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

arXiv: https://arxiv.org/abs/2602.19840 · HTML: https://mlsanity.com/paper/2602.19840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 26 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 22 of 22)

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- [HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation](https://mlsanity.com/paper/2505.16281/) (2025)
- [Team "better_call_claude": Style Change Detection using a Sequential Sentence Pair Classifier](https://mlsanity.com/paper/2508.00675/) (2025)

## Abstract (arXiv)

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.