Xiubo Liang

Kaifeng Wei, Teng Liu, Liang Dong et al.

Graph Anomaly Detection (GAD) is increasingly shifting to Generalist GAD (GGAD) for cross-domain "one-for-all" detection, but existing GGAD methods predominantly rely on the neighbor consistency principle, falling into the \textbf{Node-to-Neighbor Consistency Paradigm} for anomaly quantification. These methods suffer from complex training pipelines, heavy training data dependency, high computational costs, and unstable cross-domain generalization. To address these limitations, we propose NeighborDiv, a training-free generalist graph anomaly detection framework based on neighbor diversity. Departing from the dominant Node-to-Neighbor Consistency Paradigm, we shift the focus to the \textbf{Neighbor-to-Neighbor Diversity Paradigm}, and uncover that the internal structural dispersion of a node's neighbor set is a powerful, independently discriminative anomaly signal. We quantify neighbor diversity via the variance of inter-neighbor feature similarities, which captures how a node organizes its local graph environment, and operates independently of conventional node-to-neighbor consistency frameworks. Extensive experiments under two standard GGAD evaluation paradigms show NeighborDiv achieves state-of-the-art performance, with relative gains of 10.25% in average AUC and 17.78% in average AP over the second-best baseline under Single-Domain Independent Training (SDIT), and 6.89%/9.58% in AUC/AP under Unified Multi-Domain Training (UMDT), respectively. Notably, NeighborDiv yields zero performance volatility across all datasets, eliminating training-set dependency and establishing a lightweight and highly practical GGAD framework.

Kangjia Zhao, Jiahui Song, Leigang Sha et al.

Nowadays, research on GUI agents is a hot topic in the AI community. However, current research focuses on GUI task automation, limiting the scope of applications in various GUI scenarios. In this paper, we propose a formalized and comprehensive environment to evaluate the entire process of automated GUI Testing (GTArena), offering a fair, standardized environment for consistent operation of diverse multimodal large language models. We divide the testing process into three key subtasks: test intention generation, test task execution, and GUI defect detection, and construct a benchmark dataset based on these to conduct a comprehensive evaluation. It evaluates the performance of different models using three data types: real mobile applications, mobile applications with artificially injected defects, and synthetic data, thoroughly assessing their capabilities in this relevant task. Additionally, we propose a method that helps researchers explore the correlation between the performance of multimodal language large models in specific scenarios and their general capabilities in standard benchmark tests. Experimental results indicate that even the most advanced models struggle to perform well across all sub-tasks of automated GUI Testing, highlighting a significant gap between the current capabilities of Autonomous GUI Testing and its practical, real-world applicability. This gap provides guidance for the future direction of GUI Agent development. Our code is available at https://github.com/ZJU-ACES-ISE/ChatUITest.

Bo Zhang, Xiubo Liang, Wei Song et al.

Within modern warehouse scenarios, the rapid expansion of e-commerce and increasingly complex, multi-level storage environments have exposed the limitations of traditional AGV (Automated Guided Vehicle) path planning methods--often reliant on static 2D models and expert-tuned heuristics that struggle to handle dynamic traffic and congestion. Addressing these limitations, this paper introduces a novel AGV path planning approach for 3D warehouse environments that leverages a hybrid framework combining ACO (Ant Colony Optimization) with deep learning models, called NAHACO (Neural Adaptive Heuristic Ant Colony Optimization). NAHACO integrates three key innovations: first, an innovative heuristic algorithm for 3D warehouse cargo modeling using multidimensional tensors, which addresses the challenge of achieving superior heuristic accuracy; second, integration of a congestion-aware loss function within the ACO framework to adjust path costs based on traffic and capacity constraints, called CARL (Congestion-Aware Reinforce Loss), enabling dynamic heuristic calibration for optimizing ACO-based path planning; and third, an adaptive attention mechanism that captures multi-scale spatial features, thereby addressing dynamic heuristic calibration for further optimization of ACO-based path planning and AGV navigation. NAHACO significantly boosts path planning efficiency, yielding faster computation times and superior performance over both vanilla and state-of-the-art methods, while automatically adapting to warehouse constraints for real-time optimization. NAHACO outperforms state-of-the-art methods, lowering the total cost by up to 24.7% on TSP benchmarks. In warehouse tests, NAHACO cuts cost by up to 41.5% and congestion by up to 56.1% compared to previous methods.