x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)
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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language. |
| Approach: | They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. |
| Outcome: | The proposed model can reason in a single dominant language on a per-instance basis. |
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