What Makes Good Multilingual Reasoning? Disentangling Traces with Measurable Features
Quick summary
arXiv:2604.04720v2 Announce Type: replace-cross Abstract: 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 a
Key takeaways
- arXiv:2604.04720v2 Announce Type: replace-cross Abstract: 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 a
Why it matters
“What Makes Good Multilingual Reasoning? Disentangling Traces with Measurable Features” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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