Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts
In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in knowledge transfer is primarily a script barrier. First, we conduct an observational data analysis on the performance of thinking models on two datasets with local knowledge from around the world, ECLeKTic and MultiLoKo. Our regression analysis shows that script match - not language or family - is the primary predictor of knowledge transfer failure once model capability and question difficulty are accounted for. We further this finding by providing the LLMs with the key entities of the questions in their source language and find that this disp...
| Authors | Lucas Bandarkar, Alan Ansell, Trevor Cohn |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 28 August 2026 |
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