SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

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 st

Read original source ↗ Open in workspace

recordType

paper

region

Global