用信号处理思路提升翻译风格保真度，让机器译文更像原作者笔调。
SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation
- 将文学风格转为频谱信号，动态组合专用翻译代理
- 在风格保真度上显著优于主流模型，语义准确率仍保持领先
- 适合追求文风还原的文学翻译与创意写作场景
现代大语言模型虽能生成流畅且语义正确的译文，但难以保留作者独特的文学风格，常产出通用化表达。这一局限源于现有单模型及静态多智能体系统无法感知和适应风格差异。为此，我们提出风格自适应多智能体系统（SAMAS），将风格保持视为信号处理问题。具体而言，通过小波包变换将文学风格量化为风格特征谱（SFS），作为控制信号，动态构建适配源文本结构模式的专用翻译代理流程。大规模实验表明，SAMAS在翻译基准测试中达到与强基线相当的语义准确性，主要凭借其在风格保真度上的统计显著优势。
原文摘要 · Abstract (English)
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.
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