Issue Ebook
cover story
Current Issue
Issue 7, 2026
Editors-in-Chief
Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 3069-8928
CN 33-1439/TP
IF (2025 JCR) 3.2
| Submit Now |
- Current Issue
- Cover Articles
- Archive
- Virtual Issues
- Online First
Volume 27 Issue 7，2026
Regular Papers
A platform-aware survey of execution, verification, and risk of graphical user interface agents Enhanced Publication AI Introduction
Abstract：Graphical user interface (GUI) agents are widely used in general-purpose digital automation. Large language models, vision-language models, and multimodal foundation models can now interpret screenshots, follow natural-language instructions, and execute grounded actions in websites, mobile applications, and desktop operating systems. However, despite their different execution conditions, these settings are commonly grouped under one label. This survey reviews representative frameworks, models, datasets, and benchmarks of GUI agents through a common technical stack: observation, grounding, planning, memory, execution, and verification. We advocate the treatment of web, mobile, and desktop/operating-system agents as distinct operational regimes with different observabilities, action semantics, hidden-state dependences, execution costs, and side-effect risks. We also describe a shift from next-action prediction to dependable execution with increased focus on outcome verification, execution efficiency, and trustworthiness. The survey concludes with future directions in belief-state tracking, adaptive sensing, hybrid GUI–tool execution, and human oversight for reliable computer use.Keywords：Graphical user interface (GUI) agents;Operational regimes;Trustworthy evaluation;Postcondition verification10|2|0Updated：2026-08-11Regular Papers
CCU-Bench: a systematic benchmark for cross-cultural understanding in large language models Enhanced Publication AI Introduction
Abstract：Large language models (LLMs) have achieved strong performance on many language tasks, but they still struggle with culturally grounded symbolic reasoning. Existing benchmarks have not systematically evaluated the progressive capability chain required for cross-cultural understanding, which involves intra-cultural symbolic understanding, cross-cultural symbolic alignment, and cross-cultural conflict identification. To address this gap, we propose CCU-Bench, a systematic benchmark for evaluating cross-cultural understanding in LLMs. Grounded in Hofstede’s cultural dimensions theory, CCU-Bench focuses on three culturally distinct contexts, China, Japan, and Mexico, and is organized around three dimensions, image, connotation, and emotion. The benchmark is constructed through three stages, data collection and filtering, question–answer (QA) generation, and quality control, resulting in a high-quality evaluation set of 3029 QA pairs in five formats. Experiments on 19 mainstream LLMs show that current models perform unsatisfactorily on this task, achieving an average score of 57.8%. Closed-source models consistently outperform open-source models, while cross-cultural symbolic alignment remains the most challenging sub-task. Further error analysis reveals that the dominant failures stem from deficiencies in cultural knowledge, biases in intent mapping, and weak higher-order reasoning about cross-cultural conflicts, rather than simple instruction-following issues. These findings highlight persistent limitations of current LLMs in culturally grounded reasoning and demonstrate that CCU-Bench provides a standardized benchmark for culturally aware artificial intelligence research.Keywords：Benchmark;Large language models (LLMs);Cross-cultural understanding;LLM evaluation and benchmarks5|1|0Updated：2026-08-11Regular Papers
Dual-stream fusion with dynamic grid interaction for Chinese medical named entity recognition Enhanced Publication AI Introduction
Abstract：Chinese medical named entity recognition (CMNER) is a fundamental task in medical information extraction. It is crucial for building downstream applications, such as clinical knowledge graphs, and enabling intelligent clinical decision-making. However, existing mainstream approaches, including lexicon-enhanced, span-based, and grid-based tagging methods, struggle with the absence of natural boundaries, complex nested entity structures, and long-range contextual dependencies inherent in clinical texts. To address these challenges, we propose a novel dual-stream fusion with dynamic grid interaction (D2GI) model, which performs deep semantic mining by integrating complementary feature streams and adaptive grid interactions to accurately capture complex entity boundaries and inter-character relations. Specifically, our dual-stream fusion architecture leverages RoFormer to extract long-range dependencies and incorporates Word2Vec to provide stable prior semantics, thereby enhancing the representation of rare medical terms. Furthermore, to overcome the limitations of static refinement, the dynamic grid interaction module employs a gated attention mechanism to adaptively fuse local and global contexts, facilitating accurate recognition of nested entities. Multiple experiments on three public datasets demonstrate that D2GI is superior to state-of-the-art baselines, achieving F1-score improvements of 1.48 percentage points (PPs) on CMeEE-V2, 1.61 PPs on DiaKG, and 3.08 PPs on CCKS2020.Keywords：Dual-stream fusion;Dynamic grid interaction;Medical semantic mining;Medical information extraction20|2|0Updated：2026-08-11Regular Papers
A microservice framework for data–model fusion in situational awareness simulations Enhanced Publication AI Introduction
Abstract：In response to the increasing demand for heterogeneous data interaction and cross-disciplinary modeling in aerospace situational awareness simulations, we propose a microservice platform for data–model computing (MP-DMC), which is a high-performance microservice framework built on a container cloud. The proposed MP-DMC framework unifies the control of data and models through a distributed node resource management and scheduling strategy, integrating an election-optimized leader–follower mechanism, a predictive model based on a double-moving-average long short-term memory (DMA-LSTM) network for dynamic elastic scaling, and an intelligent load migration algorithm to address management inefficiencies, prevent node crashes, and mitigate resource oscillations under high-concurrency conditions. Experimental results demonstrate that the proposed MP-DMC framework outperforms mainstream algorithms in terms of election performance, node scaling efficiency, task response time, and load balancing, including consensus algorithms (Paxos, Raft, and PBFT), elastic scaling methods (HPA, DMA-HPA, ProSmart HPA, and RL), and scheduling algorithms (round-robin, purely random, and weighted random), achieving exceptional resource allocation performance and system availability.Keywords：Data–model fusion;High-performance simulation computing;Scheduling strategy;Node load prediction;Microservice6|2|0Updated：2026-08-11Regular Papers
Learning-based data-driven control for micro-nano free-floating space robots in Cartesian space Enhanced Publication AI Introduction
Abstract：This paper addresses the problem of end-effector position-tracking control for micro-nano free-floating space robots in Cartesian space without relying on explicit analytical kinematic or dynamic models. To address this challenge, we develop a two-layer learning architecture. In the first layer, a deep neural network is used for kinematic learning to capture the nonlinear mapping from end-effector Cartesian coordinates to joint angular velocities and to generate reference joint trajectories. In the second layer, a Koopman-operator-based network is employed to construct an approximately linearized representation of the joint-space dynamics of free-floating space robots. Based on this model, we propose a terminal fractional-order model predictive control scheme that incorporates the Grünwald–Letnikov fractional-order operator, thereby enhancing online control performance and improving tracking speed and accuracy relative to conventional model predictive control. Simulation results verify the effectiveness of the proposed method, demonstrating accurate and rapid end-effector trajectory tracking, all without requiring explicit analytical kinematic and dynamic models in the controller design, while the training pipeline relies solely on input–output trajectories.Keywords：Free-floating space robot;Deep neural networks;Model learning for control;Model predictive control8|2|0Updated：2026-08-11Regular Papers
Development and iterative learning-enhanced sliding mode control of a bioinspired knee exoskeleton Enhanced Publication AI Introduction
Abstract：This paper introduces a novel knee exoskeleton designed to provide assistive torque to the knee joint and facilitate postoperative rehabilitation. Drawing inspiration from the anatomy of the human knee joint, the exoskeleton incorporates a biomimetic structure that integrates the “optimal axis of rotation” with the trajectory of the instantaneous center of rotation. This design effectively mitigates the issue of kinematic misalignment in the human‒machine joint, offering a more comfortable wearing experience for users. Furthermore, a highly integrated quasi-direct drive system is incorporated into the knee exoskeleton. The drive system can deliver a continuous torque of 18 N·m and a peak torque of 45 N·m to sufficiently meet the daily walking needs of patients. From a control perspective, a unified sliding mode control strategy based on iterative learning control is proposed to maintain high tracking accuracy. This strategy enables the system to achieve accurate tracking of the knee motion trajectory by continuously updating the control inputs through successive gait cycles. Experimental results demonstrate that the designed biomimetic joint mechanism effectively resolves the issue of misalignment in the human‒machine joint. Furthermore, the synergistic control strategy demonstrates excellent steady-state precision and transient robustness in terms of trajectory tracking during human‒exoskeleton cooperative walking.Keywords：Human‒machine misalignment;Knee exoskeleton;Sliding mode control;Iterative learning control6|2|0Updated：2026-08-11
SEE MORE
0 1 Hybrid-augmented intelligence: collaboration and cognition
0 4 Applications of artificial intelligence in intelligent manufacturing: a review*
0 5 Challenges and opportunities: from big data to knowledge in AI 2.0
0 6 Current trends in the development of intelligent unmanned autonomous systems
0 7 Recent advances in efficient computation of deep convolutional neural networks
0 8 Terahertz time-domain spectroscopy and micro-cavity components for probing samples: a review
0 9 Cross-media analysis and reasoning: advances and directions
SEE MORE
Videos
-
00:02:512023 Issue 1 | Scalability and efficiency challenges for the exascale supercomputing system: practice of a parallel supporting environment on the Sunway exascale prototype system
2023-12-30Play Total： 30 - 00:02:30
2023 Issue 6 | Model division multiple access for semantic communications
2023-12-30Play Total： 22 - 00:02:15
2022 Issue 10 | Discussion on a new paradigm of endogenous security towards 6G networks
2023-12-30Play Total： 11 - 00:02:22
2022 Issue 12 | Technology trends in large-scale high-efficiency network computing
2023-12-30Play Total： 8 - 00:02:48
2022 Issue 6 | Self-deployed execution environment for high performance computing
2022-08-03Play Total： 17 - 00:02:24
2022 Issue 2 | A full-process intelligent trial system for smart court
2022-05-17Play Total： 20 - 00:02:37
2022 Issue 3 | Automatic protocol reverse engineering for industrial control systems with dynamic taint analysis
2022-05-17Play Total： 10 - 00:05:36
P1 Speech by Academician Baoyan Duan
2022-04-17Play Total： 26 - 00:02:27
P2 Speech by Professor Min Sheng, Xidian University
2022-04-17Play Total： 18 - 00:02:37
P3 Speech by Professor Yunsong Li, Xidian University
2022-04-17Play Total： 22
SEE MORE
- Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
- Tel：+86-571-88273162 Email：fitee@zju.edu.cn
- Copyright © 2000 - 2025, Zhejiang University Press 京ICP备09064830号-19
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0