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Yue Wang

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100 papers

2 author rows

EAAI Journal 2026 Journal Article

A reparameterized hierarchical feature learning network designed for accurate sizing and localization of steel surface defects

Ronggang Ge
Yue Wang
Yonggeng Wei

Accurately measuring the size and spatial distribution of surface defects on steel products is essential for ensuring product quality. However, existing detection methods exhibit notable limitations in achieving high-precision measurements of defect dimensions and spatial localization. To address this issue, this study proposes two new modules: the reparameterized multi-scale receptive field module (RMRFM) and the hierarchical enhanced feature propagation network (HEFPN). By integrating the strengths of both modules, we further develop a unified detection architecture, termed You Only Look Once with Reparameterized Hierarchical Feature Learning (YOLO-RH). RMRFM significantly improves the model's measurement accuracy of defect size and spatial distribution without sacrificing detection speed through a reparameterized multi branch feature extraction strategy Meanwhile, HEFPN introduces a cross-layer feature interaction mechanism that effectively preserves shallow-layer texture information during feature extraction, providing essential support for the accurate measurement of defect attributes. Extensive experiments conducted on the NEU-DET dataset demonstrate that both RMRFM and HEFPN yield strong individual performance, while their combination in YOLO-RH achieves AP50, AP50: 95, AR, APS, and ARS scores of 71. 9%, 38. 4%, 55. 1%, 49. 1%, and 61. 1%, respectively, which were 3. 7%, 1. 9%, 5. 2%, 11. 8%, and 12. 6% higher than baseline and consistently outperformed other state-of-the-art methods. Furthermore, generalization experiments on GC10-DET and PV-Multi datasets confirm the robustness of YOLO-RH across different materials and defect types. Finally, a steel defect measurement platform based on YOLO-RH is developed to validate its practical feasibility, offering a viable solution for intelligent defect measurement and automated sorting in real-world industrial environments.

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EAAI Journal 2026 Journal Article

Efficient and robust shoveling control system based on semantic elevation mapping for unmanned loaders

Guangda Chen
Zhiwen Zhang
Lin Cheng
Cheng Jin
Shunyi Yao
Yue Wang
Rong Xiong
Yingfeng Chen

Improving the automation of wheeled loaders is key to solving labor gaps and boosting safety in construction. This paper proposes an automatic shoveling system for unmanned loaders that, for the first time, balances safety, robustness, efficiency, and energy consumption. The system features automatic calibration of camera and light detection and ranging (LiDAR) using large segmentation models and nonlinear optimization, ensuring stability despite vibrations. A lightweight neural network performs semantic segmentation, and multi-frame point clouds are fused with a confidence algorithm for accurate pile segmentation. The shoveling point selection algorithm integrates semantic and elevation data to prioritize loader and environmental safety. Volume prediction initiates scooping, and a shoveling strategy balances robustness and efficiency. Extensive field tests conducted over two months with two types of loaders in three scenarios, totaling 2090 operations, demonstrate the system’s long-term stability, high bucket full rates, efficiency matching manual operations, and an 11% reduction in energy consumption. These results highlight the system’s potential to transform automated construction machinery.

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JBHI Journal 2026 Journal Article

M $^{3}$- DEGREES Net: Monocular-Guided Metric Marching Depth Estimation With Graph-Based Relevance Ensemble for Endoluminal Surgery

Bo Lu
Tiancheng Zhou
Qingbiao Li
Wenzheng Chi
Yue Wang
Yu Wang
Huicong Liu
Jia Gu

Robotic endoluminal surgery has gained tremendous attention for its enhanced treatments in gastrointestinal intervention, where navigating surgeons with monocular camera-based metric depth estimation is a vital sector. However, existing methods either rely on external sensors or perform poorly in terms of visual navigation. In this work, we present our M $^{3}$ - Degrees Net, a novel monocular vision-guided and graph learning-based network tailored for accurate metric marching depth (MD) estimation. We first leverage a generative model to output a scale-free depth map, providing a depth basis in a coarse granularity. To achieve an optimized and metric MD prediction, a relational graph convolutional network with multi-modal visual knowledge fusion is devised. It utilizes shared salient features between keyframes and encodes their pixel differences on the depth basis as the main node, while a projection length-based node that predicts the MD on a proportional relationship basis is introduced, aiming to enable the network with explicit depth awareness. Moreover, to compensate for rotation-induced MD estimation bias, we model the endoscope’s orientation changes as image-level feature shifts, formulating an ego-motion correction node for MD optimization. Lastly, a multi-layer regression network for the metric MD estimation with finer granularity is devised. We validate our network on both public and in-house datasets, and the quantitative results reveal that it can limit the overall MD error under 27. 3%, which vastly outperforms the existing methods. Besides, our M $^{3}$ - Degrees Net is qualitatively tested on the in-house clinical gastrointestinal endoscopy data, demonstrating its satisfactory performance even under cavity mucus with varying reflections, indicating promising clinical potentials.

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AAAI Conference 2026 Conference Paper

ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning

Debamita Ghosh
George K. Atia
Yue Wang

Reinforcement learning (RL) faces significant challenges in real-world deployments due to the sim-to-real gap, where policies trained in simulators often underperform in practice due to mismatches between training and deployment conditions. Distributionally robust RL addresses this issue by optimizing worst-case performance over an uncertainty set of environments and providing an optimized lower bound on deployment performance. However, existing studies typically assume access to either a generative model or offline datasets with broad coverage of the deployment environment—assumptions that limit their practicality in unknown environments without prior knowledge. In this work, we study the more realistic and challenging setting of online distributionally robust RL, where the agent interacts only with a single unknown training environment while aiming to optimize its worst-case performance. We focus on general f-divergence-based uncertainty sets, including chi-squared and KL divergence balls, and propose a computationally efficient algorithm with sublinear regret guarantees under minimal assumptions. Furthermore, we establish a minimax lower bound on regret of online learning, demonstrating the near-optimality of our approach. Extensive experiments across diverse environments further confirm the robustness and efficiency of our algorithm, validating our theoretical findings.

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EAAI Journal 2026 Journal Article

Power System state prediction method based on improved long short-term memory considering renewable energy uncertainty

Yue Yu
Chihan Zhou
Yue Wang
Tao Lu
Ziqi Fan

To address the multi-dimensional uncertainty issues brought by high-penetration renewable energy sources grid integration, a power system state prediction method based on improved long short-term memory (LSTM) considering renewable energy uncertainty is proposed. First, a deep bidirectional long short-term memory neural network (Deep Bi-LSTM) is employed as the foundation of the Bayesian framework, capturing uncertainties in active distribution networks through posterior inference. Second, to address the limitations of the improved Deep Bi-LSTM model in feature extraction, an attention mechanism is introduced and a global-attention long short-term memory (GLSTM) model is constructed to strengthen the correlation between input features and target features. Finally, a novel state prediction method is proposed, which utilizes the GLSTM model to predict dynamic state variables, achieving overall modeling and quantification of both model uncertainty and aleatory uncertainty. Through simulation experiments conducted on IEEE 69-bus standard test systems, it is verified that the proposed Bayesian deep learning (BDL) method can effectively capture both types of uncertainties and achieve high-precision state prediction, providing a new feasible approach for the field of state prediction.

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AAAI Conference 2026 Conference Paper

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Lejun Ai
Yulong Li
Haodong Yi
Jixuan Xie
Yue Wang
Jia Liu
Min Chen
Rui Wang

Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

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AAMAS Conference 2026 Conference Paper

Robust Direct Preference Optimization for Offline Learning

Shihab Ahmed
Zhenyi Wang
Yue Wang

Recent preference-based alignment methods, such as Direct Preference Optimization (DPO), perform well under comprehensive offline datasets. However, practical datasets often contain sparse, noisy, and unevenly distributed comparisons, which can degrade model performance. To address this, we first adopt the principle of pessimism and propose a Robust DPO framework that optimizes for the worst-case reward within some data-dependent uncertainty set, and show its effectiveness in offline problems. Moreover, we show that the resulting robust optimal policy can be obtained by directly fine-tuning a baseline DPO model, avoiding the need for retraining. We further construct an uncertainty set to tackle the data uncertainty, based on the graph Laplacian, and show the set contains the true underlying reward with high probability. We then further evaluate the effectiveness of our method in controlled tabular and LLM setting, which validate our theoretical finds.

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AAAI Conference 2026 Conference Paper

SSHPool: The Separated Subgraph-based Hierarchical Pooling

Zhuo Xu
Lu Bai
Lixin Cui
Ming Li
Hangyuan Du
Ziyu Lyu
Yue Wang
Edwin R. Hancock

In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ the local graph convolution units as the local structure to further compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. Since these subgraphs are separated by different clusters and the structural information cannot be propagated between them, the local convolution operation can significantly avoid the over-smoothing problem caused by message passing through edges in most existing Graph Neural Networks (GNNs). By hierarchically performing the proposed procedures on the resulting coarsened graph, the proposed SSHPool can effectively extract the hierarchical global features of the original graph structure, encapsulating rich intrinsic structural characteristics. Furthermore, we develop an end-to-end GNN framework associated with the SSHPool module for graph classification. Experimental results demonstrate the superior performance of the proposed model on real-world datasets.

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AAAI Conference 2026 Conference Paper

Stabilizing Policy Gradient Methods via Reward Profiling

Shihab Ahmed
El Houcine Bergou
Yue Wang
Aritra Dutta

Policy gradient methods, which have been extensively studied in the last decade, offer an effective and efficient framework for reinforcement learning problems. However, their performances can often be unsatisfactory, suffering from unreliable reward improvements and slow convergence, due to high variance in gradient estimations. In this paper, we propose a universal reward profiling framework that can be seamlessly integrated with any policy gradient algorithm, where we selectively update the policy based on high-confidence performance estimations. We theoretically justify that our technique will not slow down the convergence of the baseline policy gradient methods, but with high probability, will result in stable and monotonic improvements of their performance. Empirically, on eight continuous‐control benchmarks (Box2D and MuJoCo/PyBullet), our profiling yields up to 1.5x faster convergence to near‐optimal returns, up to 1.75x reduction in return variance on some setups. Our profiling approach offers a general, theoretically grounded path to more reliable and efficient policy learning in complex environments.

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EAAI Journal 2025 Journal Article

A 6-dimensional pose estimation method combining sparse viewpoint classification initialization and optical flow-guided iterative refinement

Huan Yang
Yue Wang
Xinghang Yin
Yongxu Liu
Peng Wang

Electronic equipment is typically a complex and high-precision electromechanical system, where the routing and bundling of Radio Frequency (RF) cables are crucial to equipment performance. Traditional assembly methods require workers to assemble according to the assembly process card, which can easily lead to incorrect or missing assembly, poor assembly consistency, and low efficiency. Augmented Reality (AR) assembly guidance can effectively improve efficiency and reduce errors. 6-dimensional (6D) pose estimation is a key technology for AR assembly guidance. In the assembly process of complex electronic products, existing deep learning methods suffer from poor tracking and localization robustness and real-time performance due to factors such as arm occlusion, resulting in slow tracking recovery. This article proposes a two-stage real-time 6D pose estimation method from coarse to fine, which can estimate the pose of target objects in complex backgrounds at a speed of about 20 frames per second and quickly recover after tracking target loss. The real-time and effectiveness were verified through experiments on the red squirrel and electronic chassis.

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IROS Conference 2025 Conference Paper

Adaptive Neural Uncalibrated Visual Servo with Zero-shot Transfer of Extrinsics and Scenes

Anzhe Chen
Shuxin Li
Hongxiang Yu
Zhongxiang Zhou
Rong Xiong
Yue Wang

Deploying visual servo controller to novel scenes with uncertain parameters requires additional manual effort for calibration. Traditional methods tackle this problem by online estimating the Jacobian matrix. However, they struggle in challenging scenes due to intrinsic limitations. For instance, image-based uncalibrated visual servo requires tracking a fixed set of points, which is impractical in texture-less scenes. Position-based uncalibrated visual servo necessitates absolute scale of translation, which requires depth sensor or model-based pose estimator, introducing extra hardware cost or model complexity. Recent advances in neural network-based visual servoing have shown improvement in convergence, precision and generalization compared to traditional methods. However, the uncalibrated neural visual servo remains underexplored. In this paper, we propose a structured Jacobian estimator for neural-based visual servo controller, enabling zero-shot transfer to novel environments with unknown extrinsic and scene scale. Stability of pose error is analyzed under the bounded calibration error assumption. Moreover, we propose an automatic control gain scheduler to accelerate the convergence while maintaining high success rate and precision. The scheduling behavior is analyzed through greedy optimal control. Our method is validated with simulated and real-world experiments.

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AAAI Conference 2025 Conference Paper

Adaptive Wavelet-Positional Encoding for High-Frequency Information Learning in Implicit Neural Representation

Hongxu Zhao
Zelin Gao
Yue Wang
Rong Xiong
Yu Zhang

Implicit Neural Representation (INR) has shown great potential in constructing the complex nature signal as a continuous implicit function. However, the representation results are incomplete since different components of the signal correspond to different frequencies and neural network inherently tends to low-frequency convergence. In this paper, we propose the adaptive Wavelet-Positional Encoding (WPE) to precisely represent content under different frequency distributions for coordinate-based implicit representations. The High-Frequency Perception (HFP) method is first proposed to query locations of high-frequency components from input signals, which can be indicated as local centers of WPE. Then, motivated by wavelet series regression, we present to embed these queried low-dimensional coordinate inputs into wavelet-frequency space by WPE to represent fine details of target signals. Experiments demonstrate that the proposed method can be integrated into various INR methods without modifying training frameworks while significantly improving their performance in 1D signal fitting, 2D image regression, and even 3D scene representation.

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IJCAI Conference 2025 Conference Paper

AKBR: Learning Adaptive Kernel-based Representations for Graph Classification

Lu Bai
Feifei Qian
Lixin Cui
Ming Li
Hangyuan Du
Yue Wang
Edwin Hancock

In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning mechanism for the classifier, the proposed AKBR approach aims to define an end-to-end representation learning model to construct an adaptive kernel matrix for graphs. To this end, we commence by leveraging a novel feature-channel attention mechanism to capture the interdependencies between different substructure invariants of original graphs. The proposed AKBR model can thus effectively identify the structural importance of different substructures, and compute the R-convolution kernel between pairwise graphs associated with the more significant substructures specified by their structural attentions. Furthermore, the proposed AKBR model employs all sample graphs as the prototype graphs, naturally providing an end-to-end learning architecture between the kernel computation as well as the classifier. Experimental results show that the proposed AKBR model outperforms existing state-of-the-art graph kernels and deep learning methods on standard graph benchmarks.

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IJCAI Conference 2025 Conference Paper

An End-to-End Simple Clustering Hierarchical Pooling Operation for Graph Learning Based on Top-K Node Selection

Zhehan Zhao
Lu Bai
Ming Li
Lixin Cui
Hangyuan Du
Yue Wang
Edwin Hancock

Graph Neural Networks (GNNs) are powerful tools for graph learning, but one of the important challenges is how to effectively extract representations for graph-level tasks. In this paper, we propose an end-to-end Simple Clustering Hierarchical Pooling (SCHPool) operation, which is based on Top-K node selection for learning expressive graph representations. Specifically, SCHPool considers each node and its local neighborhood as a cluster, and introduces a novel multi-view scoring function to evaluate node importance. Based on these scores, clusters centered around the Top-K nodes are retained. This design eliminates the need for complex clustering operations, significantly reducing computational overhead. Furthermore, during the coarsening process, SCHPool employs a lightweight yet comprehensive attention mechanism to adaptively aggregate both the node features within clusters and the edge connectivity strengths between clusters. This facilitates the construction of more informative coarsened graphs, enhancing model performance. Experimental results demonstrate the effectiveness of the proposed model.

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IROS Conference 2025 Conference Paper

An Intelligent Skeleton Based on Liquid Metal for Biohybrid Actuator Powered by Muscle

Xiaoqi Lu
Yuyin Zhang
Yunajie Gan
Shen Gao
Yue Wang
Na Liu
Tao Yue

Biological machines that use biological cells and soft materials in combination to obtain a sense of the environment driven by bioenergy and generate driving force are called biohybrid actuators. With the development of tissue engineering and organoid technology, researchers have applied biohybrid actuators technology to the research of precision medicine and targeted drug delivery, but the research on feedback and evaluation of biohybrid actuation performance is limited to visual and simulation calculations. Therefore, we hope to develop an intelligent crawling skeleton for sensing function, which can be used to evaluate the actuation ability of muscle actuators, and eventually realize the high-precision control of biohybrid actuators. In this work, an intelligent crawling skeleton based on three-dimensional liquid metal is proposed to detect and feedback the crawling of C2C12 muscle actuators. Three-dimensional muscle tissue was composed of mixing hydrogels and cells, and the functionalization of muscle rings was promoted using static mechanical forces and external electric field stimulation. The composite crawling skeleton is fabricated by inverting mold and soft lithography technology. The skeleton can adapt to large deformations above 90 degrees and is more sensitive to deformations by adjusting materials with different elastic modulus. Inspired by the tendon-bone structure, the intelligent crawling skeleton can obtain the deformation degree of the biohybrid actuator in the crawling process according to the characteristics of the deformation from the muscle tissue, and put forward a good idea for the feedback and closed-loop control of the biohybrid actuators.

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IROS Conference 2025 Conference Paper

Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain

Wei Zhang
Yinchuan Wang
Wangtao Lu
Pengyu Zhang
Xiang Zhang
Yue Wang
Chaoqun Wang

It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stability of the robot on rough terrain is analyzed. Based on the tip-over stability, we define the traversable orientation, which indicates the safe range of robot orientations. This orientation is then incorporated into a capsizing-safety constraint for trajectory optimization. We employ a graph-based solver to compute a robust and feasible trajectory while adhering to the capsizing-safety constraint. Extensive simulation and real-world experiments validate the effectiveness and robustness of the proposed method. The results demonstrate that CAP outperforms existing state-of-the-art approaches, providing enhanced navigation performance on uneven terrains.

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AAAI Conference 2025 Conference Paper

Contradicted in Reliable, Replicated in Unreliable: Dual-Source Reference for Fake News Early Detection

Yifan Feng
Weimin Li
Yue Wang
Jingchao Wang
Fangfang Liu
Zhongming Han

Early detection of fake news is crucial to mitigate its negative impact. Current research in fake news detection often utilizes the difference between real and fake news regarding the support degree from reliable sources. However, it has overlooked their different semantic outlier degrees among unreliable source information during the same period. Since fake news often serves idea propaganda, unreliable sources usually publish a lot of information with the same propaganda idea during the same period, making it less likely to be a semantic outlier. To leverage this difference, we propose the Reliable-Unreliable Source Reference (RUSR) Fake News Early Detection Method. RUSR introduces the publication background for detected news, which consists of related news with common main objects of description and slightly earlier publication from both reliable and unreliable sources. Furthermore, we develop a strongly preference-driven support degree evaluation model and a two-hop semantic outlier degree evaluation model, which respectively mitigate the interference of news with weak validation effectiveness and the tightness degree of semantic cluster. The designed redistribution module and expanding range relative time encoding are adopted by both models, respectively optimizing early checkpoint of training and expressing the relevance of news implied by their release time gap. Finally, we present a multi-model mutual benefit and collaboration framework that enables the multi-model mutual benefit of generalization in training and multi-perspective prediction of news authenticity in inference. Experiments on our newly constructed dataset demonstrate the superiority of RUSR.

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AIIM Journal 2025 Journal Article

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction

Qiao Ning
Yue Wang
Yaomiao Zhao
Jiahao Sun
Lu Jiang
Kaidi Wang
Minghao Yin

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions prediction are more popular in recent years. Conventional computational methods almost simply view heterogeneous network constructed by the drug-related and protein-related dataset instead of comprehensively exploring drug-protein pair (DPP) information. To address this limitation, we proposed a Double Multi-view Heterogeneous Graph Neural Network framework for drug-target interaction prediction (DMHGNN). In DMHGNN, one multi-view heterogeneous graph neural network is based on meta-paths and denoising autoencoder for protein-, drug-related heterogeneous network learning, and another multi-view heterogeneous graph neural network is based on multi-channel graph convolutional network for drug-protein pair similarity network learning. First, a meta-path-based graph encoder with the attention mechanism is used for substructure learning of complex relationships from heterogeneous network constructed by proteins, drugs, side-effects and diseases, obtaining key information that is easy to be ignored in global learning of heterogeneous networks, and multi-source neighbouring features for drugs and proteins are learned from heterogeneous network via denoising auto-encoder model. Then, multi-view graphs of drug-protein pairs (DPPs) including the topology graph, semantics graph and collaborative graph with shared weights are constructed, and the multi-channel graph convolutional network (GCN) is utilized to learn the deep representation of DPPs. Finally, a multi-layer fully connection network is trained to predict drug-target interactions. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.

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IROS Conference 2025 Conference Paper

Domain-Conditioned Scene Graphs for State-Grounded Task Planning

Jonas Herzog
Jiangpin Liu
Yue Wang

Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split the pipeline into perceptional state grounding and subsequent state-based planning. As we show in this work, the state grounding ability of LMM-based approaches is still limited by weaknesses in granular, structured, domain-specific scene understanding. To address this shortcoming, we develop a more structured state grounding framework that features a domain-conditioned scene graph as its scene representation. We show that such representation is actionable in nature as it is directly mappable to a symbolic state in planning languages such as the Planning Domain Definition Language (PDDL). We provide an instantiation of our state grounding framework where the domain-conditioned scene graph generation is implemented with a lightweight vision-language approach that classifies domain-specific predicates on top of domain-relevant object detections. Evaluated across three domains, our approach achieves significantly higher state grounding accuracy and task planning success rates compared to LMM-based approaches. https://github.com/Vision-Kek/DC-SGG

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EAAI Journal 2025 Journal Article

Event-triggered predefined-time tracking control for high-order nonlinear systems with time-varying actuator failures and uncertain disturbances

Yue Wang
Jie Gao
Junchan Zhao
Xingyu Wu

For a class of higher-order nonlinear system control problems with time-varying actuator failures and external disturbances, this paper designs efficient control strategies that allow the system to be stabilized in a predefined time. First, for such systems, this paper designs an effective predefined-time control strategy using the backstepping control method combined with the adaptive radial basis neural network technique, which makes the stabilization time of the system simple and adjustable. Secondly, while using the command filtering technique to solve the “complexity explosion” problem in the design of controllers for high-order nonlinear systems, this paper designs a novel predefined-time filtering error compensation mechanism to eliminate the impact of filtering errors on the stability of the system. Finally, an event-triggered mechanism is introduced, which effectively saves the communication resources. The effectiveness of the control strategy proposed in this paper is demonstrated by the simulation experiments.

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YNIMG Journal 2025 Journal Article

Expertise-related functional connectivity changes in Chinese calligraphy linked to flow experience

Qingyan Kong
Yue Wang
Min Li
Buxin Han
Rui Li

Flow is a deeply immersive state that supports optimal performance, yet its neural basis under conditions of real-world expertise remains poorly understood. Using functional MRI, this study investigated how long-term Chinese calligraphy expertise relates to flow in a culturally meaningful setting. Expert and novice participants performed imagined embodied handwriting of Kai-Shu and Cao-Shu, which differ in motor and cognitive challenges. Expert calligraphers reported significantly higher flow than novices across both scripts, including in the more challenging Cao-Shu style despite having no formal training in it. Functional connectivity analyses were performed on background task-residual BOLD signals to assess intrinsic coupling that persists during performance. In Kai-Shu, experts showed stronger ventral anterior insula (vAI)-superior parietal lobule (SPL) connectivity and weaker vAI-ventral striatum (VS) connectivity, suggesting enhanced perception-action coupling and reduced task-irrelevant processing. In Cao-Shu, experts exhibited reduced anterior medial prefrontal cortex (aMPFC) connectivity with default mode network (DMN) regions, suggesting reduced self-referential processing under higher task challenges. These connectivity patterns were significantly associated with reported flow ratings and together suggest a flexible neural adaptation supporting task-focused engagement in familiar contexts and reduced introspection when demands increase. To further examine whether these effects form an integrated mechanism linking proficiency and flow, Bayesian network (BN) modeling revealed a directional dependency from expertise to functional connectivity to flow, suggesting that long-term practice contributes to a proficient neural mechanism that supports higher flow experiences during task engagement. These findings extend current accounts of flow by delineating how sustained expertise is associated with neural processing patterns that are linked to higher flow across varying task challenges.

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IJCAI Conference 2025 Conference Paper

Exploring the Over-smoothing Problem of Graph Neural Networks for Graph Classification: An Entropy-based Viewpoint

Feifei Qian
Lu Bai
Lixin Cui
Ming Li
Hangyuan Du
Yue Wang
Edwin Hancock

The over-smoothing has emerged as a major challenge in the development of Graph Neural Networks (GNNs). While existing state-of-the-art methods effectively mitigate the diminishing distance between nodes and improve the performance of node classification, they tend to be elusive for graph-level tasks. This paper introduces a novel entropy-based perspective to explore the over-smoothing problem, simultaneously enhancing the distinguishability of non-isomorphic graphs. We provide a theoretical analysis of the relationship between the smoothness and the entropy for graphs, highlighting how the over-smoothing in high-entropic regions negatively impact the graph classification performance. To tackle this issue, we propose a simple yet effective method to Sample and Discretize node features in high-Entropic regions (SDE), aiming to preserve the critical and complicated structural information. Moreover, we introduce a new evaluation metric to assess the over-smoothing for graph-level tasks, focusing on node distributions. Experimental results demonstrate that the proposed SDE method significantly outperforms existing state-of-the-art methods, establishing a new benchmark in the field of GNNs.

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ICLR Conference 2025 Conference Paper

Fantastic Copyrighted Beasts and How (Not) to Generate Them

Luxi He
Yangsibo Huang
Weijia Shi
Tinghao Xie
Haotian Liu
Yue Wang
Luke Zettlemoyer
Chiyuan Zhang

Recent studies show that image and video generation models can be prompted to reproduce copyrighted content from their training data, raising serious legal con- cerns about copyright infringement. Copyrighted characters (e.g., Mario, Batman) present a significant challenge: at least one lawsuit has already awarded damages based on the generation of such characters. Consequently, commercial services like DALL·E have started deploying interventions. However, little research has systematically examined these problems: (1) Can users easily prompt models to generate copyrighted characters, even if it is unintentional?; (2) How effective are the existing mitigation strategies? To address these questions, we introduce a novel evaluation framework with metrics that assess both the generated image’s similarity to copyrighted characters and its consistency with user intent, grounded in a set of popular copyrighted characters from diverse studios and regions. We show that state-of-the-art image and video generation models can still generate characters even if characters’ names are not explicitly mentioned, sometimes with only two generic keywords (e.g., prompting with “videogame, plumber” consistently gener- ates Nintendo’s Mario character). We also introduce semi-automatic techniques to identify such keywords or descriptions that trigger character generation. Using this framework, we evaluate mitigation strategies, including prompt rewriting and new approaches we propose. Our findings reveal that common methods, such as DALL·E’s prompt rewriting, are insufficient alone and require supplementary strategies like negative prompting. Our work provides empirical grounding for discussions on copyright mitigation strategies and offers actionable insights for model deployers implementing these safeguards.

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NeurIPS Conference 2025 Conference Paper

First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training

Lai Wei
Yuting Li
Chen Wang
Yue Wang
Linghe Kong
Weiran Huang
Lichao Sun

Improving Multi-modal Large Language Models (MLLMs) in the post-training stage typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL), which require expensive and manually annotated multi-modal data--an ultimately unsustainable resource. This limitation has motivated a growing interest in unsupervised paradigms as a third stage of post-training after SFT and RL. While recent efforts have explored this direction, their methods are complex and difficult to iterate. To address this, we propose MM-UPT, a simple yet effective framework for unsupervised post-training of MLLMs, enabling continual self-improvement without any external supervision. The training method of MM-UPT builds upon GRPO, replacing traditional reward signals with a self-rewarding mechanism based on majority voting over multiple sampled responses. Our experiments demonstrate that such training method effectively improves the reasoning ability of Qwen2. 5-VL-7B (e. g. , 66. 3\%$\rightarrow$72. 9\% on MathVista, 62. 9\%$\rightarrow$68. 7\% on We-Math), using standard dataset without ground truth labels. To further explore scalability, we extend our framework to a data self-generation setting, designing two strategies that prompt the MLLM to synthesize new training samples on its own. Additional experiments show that combining these synthetic data with the unsupervised training method can also boost performance, highlighting a promising approach for scalable self-improvement. Overall, MM-UPT offers a new paradigm for autonomous enhancement of MLLMs, serving as a critical third step after initial SFT and RL in the absence of external supervision. Our code is available at \url{https: //github. com/waltonfuture/MM-UPT}.