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Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks fail to adequately support large-scale simulation development due to inherent limitations in adaptability, configurability, reliability, and code reusability. For example, they cannot simulate a society where the agent population and profiles change over time. To fill this gap, we propose Agent-Kernel, a framework built upon a novel society-centric modular microkernel architecture. It decouples core system functions from simulation logic and separates cognitive processes from physical environments and action execution. Consequently, Agent-Kernel achieves superior adaptability, configurability, reliability, and reusability. We validate the framework's superiority through two distinct applications: a simulation of the Universe 25 (Mouse Utopia) experiment, which demonstrates the handling of rapid population dynamics from birth to death; and a large-scale simulation of the Zhejiang University Campus Life, successfully coordinating 10,000 heterogeneous agents, including students and faculty.

The acronym dataset is a QA task that requires models to decode financial acronyms. The FinMA7B-full model achieved the highest ROUGE-1 score of 0.12 and the B.1 Why was the datasheet created? B.2 Has the dataset been used already? If so, where are the results so others can compare (e.g., links to published papers)? Y es, the dataset has already been used. It was employed in the FinLLM Share Task during the FinNLP-AgentScen Workshop at IJCAI 2024, known as the FinLLM Challenge.

Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality.

For all other remaining architectures, the reported results are from private datasets. Neck Shaft Angle(NSA) cannot be estimated. Additionally, [? ] requires estimation of the diaphysis Figure 4: Repeatability of the femur morphometry extraction method as measured by error distributions for a) the landmarks/anatomical sizes and b) axis alignment identified by the adapted method. Do the main claims made in the abstract and introduction accurately reflect the paper's Did you specify all the training details (e.g., data splits, hyperparameters, how they were Data splits are available in the GitHub repository. Did you report error bars (e.g., with respect to the random seed after running ex-67 Did you include the total amount of compute and the type of resources used (e.g., Did you mention the license of the assets?