What Makes Good Multilingual Reasoning? Disentangling Traces with Measurable Features
Large Reasoning Models (LRMs) still exhibit large performance gaps between English and other languages, yet much current work assumes these gaps can be closed simply by making reasoning in every language resemble English reasoning. This work challenges this assumption by asking instead: what actually characterizes successful reasoning traces in multilingual settings, and to what extent do English-derived reasoning features genuinely help in other languages? We first define a suite of measurable reasoning features spanning multilingual alignment, reasoning step, and reasoning flow aspects of reasoning traces, and use logistic regression to quantify how each feature associates with final an...
| Authors | Dayeon Ki, Kevin Duh, Marine Carpuat |
|---|---|
| Primary category | cs.CL |
| Published | 6 April 2026 |
| Last updated at source | 20 September 2026 |
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