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Xun Liang

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18 papers
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18

AAAI Conference 2026 Conference Paper

Enhancing Spatial Reasoning Through Visual and Textual Thinking

  • Xun Liang
  • Xin Guo
  • Zhongming Jin
  • Weihang Pan
  • Penghui Shang
  • Deng Cai
  • Binbin Lin
  • Jieping Ye

The spatial reasoning task aims to reason about the spatial relationships in 2D and 3D space, which is a fundamental capability for Visual Question Answering (VQA) and robotics. Although vision language models (VLMs) have developed rapidly in recent years, they are still struggling with the spatial reasoning task. In this paper, we introduce a method that can enhance Spatial reasoning through Visual and Textual thinking Simultaneously (SpatialVTS). In the spatial visual thinking phase, our model is trained to generate location-related specific tokens of important targets automatically. Not only are the objects mentioned in the problem addressed, but also the potential objects related to the reasoning are considered. During the spatial textual thinking phase, our model conducts long-term thinking based on visual cues and dialogues and gradually inferences the answers to spatial reasoning problems. To effectively support the model's training, we made manual corrections to the existing spatial reasoning dataset, eliminating numerous incorrect labels resulting from automatic annotation, restructuring the data input format to enhance generalization, and developing a reasoning framework for model thinking. Without introducing any additional information (such as masks or depth), our model's overall average level in several spatial understanding tasks has significantly improved compared with other models.

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AAAI Conference 2026 Conference Paper

FAMDR: Feature-Aligned Multimodal Denoising for Reliable Diagnostic Reconciliation in Medical Imaging

  • Xun Liang
  • Zhiying Li
  • Hongxun Jiang

This paper presents FAMDR, a Feature-Aligned Multimodal Denoising framework for Reliable Diagnostic Reconciliation. Existing approaches suffer from two major limitations: (1) an overemphasis on simplifying observational descriptions and (2) a failure to denoise the misleading content in radiological findings against clinical histories. Current methods often dismiss such cross-modal inconsistencies as noise rather than clinically significant signals. To bridge this gap, the framework integrates four synergistic components: (1) noise-aware multimodal alignment that preserves discriminative discrepancy features while ensuring semantic coherence, (2) cross-modal retrieval augmentation leveraging external medical knowledge to resolve ambiguous cases, (3) granular localization of noises at pixel and phrase levels using adaptive thresholding, and (4) medical noise uncertainty quantification to provide reliable confidence estimates. Evaluated on an extended MIMIC-CXR dataset enriched with expert-annotated noise and longitudinal records, FAMDR achieves superior accuracy in semantic denoising and inconsistency localization while preserving clinical interpretability. Its capability to generate actionable, uncertainty-aware reports advances safer and more reliable integration into diagnostic workflows.

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AAAI Conference 2026 Conference Paper

FGD-Align: Pluralistic Alignment for Large Language Models via Fuzzy Group Decision-Making

  • Weihang Pan
  • Zhengxu Yu
  • Yong Wu
  • Xun Liang
  • Zhongming Jin
  • Qiang Fu
  • Penghui Shang
  • Binbin Lin

Ensuring alignment with human values is essential for modern large language models (LLMs), especially amid growing concerns around AI safety and social impact. Yet achieving such alignment remains challenging due to the limited, noisy, and often conflicting nature of human feedback from diverse annotators. Most existing approaches, such as Direct Preference Optimization (DPO), assume consistent and conflict-free supervision, overlooking the ambiguity, inconsistency, and value trade-offs inherent in real-world preferences—often leading to reduced robustness and exclusion of minority views. To address this, we propose FGD-Align, a novel pluralistic alignment framework grounded in Fuzzy Group Decision-Making theory. Our approach rigorously models and aggregates human preferences while retaining the complexity of real-world value trade-offs. Unlike traditional methods that rely on coarse-grained preference pairs, FGD-Align introduces fuzzy preference modeling via triangular fuzzy numbers to capture nuanced, multi-criteria human judgments. We further develop a new training objective, Probabilistic Fuzzy DPO, which incorporates fuzzy preference strength as adaptive loss weights and gradient filters, enhancing robustness to ambiguity and inconsistency in feedback. Comprehensive experiments demonstrate that FGD-Align consistently outperforms both DPO variants and advanced preference aggregation methods in terms of preference accuracy and robustness to ambiguity. It achieves superior alignment stability and better preserves minority preferences, all with minimal computational overhead. Our work bridges the gap between algorithmic tractability and the nuanced landscape of human values, enabling more scalable, inclusive, and socially-aware AI alignment.

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AAAI Conference 2026 Conference Paper

From Semantics to Spectrum: A New Lens on Graph Augmentation Strategy

  • Xiangping Zheng
  • Xiuxin Hao
  • Bo Wu
  • Wei Li
  • Bin Ren
  • Bin Tang
  • Yuhui Guo
  • Xun Liang

Graph augmentation is a cornerstone of effective graph contrastive learning, yet existing methods often rely on random designed perturbations, which may distort latent semantics and impair representation quality. In this work, we argue that semantic consistency can be effectively approximated by low-frequency components in the spectral domain, offering a principled proxy for guiding augmentation. Based on this insight, we propose Frequency-Aware Graph Contrastive Learning (FA-GCL), a novel framework that explicitly preserves low-frequency signals while selectively perturbing high-frequency components. By aligning augmentation with frequency-aware decomposition, FA-GCL generates diverse yet semantically coherent views, mitigating semantic drift and enhancing representational discrimination. Extensive experiments across multiple benchmarks demonstrate that FA-GCL consistently outperforms state-of-the-art baselines with statistically significant gains, validating its exclusive merits.

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AAAI Conference 2026 Conference Paper

Safe RAG by RAG: Untying the Bell That RAG Rang with the RAG Hand

  • Xun Liang
  • Mengwei Wang
  • Yuefeng Ma
  • Simin Niu

Retrieval-augmented generation (RAG) is widely adopted for knowledge-intensive tasks, but unverified external knowledge can pose risks such as data injection and retrieval pollution, leading to unexpected generation. Existing defenses rely on patch-based fixes, which limit generalization and increase system latency. To address these issues, we propose RAG2RAG, a framework-level security solution specifically designed for RAG. Inspired by human intuition to reason about what can and cannot be said during RAG phase, RAG2RAG augments the main RAG module with a lightweight RAG-based security expert module composed of two components: (1) a Detective that dynamically retrieves supporting evidence, and (2) a Judge that makes final decisions based on retrieved context. The main and expert modules operate in parallel without causing noticeable delays. Experiments across two languages, six domains, and seven types of poisoning attacks demonstrate that RAG2RAG overall achieves higher accuracy and lower attack success rates than seven mainstream baselines. Furthermore, it integrates seamlessly with various RAG architectures, offering efficient protection across diverse threat scenarios.

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AAAI Conference 2026 Conference Paper

SEAP: Sparse Expert Activation Pruning Unlocks the Brainpower of Large Language Models

  • Xun Liang
  • Hanyu Wang
  • Huayi Lai
  • Simin Niu
  • Shichao Song
  • Jiawei Yang
  • Jihao Zhao
  • Feiyu Xiong

Pruning is a promising approach to reduce the high inference cost of large language models (LLMs), but it often comes at the expense of performance. Motivated by the "functional localization" theory in neuroscience, we hypothesize that LLMs contain task-specific expert activation paths, where specific subsets of neurons are co-activated for particular tasks. This structure allows selective activation to preserve task performance while improving inference efficiency. We introduce Sparse Expert Activation Pruning (SEAP), a training-free pruning method for large language models. SEAP identifies task-relevant activation paths by analyzing the clustering patterns of hidden states and neuron activations on a multi-task calibration dataset. Cross-task transfer evaluations confirm the existence of such expert activation structures. SEAP constructs task-aware pruning masks by leveraging a task-expert calibration dataset, which provides representative samples across diverse tasks to reveal their activation signatures. It then employs a lightweight task router to dynamically select relevant computation paths based on the input task. This design significantly reduces inference cost without compromising accuracy. Experimental results show that SEAP retains model performance with only a 1.5% drop on most tasks at 20% sparsity, and at 50% sparsity, it surpasses strong pruning baselines such as WandA and FLAP by over 20%. These results highlight SEAP as a scalable and effective solution for efficient LLM inference.

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AAAI Conference 2025 Conference Paper

Enhancing Healthcare Recommendations: A Privacy-Protective and Interpretable Cross-Domain Framework

  • Xun Liang
  • Zhiying Li
  • Hongxun Jiang

Cross-domain recommendations in healthcare services differ from traditional ones in electronic commerce due to the need for heightened medical privacy protection for a small group of users, while ensuring the majority, who may lack sufficient medical knowledge, can understand the recommendations. To recommend doctors who provide online consultations to health video viewers and enable multimodal cross-domain recommendations from short video platforms (source domain) to online healthcare communities (target domain), this paper introduces a framework based on the User-Centric Synthetic Data Architect (UCSDA) and Pre-trained Large Language Model (PtLLM). UCSDA employs a user-centric, advanced selection-synthesis mechanism to filter users' cold interaction items and synthesize noise items, reducing privacy leakage risk. PtLLM focuses on necessary patient and doctor IDs during the recommendation decision process to generate explanations. The model's effectiveness and scalability were validated using three public datasets and a healthcare cross-domain recommendation dataset. In addition to traditional evaluation metrics, strong privacy metrics and the unique sentence ratio were used to assess privacy protection and interpretability. We also compared the characteristics of privacy protection and interpretability between e-commerce and healthcare recommendation scenarios.

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AAAI Conference 2025 Conference Paper

Integrating Large Language Models and Möbius Group Transformations for Temporal Knowledge Graph Embedding on the Riemann Sphere

  • Sensen Zhang
  • Xun Liang
  • Simin Niu
  • Zhendong Niu
  • Bo Wu
  • Gengxin Hua
  • Long Wang
  • Zhenyu Guan

The significance of Temporal Knowledge Graphs (TKGs) in Artificial Intelligence (AI) lies in their capacity to incorporate time-dimensional information, support complex reasoning and prediction, optimize decision-making processes, enhance the accuracy of recommendation systems, promote multimodal data integration, and strengthen knowledge management and updates. This provides a robust foundation for various AI applications. To effectively learn and apply both static and dynamic temporal patterns for reasoning, a range of embedding methods and large language models (LLMs) have been proposed in the literature. However, these methods often rely on a single underlying embedding space, whose geometric properties severely limit their ability to model intricate temporal patterns, such as hierarchical and ring structures. To address this limitation, this paper proposes embedding TKGs into projective geometric space and leverages LLMs technology to extract crucial temporal node information, thereby constructing the 5EL model. By embedding TKGs into projective geometric space and utilizing Möbius Group transformations, we effectively model various temporal patterns. Subsequently, LLMs technology is employed to process the trained TKGs. We adopt a parameter-efficient fine-tuning strategy to align LLMs with specific task requirements, thereby enhancing the model's ability to recognize structural information of key nodes in historical chains and enriching the representation of central entities. Experimental results on five advanced TKG datasets demonstrate that our proposed 5EL model significantly outperforms existing models.

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AAAI Conference 2024 Short Paper

Biomedical Knowledge Graph Embedding with Householder Projection (Student Abstract)

  • Sensen Zhang
  • Xun Liang
  • Simin Niu
  • Xuan Zhang
  • Chen Feng
  • Yuefeng Ma

Researchers have applied knowledge graph embedding (KGE) techniques with advanced neural network techniques, such as capsule networks, for predicting drug-drug interactions (DDIs) and achieved remarkable results. However, most ignore molecular structure and position features between drug pairs. They cannot model the biomedical field's significant relational mapping properties (RMPs,1-N, N-1, N-N) relation. To solve these problems, we innovatively propose CDHse that consists of two crucial modules: 1) Entity embedding module, we obtain position feature obtained by PubMedBERT and Convolutional Neural Network (CNN), obtain molecular structure feature with Graphic Nuaral Network (GNN), obtain entity embedding feature of drug pairs, and then incorporate these features into one synthetic feature. 2) Knowledge graph embedding module, the synthetic feature is Householder projections and then embedded in the complex vector space for training. In this paper, we have selected several advanced models for the DDIs task and performed experiments on three standard BioKG to validate the effectiveness of CDHse.

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AAAI Conference 2024 Short Paper

Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)

  • Hui Tang
  • Xun Liang
  • Sensen Zhang

Detecting anomalies on attributed graphs is a challenging task since labelled anomalies are highly labour-intensive by taking specialized domain knowledge to make anomalous samples not as available as normal ones. Moreover, graphs contain complex structure information as well as attribute information, leading to anomalies that can be typically hidden in the structure space, attribute space, and the mix of both. In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality pseudo labels by considering the structure and attribute inconsistencies between normal and abnormal samples. Meanwhile, ProGAD introduces the prior knowledge of class distribution to correct and refine pseudo labels with a prototype-aware strategy. Experiments demonstrate that ProGAD achieves strong performance compared with the current state-of-the-art methods.

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AAAI Conference 2024 Short Paper

When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)

  • Simin Niu
  • Xun Liang
  • Sensen Zhang
  • Shichao Song
  • Xuan Zhang
  • Xiaoping Zhou

Cross-domain Graph Meta-learning (CGML) has shown its promise, where meta-knowledge is extracted from few-shot graph data in multiple relevant but distinct domains. However, several recent efforts assume target data available, which commonly does not established in practice. In this paper, we devise a novel Cross-domain Data Augmentation for Graph Meta-Learning (CDA-GML), which incorporates the superiorities of CGML and Data Augmentation， has addressed intractable shortcomings of label sparsity, domain shift, and the absence of target data simultaneously. Specifically, our method simulates instance-level and task-level domain shift to alleviate the cross-domain generalization issue in conventional graph meta-learning. Experiments show that our method outperforms the existing state-of-the-art methods.

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AAAI Conference 2023 Short Paper

Enhancing Dynamic GCN for Node Attribute Forecasting with Meta Spatial-Temporal Learning (Student Abstract)

  • Bo Wu
  • Xun Liang
  • Xiangping Zheng
  • Jun Wang

Node attribute forecasting has recently attracted considerable attention. Recent attempts have thus far utilize dynamic graph convolutional network (GCN) to predict future node attributes. However, few prior works have notice that the complex spatial and temporal interaction between nodes, which will hamper the performance of dynamic GCN. In this paper, we propose a new dynamic GCN model named meta-DGCN, leveraging meta spatial-temporal tasks to enhance the ability of dynamic GCN for better capturing node attributes in the future. Experiments show that meta-DGCN effectively modeling comprehensive spatio-temporal correlations between nodes and outperforms state-of-the-art baselines on various real-world datasets.

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AAAI Conference 2023 Short Paper

Exploiting High-Order Interaction Relations to Explore User Intent (Student Abstract)

  • Xiangping Zheng
  • Xun Liang
  • Bo Wu

This paper studies the problem of exploring the user intent for session-based recommendations. Its challenges come from the uncertainty of user behavior and limited information. However, current endeavors cannot fully explore the mutual interactions among sessions and do not explicitly model the complex high-order relations among items. To circumvent these critical issues, we innovatively propose a HyperGraph Convolutional Contrastive framework (termed HGCC) that consists of two crucial tasks: 1) The session-based recommendation (SBR task) that aims to capture the beyond pair-wise relationships between items and sessions. 2) The self-supervised learning (SSL task) acted as the auxiliary task to boost the former task. By jointly optimizing the two tasks, the performance of the recommendation task achieves decent gains. Experiments on multiple real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art methods.

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AAAI Conference 2022 Short Paper

Capsule Graph Neural Network for Multi-Label Image Recognition (Student Abstract)

  • Xiangping Zheng
  • Xun Liang
  • Bo Wu

This paper studies the problem of learning complex relationships between multi-labels for image recognition. Its challenges come from the rich and diverse semantic information in images. However, current methods cannot fully explore the mutual interactions among labels and do not explicitly model the label co-occurrence. To overcome these shortcomings, we innovatively propose CGML that consists of two crucial modules: 1) an image representation learning module that aims to complete the feature extraction of an image whose features are expressed in the form of primary capsules; 2) a label adaptive graph convolutional network module that leverages the popular graph convolutional networks with an adaptive label correlation graph to model label dependencies. Experiments show that our approach obviously outperforms the existing state-of-the-art methods.

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AAAI Conference 2021 Short Paper

RGB-D Scene Recognition based on Object-Scene Relation (Student Abstract)

  • Yuhui Guo
  • Xun Liang

We develop a RGB-D scene recognition model based on object-scene relation(RSBR). First learning a Semantic Network in the semantic domain that classifies the label of a scene on the basis of the labels of all object types. Then, we design an Appearance Network in the appearance domain that recognizes the scene according to local captions. We enforce the Semantic Network to guide the Appearance Network in the learning procedure. Based on the proposed RSBR model, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.

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AAAI Conference 2018 Short Paper

Joint Learning of Evolving Links for Dynamic Network Embedding

  • Aakas Zhiyuli
  • Xun Liang
  • YanFang Chen
  • Peng Shu
  • Xiaoping Zhou

This paper studies the problem of learning node embeddings (a.k.a. distributed representations) for dynamic networks. The embedding methods allocate each node in network with a d-dimensions vector, which can generalize across various tasks, such as item recommendation, node labeling, and link prediction. In practice, many real-world networks are evolving with nodes/links added or deleted. However, most of the proposed methods are focusing on static networks. Although some previous researches have shown some promising results to handle the dynamic scenario, they just considered the added links and ignored the deleted ones. In this work, we designed a joint learning of added and deleted links model, named RDEM, for dynamic network embedding.

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AAAI Conference 2016 Conference Paper

Learning Structural Features of Nodes in Large-Scale Networks for Link Prediction

  • Aakas Zhiyuli
  • Xun Liang
  • Xiaoping Zhou

We present an algorithm (LsNet2Vec) that, given a large-scale network (millions of nodes), embeds the structural features of node into a lower and fixed dimensions of vector in the set of real numbers. We experiment and evaluate our proposed approach with twelve datasets collected from SNAP1. Results show that our model performs comparably with state-of-theart methods, such as Katz method and Random Walk Restart method, in various experiment settings.

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v2026.09.13