.
论文题目：OAD-Promoter: Enhancing Zero-shot VQA using Large Language Models with Object Attribute Description
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
作者信息：许全星 博士生（第一作者），周玲 助理教授（导师，通信作者），黄如兵 副教授（导师）
论文摘要：Large Language Models (LLMs) have become a crucial tool in Visual Question Answering (VQA) for handling knowledge-intensive questions in few-shot or zero-shot scenarios. However, their reliance on massive training datasets often causes them to inherit language biases during the acquisition of knowledge. This limitation imposes two key constraints on existing methods: (1) LLM predictions become less reliable due to bias exploitation, and (2) despite strong knowledge reasoning capabilities, LLMs still struggle with out-of-distribution (OOD) generalization. To address these issues, we propose Object Attribute Description Promoter (OAD-Promoter), a novel approach for enhancing LLM-based VQA by mitigating language bias and improving domain-shift robustness. OAD-Promoter comprises three components: the Object-concentrated Example Generation (OEG) module, the Memory Knowledge Assistance (MKA) module, and the OAD Prompt. The OEG module generates global captions and object-concentrated samples, jointly enhancing visual information input to the LLM and mitigating bias through complementary global and regional visual cues. The MKA module assists the LLM in handling OOD samples by retrieving relevant knowledge from stored examples to support questions from unseen domains. Finally, the OAD Prompt integrates the outputs of the preceding modules to optimize LLM inference. Experiments demonstrate that OAD-Promoter significantly improves the performance of LLM-based VQA methods in few-shot or zero-shot settings, achieving new state-of-the-art results.
论文题目：Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
作者信息：何宇浩 博士生（第一作者），田晋宇 助理教授（导师，通信作者）
论文摘要：Recent studies have shown that deep learning models are very vulnerable to poisoning attacks. Many defense methods have been proposed to address this issue. However, traditional poisoning attacks are not as threatening as commonly believed. This is because they often cause differences in how the model performs on the training set compared to the validation set. Such inconsistency can alert defenders that their data has been poisoned, allowing them to take the necessary defensive actions. In this paper, we introduce a more threatening type of poisoning attack called the Deferred Poisoning Attack. This new attack allows the model to function normally during the training and validation phases but makes it very sensitive to evasion attacks or even natural noise. We achieve this by ensuring the poisoned model’s loss function has a similar value as a normally trained model at each input sample but with a large local curvature. A similar model loss ensures that there is no obvious inconsistency between the training and validation accuracy, demonstrating high stealthiness. On the other hand, the large curvature implies that a small perturbation may cause a significant increase in model loss, leading to substantial performance degradation, which reflects a worse robustness. We fulfill this purpose by making the model have singular Hessian information at the optimal point via our proposed Singularization Regularization term. We have conducted both theoretical and empirical analyses of the proposed method and validated its effectiveness through experiments on image classification tasks. Furthermore, we have confirmed the hazards of this form of poisoning attack under more general scenarios using natural noise, offering a new perspective for research in the field of security.
论文题目：Revisiting Contrastive Learning in Collaborative Filtering via Parallel Graph Filters
第一单位：Faculty of Innovation Engineering, Macau University of Science and Technology
作者信息：开放 硕士研究生（第一作者）
论文摘要：Graph Contrastive Learning (GCL) has recently emerged as a powerful paradigm for modeling user–item interactions and for learning high-quality representations in recommender systems. While existing GCL-based methods benefit from data augmentation and sampling strategies, they often overlook the inherent limitations of the contrastive objectives: 1) Stacking multiple Graph Convolutional Network layers to capture high-order information often causes the oversmoothing phenomenon, where node representations become overly similar. 2) Structurally similar negative sample pairs may exhibit high cosine similarity, causing gradient saturation during representation optimization. To address the above challenges, we revisit matrix factorization in recommendation models and uncover its implicit connection to a parallel graph filter bank. This perspective reveals how overly aggressive low-pass or high-pass filtering distorts feature distributions, contributing to gradient saturation. Building on this insight, we propose Light Cosine Similarity Collaborative Filtering (LightCSCF), a margin-constrained method that improves gradient optimization in contrastive learning by focusing on structurally hard examples, alleviating both gradient saturation and boundary over-smoothing. Extensive experiments on three real-world datasets demonstrate that LightCSCF consistently outperforms state-of-the-art baselines in recommendation accuracy and robustness to data sparsity.
