When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces

2026-09-17

AI Summary

研究分析了大型推理模型在机器翻译中的推理轨迹，通过引入层次元摘要框架揭示了最佳推理语言、长度和结构模式，并建议应以模型、长度和模式感知的方式控制机器翻译推理。

Abstract

Large Reasoning Models increasingly use intermediate traces for machine translation, but it remains unclear when such reasoning helps or hurts. We analyze reasoning traces across models, languages, domains, and datasets, focusing on reasoning language, length, and structure. We find that the best reasoning language is model-specific, reasoning length has a non-monotonic relationship with quality, and traces exhibit recurring functional patterns. To uncover these patterns, we introduce Hierarchical Meta-Summarization (HMS), a scalable framework that induces coarse- and fine-grained reasoning structures without predefined taxonomies. HMS reveals a shared organization--understanding/planning, translating/drafting, and refining/verifying--alongside domain-specific variation. Our results suggest that MT reasoning should be controlled in a model-aware, length-aware, and pattern-aware manner rather than uniformly encouraged.

Problem

• Reasoning
• Machine Translation
• Traces
• Hierarchical Analysis
• Model-aware

Innovation

• Hierarchical Meta-Summarization
• Reasoning Traces
• Machine Translation