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SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

Jingzhuo Wu, Jiajun Zhang, Keyan Jin, Dehua Ma, Junbo Wang

TL;DR

The Style-Adaptive Multi-Agent System (SAMAS) is introduced, a novel framework that treats style preservation as a signal processing task and quantifies literary style into a Stylistic Feature Spectrum (SFS) using the wavelet packet transform.

Abstract

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.

SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

TL;DR

The Style-Adaptive Multi-Agent System (SAMAS) is introduced, a novel framework that treats style preservation as a signal processing task and quantifies literary style into a Stylistic Feature Spectrum (SFS) using the wavelet packet transform.

Abstract

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.
Paper Structure (11 sections, 2 figures, 1 table)

This paper contains 11 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Stylistic Feature Spectrum
  4. SFS-Driven Dynamic Multi-Agent System
  5. System Architecture
  6. Dynamic Workflow Allocation
  7. Experiments
  8. Experiments Setup
  9. Main Results
  10. Style Analysis
  11. Conclusion

Figures (2)

  • Figure 1: Overview of the SAMAS. The system first quantifies a text's literary style into a Stylistic Feature Spectrum (SFS) signal. This signal then guides the dynamic allocation of a workflow, which is executed by a pool of specialized agents to produce a stylistically faithful translation that outperforms a single-LLM baseline.
  • Figure 2: Human preference for style fidelity in head-to-head comparisons. In all evaluations, translations generated by SAMAS were consistently favored by human evaluators over those from five different strong baseline models.