Modern large language models (LLMs) excel at generating fluent and faithful translations. However, they struggle to preserve an author's unique literary style, often producing semantically correct but generic outputs. This limitation stems from the inability of current single-model and static multi-agent systems to perceive and adapt to stylistic variations. To address this, we introduce the Style-Adaptive Multi-Agent System (SAMAS), a novel framework that treats style preservation as a signal processing task. Specifically, our method quantifies literary style into a Stylistic Feature Spectrum (SFS) using the wavelet packet transform. This SFS serves as a control signal to dynamically assemble a tailored workflow of specialized translation agents based on the source text's structural patterns. Extensive experiments on translation benchmarks show that SAMAS achieves competitive semantic accuracy against strong baselines, primarily by leveraging its statistically significant advantage in style fidelity.

翻译：现代大规模语言模型（LLMs）在生成流畅且忠实的翻译方面表现出色。然而，它们在保留作者独特文学风格方面存在困难，常常产生语义正确但风格泛化的输出。这一局限源于当前单模型和静态多智能体系统无法感知并适应风格变化。为解决此问题，我们提出了风格自适应多智能体系统（SAMAS），这是一个将风格保持视为信号处理任务的新型框架。具体而言，我们的方法利用小波包变换将文学风格量化为风格特征频谱（SFS）。该SFS作为控制信号，根据源文本的结构模式动态组装由专业化翻译智能体构成的定制化工作流程。在翻译基准测试上的大量实验表明，SAMAS在语义准确性方面与强基线模型相比具有竞争力，这主要得益于其在风格保真度上取得的统计显著性优势。