Which Language Should a Skeleton Speak? Language Choices in Multilingual Reasoning
HyeonSeok Lim, SeungWoo Song, Inho Won, Hoyun Song, Jihyo Kim, KyungTae Lim
ETRI · KAIST · Dankook University
Published October 7, 2026 · on the feed October 12, 2026 · ▲ 3 · arXiv 2610.09607
The abstract
Skeleton-based reasoning prompting is a promising training-free approach for structuring LLM reasoning, but prior work largely assumes an English-centric setting. We propose the Language-Aware Skeleton Exploration Framework (LASEF) to study skeleton-language choice in multilingual mathematical reasoning. Across math benchmarks, model scales, and languages, we show that English skeletons yield a small positive tendency on average, most visible for smaller models and low-resource languages. However, few language-level gains remain significant after correction, and English is not universally optimal. Combining greedy decoding, multi-rollout evaluation, translation ablation, and cross-benchmark validation, we further find three patterns of skeleton-language effects: directionally consistent, evaluation- and benchmark-dependent, and asymmetric negative. These effects cannot be fully explained by generation quality alone. Overall, skeleton language is a context-dependent design variable that requires multi-level exploration. All resources are released at https://github.com/lhsstn/LASEF.
The Brief
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Transcript
Which Language Should a Skeleton Speak? Language Choices in Multilingual Reasoning. By HyeonSeok Lim and five others, from the Electronics and Telecommunications Research Institute, KAIST, and one other institution. Published on the seventh of October. Its code repository is public but slight: six source files.
Skeleton-based reasoning prompting structures how an LLM reasons, and needs no training. Prior work has mostly assumed English throughout. This study asks what happens when the language of the skeleton is a choice, in multilingual mathematical reasoning. Its answer: skeleton language is a context-dependent design variable, and English is not universally the best pick. That is a more cautious answer than the question invites. It would be tidy to find that one language always wins, whatever the model, the benchmark or the language of the problem. That is not what the study finds.
The authors build the Language-Aware Skeleton Exploration Framework, LASEF, and run it across math benchmarks, model scales and languages. English skeletons show a small positive tendency on average, most visible for smaller models and low-resource languages. But after correction, few language-level gains remain significant.
To look past the average, the authors combine greedy decoding, multi-rollout evaluation, translation ablation and cross-benchmark validation. Three patterns of effect emerge. Some are directionally consistent. Some depend on the evaluation and the benchmark. And some are asymmetric and negative. The same choice of skeleton language, in other words, can help in one setting, flip in another, and hurt in a third.
The setting is mathematical reasoning, and the average English advantage is small and mostly not significant at the level of single languages. The effects also cannot be fully explained by generation quality alone, so the study does not reduce them to one language simply writing better text than another.
Rather than a default to switch, the skeleton's language becomes something to explore at several levels before trusting. All resources are released.
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