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Junyu Luo

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12 papers

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AAAI Conference 2026 Conference Paper

BAMAS: Structuring Budget-Aware Multi-Agent Systems

Liming Yang
Junyu Luo
Xuanzhe Liu
Yiling Lou
Zhenpeng Chen

Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity, cost becomes an important consideration for practical deployment. However, existing work rarely addresses how to structure multi-agent systems under explicit budget constraints. In this paper, we propose BAMAS, a novel approach for building multi-agent systems with budget awareness. BAMAS first selects an optimal set of LLMs by formulating and solving an Integer Linear Programming problem that balances performance and cost. It then determines how these LLMs should collaborate by leveraging a reinforcement learning-based method to select the interaction topology. Finally, the system is instantiated and executed based on the selected agents and their collaboration topology. We evaluate BAMAS on three representative tasks and compare it with state-of-the-art agent construction methods. Results show that BAMAS achieves comparable performance while reducing cost by up to 86%.

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AAAI Conference 2026 Conference Paper

CogniTrust: Cognitive Memory-Driven Verifiable Supervision for Robust Hashing

Yiyang Gu
Bohan Wu
Yifang Qin
Jiaru Tang
Rong-Cheng Tu
Zhiping Xiao
Taian Guo
Junyu Luo

In this paper, we study the problem of robust multi-label hashing, where label noise hinders the learning of a reliable semantic structure from data. Many existing methods rely on heuristic sample selection or consistency-based training, but lack a unified mechanism to validate and refine supervision across structural and semantic levels. Inspired by cognitive theories of human memory, we propose a novel framework called CogniTrust that unifies verifiable supervision with a triadic memory model: a) In episodic memory, feature activations are decomposed into spatial patterns that support the assessment of structural evidence and the estimation of label reliability; b) Semantic memory keeps track of class-level prototypes from structurally attentive regions to estimate the semantic plausibility of labels; c) Reconstructive memory simulates memory recall through interpolation between images using a diffusion-based mixup process, which enriches the training signals for semantically uncertain regions. These components work together, allowing supervision to be refined through the joint consideration of spatial structure and semantic information. Extensive experiments on noisy hashing benchmarks demonstrate that CogniTrust consistently outperforms a range of state-of-the-art baselines. Our results show that cognitive memory mechanisms offer a principled basis for more reliable label denoising and robust hashing.

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AAAI Conference 2025 Conference Paper

Attention Bootstrapping for Multi-Modal Test-Time Adaptation

Yusheng Zhao
Junyu Luo
Xiao Luo
Jinsheng Huang
Jingyang Yuan
Zhiping Xiao
Ming Zhang

Test-time adaptation aims to adapt a well-trained model to potential distribution shifts at test time using only unlabeled test data, without access to the original training data. While previous efforts mainly focus on a single modality, test-time distribution shift in the multi-modal setting is more complex and calls for new solutions. This paper tackles the problem of multi-modal test-time adaptation by proposing a novel method named Attention Bootstrapping with Principal Entropy Minimization (ABPEM). We observe that test-time distribution shift causes misalignment across modalities, leading to a large gap between intra-modality discrepancies (measured by self-attention) and inter-modality discrepancies (measured by cross-attention). We name this the attention gap. This attention gap widens with more severe distribution shifts, hindering effective modality fusion. To mitigate this attention gap and encourage better modality fusion, we propose attention bootstrapping that promotes cross-attention with the guidance of self-attention. Moreover, to reduce the gradient noise in the commonly-used entropy minimization, we adopt principal entropy minimization, a refinement of entropy minimization that reduces gradient noise by focusing on the principal parts of entropy, excluding less reliable gradient information. Extensive experiments on the benchmarks validate the effectiveness of the proposed ABPEM in comparison with competing baselines.

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TMLR Journal 2025 Journal Article

DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation

Pengyun Wang
Yadi Cao
Chris Russell
Yanxin Shen
Junyu Luo
Ming Zhang
Siyu Heng
Xiao Luo

Graph domain adaptation has recently enabled knowledge transfer across different graphs. However, without the semantic information on target graphs, the performance on target graphs is still far from satisfactory. To address the issue, we study the problem of active graph domain adaptation, which selects a small quantitative of informative nodes on the target graph for extra annotation. This problem is highly challenging due to the complicated topological relationships and the distribution discrepancy across graphs. In this paper, we propose a novel approach named Dual Consistency Delving with Topological Uncertainty (DELTA) for active graph domain adaptation. Our DELTA consists of an edge-oriented graph subnetwork and a path-oriented graph subnetwork, which can explore topological semantics from complementary perspectives. In particular, our edge-oriented graph subnetwork utilizes the message passing mechanism to learn neighborhood information, while our path-oriented graph subnetwork explores high-order relationships from substructures. To jointly learn from two subnetworks, we roughly select informative candidate nodes with the consideration of consistency across two subnetworks. Then, we aggregate local semantics from its K-hop subgraph based on node degrees for topological uncertainty estimation. To overcome potential distribution shifts, we compare target nodes and their corresponding source nodes for discrepancy scores as an additional component for fine selection. Extensive experiments on benchmark datasets demonstrate that DELTA outperforms various state-of-the-art approaches. The code implementation of DELTA is available at https://github.com/goose315/DELTA.

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NeurIPS Conference 2025 Conference Paper

Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation

Xin Ma
Yifan Wang
Siyu Yi
Wei Ju
Junyu Luo
Yusheng Zhao
Xiao Luo
Jiancheng Lv

Graph transfer learning, especially in unsupervised domain adaptation, aims to transfer knowledge from a label-abundant source graph to an unlabeled target graph. However, most existing approaches overlook the common issue of label imbalance in the source domain, typically assuming a balanced label distribution that rarely holds in practice. Moreover, they face challenges arising from biased knowledge in the source graph and substantial domain distribution shifts. To remedy the above challenges, we propose a dual-branch prototype-enhanced contrastive framework for class-imbalanced graph domain adaptation in this paper. Specifically, we introduce a dual-branch graph encoder to capture both local and global information, generating class-specific prototypes from a distilled anchor set. Then, a prototype-enhanced contrastive learning framework is introduced. On the one hand, we encourage class alignment between the two branches based on constructed prototypes to alleviate the bias introduced by class imbalance. On the other hand, we infer the pseudo-labels for the target domain and align sample pairs across domains that share similar semantics to reduce domain discrepancies. Experimental results show that our ImGDA outperforms the state-of-the-art methods across multiple datasets and settings. The code is available at: https: //github. com/maxin88scu/ImGDA.

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AIJ Journal 2025 Journal Article

MATE: Masked optimal transport with dynamic selection for partial label graph learning

Yiyang Gu
Binqi Chen
Zihao Chen
Ziyue Qiao
Xiao Luo
Junyu Luo
Zhiping Xiao
Wei Ju

This paper investigates the problem of partial label graph learning, in which every graph is associated with a set of candidate labels. Previous methods for weakly supervised graph classification often provide pseudo-labels for graph samples that could be overconfident and biased towards the dominant classes, thus resulting in substantial error accumulation. In this paper, we introduce a new framework named Masked Optimal Transport with Dynamic Selection (MATE) for partial label graph learning, which improves the quality of graph assignments from the perspectives of class balancing and uncertainty mining. In particular, our MATE masks probabilities out of candidate sets and then adopts optimal transport to optimize the assignments without class biases. This design is based on the assumption that the true label distribution is class-balanced or nearly balanced, which is common in various training datasets and real-world scenarios. To further reduce potential noise, we propose a novel scoring metric termed partial energy discrepancy (PED) to evaluate the uncertainty of assignments, and then introduce a dynamic selection strategy that modifies the sample-specific thresholds via momentum updating. Finally, these samples are divided into three levels, i. e. , confident, less-confident, and unconfident and each group is trained separately in our collaborative optimization framework. Extensive experiments on various benchmarks demonstrate the superiority of our MATE compared to various state-of-the-art baselines.

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AAAI Conference 2025 Conference Paper

TRACI: A Data-centric Approach for Multi-Domain Generalization on Graphs

Yusheng Zhao
Changhu Wang
Xiao Luo
Junyu Luo
Wei Ju
Zhiping Xiao
Ming Zhang

Graph neural networks (GNNs) have gained superior performance in graph-based prediction tasks with a variety of applications such as social analysis and drug discovery. Despite the remarkable progress, their performance often degrades on test graphs with distribution shifts. Existing domain adaptation methods rely on unlabeled test graphs during optimization, limiting their applicability to graphs in the wild. Towards this end, this paper studies the problem of multi-domain generalization on graphs, which utilizes multiple source graphs to learn a GNN with high performance on unseen target graphs. We propose a new approach named Topological Adversarial Learning with Prototypical Mixup (TRACI) to solve the problem. The fundamental principle behind our TRACI is to produce virtual adversarial and mixed graph samples from a data-centric view. In particular, TRACI enhances GNN generalization by employing a gradient-ascent strategy that considers both label prediction entropy and graph topology to craft challenging adversarial samples. Additionally, it generates domain-agnostic node representations by characterizing class-graph pair prototypes through latent distributions and applying multi-sample prototypical Mixup for distribution alignment across graphs. We further provide theoretical analysis showing that TRACI reduces the model's excess risk. Extensive experiments on various benchmark datasets demonstrate that TRACI outperforms state-of-the-art baselines, validating its effectiveness.

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IJCAI Conference 2024 Conference Paper

A Survey of Data-Efficient Graph Learning

Wei Ju
Siyu Yi
Yifan Wang
Qingqing Long
Junyu Luo
Zhiping Xiao
Ming Zhang

Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural networks demonstrate proficiency in modeling this type of data, their success is often reliant on significant amounts of labeled data, posing a challenge in practical scenarios with limited annotation resources. To tackle this problem, tremendous efforts have been devoted to enhancing graph machine learning performance under low-resource settings by exploring various approaches to minimal supervision. In this paper, we introduce a novel concept of Data-Efficient Graph Learning (DEGL) as a research frontier, and present the first survey that summarizes the current progress of DEGL. We initiate by highlighting the challenges inherent in training models with large labeled data, paving the way for our exploration into DEGL. Next, we systematically review recent advances on this topic from several key aspects, including self-supervised graph learning, semi-supervised graph learning, and few-shot graph learning. Also, we state promising directions for future research, contributing to the evolution of graph machine learning.

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NeurIPS Conference 2024 Conference Paper

EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics

Jingyang Yuan
Gongbo Sun
Zhiping Xiao
Hang Zhou
Xiao Luo
Junyu Luo
Yusheng Zhao
Wei Ju

This paper studies the problem of rigid dynamics modeling, which has a wide range of applications in robotics, graphics, and mechanical design. The problem is partly solved by graph neural network (GNN) simulators. However, these approaches cannot effectively handle the relationship between intrinsic continuity and instantaneous changes in rigid dynamics. Moreover, they usually neglect hierarchical structures across mesh nodes and objects in systems. In this paper, we propose a novel approach named Event-attend Graph ODE (EGODE) for effective rigid dynamics modeling. In particular, we describe the rigid system using both mesh node representations and object representations. To model continuous dynamics across hierarchical structures, we use a coupled graph ODE framework for the evolution of both types of representations over a long period. In addition, to capture instantaneous changes during the collision, we introduce an event module, which can effectively estimate the occurrence of the collision and update the states of both mesh node and object representations during evolution. Extensive experiments on a range of benchmark datasets validate the superiority of the proposed EGODE compared to various state-of-the-art baselines. The source code can be found at https: //github. com/yuanjypku/EGODE.

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IJCAI Conference 2024 Conference Paper

Rank and Align: Towards Effective Source-free Graph Domain Adaptation

Junyu Luo
Zhiping Xiao
Yifan Wang
Xiao Luo
Jingyang Yuan
Wei Ju
Langechuan Liu
Ming Zhang

Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. In particular, to overcome label scarcity, we employ the spectral seriation algorithm to infer the robust pairwise rankings, which can guide semantic learning using a similarity learning objective. To depict distribution shifts, we utilize spectral clustering and the silhouette coefficient to detect harmonic graphs, which the source model can easily classify. To reduce potential domain discrepancy, we extract domain-invariant subgraphs from inharmonic graphs by an adversarial edge sampling process, which guides the invariant learning of GNNs. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA.

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IJCAI Conference 2024 Conference Paper

Recent Advances in Predictive Modeling with Electronic Health Records

Jiaqi Wang
Junyu Luo
Muchao Ye
Xiaochen Wang
Yuan Zhong
Aofei Chang
Guanjie Huang
Ziyi Yin

The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machine learning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research.

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AAAI Conference 2019 Short Paper

Learning Document Embeddings with Crossword Prediction

Junyu Luo
Min Yang
Ying Shen
Qiang Qu
Haixia Chai

In this paper, we propose a Document Embedding Network (DEN) to learn document embeddings in an unsupervised manner. Our model uses the encoder-decoder architecture as its backbone, which tries to reconstruct the input document from an encoded document embedding. Unlike the standard decoder for text reconstruction, we randomly block some words in the input document, and use the incomplete context information and the encoded document embedding to predict the blocked words in the document, inspired by the crossword game. Thus, our decoder can keep the balance between the known and unknown information, and consider both global and partial information when decoding the missing words. We evaluate the learned document embeddings on two tasks: document classification and document retrieval. The experimental results show that our model substantially outperforms the compared methods. 1.

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