一句话：提出“AI 模型基因组”生物类比框架，将模型架构、训练数据和参数映射为物种、遗传物质与基因序列，以直观理解 AI 模型的演化。
论文关注人工智能模型尤其是大语言模型在现代计算系统中日益重要，却缺乏直观框架来理解其结构、功能与演化的问题。作者提出“AI 模型基因组”框架，通过生物学类比将 AI 模型组件映射到基因组对应物：模型架构对应物种分类，训练数据对应遗传物质，模型参数对应基因序列，从而构建一个统一的概念化模型开发视角。该框架旨在帮助研究者与实践者从生物演化角度理解模型设计、修改策略和演化路径，提供更易理解的心智模型，并可能加速 AI 领域创新。论文属于概念性/立场性工作，未报告具体实验评估或工具实现。
Artificial intelligence models, particularly large language models (LLMs), have become increasingly important and prevalent in modern computational systems. Their rapid development has created a need for more intuitive frameworks to understand their structure, function, and evolution. This paper introduces the "AI Model Genome" framework, a novel biological analogy that maps AI model components to their genomic counterparts. By drawing parallels between model architectures and species classification, training data and genetic material, and model parameters and gene sequences, we establish a comprehensive framework for conceptualizing model development. This biological perspective offers researchers and practitioners new insights into model design, modification strategies, and evolutionary pathways, potentially accelerating innovation in the field while providing a more accessible mental model for understanding complex AI systems.
DOI 原文 ·
@inproceedings{paperbot9030,
title = {AI Model Genome},
author = {Yanjie Zhao and Haoyu Wang},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3730571}
}