A Two-Dimensional Study of the Model Context Protocol: Publication and Adoption
研究通过分析802篇文献和33,319个GitHub仓库，使用BERTopic和GPT-5.6-Luna分类器探讨了MCP协议在学术界和开源社区中的增长及应用情况。
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Selected work
Representative Papers
How effective are traditional test criteria at detecting bugs in large language models generated code?
研究探讨了传统测试标准在检测大型语言模型生成代码中的错误的有效性，使用了5个语言模型和4个基准进行实验，评估了语句覆盖、分支覆盖和变异测试三种方法。
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MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act
本文提出MARLA框架，通过五个阶段促进欧盟AI法案下的监管学习，解决技术与法律领域间证据转化问题。
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An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling
Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perceive situations, plan actions to pursue goals, and adapt to changing conditions, yet existing systems lack mechanisms for cross-agent cognitive processes coupling. This paper presents a conceptual cognitive agent model that formalises cognitive coupling through eight components: Input, Process, Output, State, Value, Memory, World Model, and Goal. The model abstracts how agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human-AI systems.
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Toward Robust LiDAR Semantic Segmentation for Real-World Deployment: Evaluation under Coarse Labels, Adverse Conditions, and Domain Shifts
本文针对激光雷达语义分割在实际部署中的鲁棒性问题，提出了一种评估协议，从粗粒度标签、恶劣条件及跨域变化三个维度进行评价。
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Recent publications
Latest Papers
A Two-Dimensional Study of the Model Context Protocol: Publication and Adoption
研究通过分析802篇文献和33,319个GitHub仓库，使用BERTopic和GPT-5.6-Luna分类器探讨了MCP协议在学术界和开源社区中的增长及应用情况。
0 citationsRead paper
How effective are traditional test criteria at detecting bugs in large language models generated code?
研究探讨了传统测试标准在检测大型语言模型生成代码中的错误的有效性，使用了5个语言模型和4个基准进行实验，评估了语句覆盖、分支覆盖和变异测试三种方法。
0 citationsRead paper
MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act
本文提出MARLA框架，通过五个阶段促进欧盟AI法案下的监管学习，解决技术与法律领域间证据转化问题。
0 citationsRead paper
An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling
Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perceive situations, plan actions to pursue goals, and adapt to changing conditions, yet existing systems lack mechanisms for cross-agent cognitive processes coupling. This paper presents a conceptual cognitive agent model that formalises cognitive coupling through eight components: Input, Process, Output, State, Value, Memory, World Model, and Goal. The model abstracts how agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human-AI systems.
0 citationsRead paper
Toward Robust LiDAR Semantic Segmentation for Real-World Deployment: Evaluation under Coarse Labels, Adverse Conditions, and Domain Shifts
本文针对激光雷达语义分割在实际部署中的鲁棒性问题，提出了一种评估协议，从粗粒度标签、恶劣条件及跨域变化三个维度进行评价。
0 citationsRead paper