AI Model Genome
Yanjie Zhao, Haoyu Wang · 2025
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.