As software complexity skyrockets and requirements evolve at breakneck speed, traditional human-centric behavioral modeling can no longer keep pace in terms of efficiency, accuracy, and scalability. While existing automated approaches can produce models, they still struggle with deep semantic understanding of textual requirements or with reasoning about intricate system logic, especially nested relationships. Inspired by the way experienced analysts “peel back” layers of a problem, we propose LATO, a \textbf{L}ayerwise \textbf{A}nalysis-Driven Au\textbf{T}omatic Behavioral M\textbf{O}deling approach. It employs a progressive decomposition strategy to guide large language models in incrementally parsing requirement structures, deconstructing behavioral dependencies, and ultimately generating executable UML activity diagrams. Comprehensive evaluations on four open-source datasets and two real-world industrial systems show that LATO comprehensively outperforms state-of-the-art baselines in accuracy, completeness, and syntactic compliance: $F_1$ scores for behavioral node-extraction improve by up to 71.1%, relation-extraction $F_1$ by 52.4 % relatively, and syntactic pass rates remain above 96.67%. The framework also exhibits strong robustness to input perturbations, confirming its cross-domain generalizability. This paper is the first to tightly fuse human-inspired strategies with LLMs in behavior modeling, yielding an intelligent infrastructure that exhibits expert-level logical understanding and generalization. By closing the modeling-skills gap, LATO delivers a next-generation, low-cost, and explainable solution for requirements engineering and AI-native software development.