@inproceedings{liu-etal-2026-reasoning-help,
title = "When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of {LRM} Reasoning Traces",
author = "Liu, Yuxiang and
Luo, Jiaming and
Briakou, Eleftheria and
Cherry, Colin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2499/",
pages = "56747--56771",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces
%A Liu, Yuxiang
%A Luo, Jiaming
%A Briakou, Eleftheria
%A Cherry, Colin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-reasoning-help
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2499/
%P 56747-56771
Markdown (Informal)
[When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces](https://aclanthology.org/2026.emnlp-main.2499/) (Liu et al., EMNLP 2026)
ACL