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MLAIRE is a multilingual language-aware information retrieval evaluation protocol that separates semantic retrieval accuracy from query-language preference to better assess retrieval utility across mixed-language corpora.
Researchers identify systematic English and query-language bias in multilingual RAG rerankers and introduce LAURA, a utility-driven alignment method that boosts performance by retrieving answer-critical documents across languages.
UL-XCoT introduces a unified logic space to prune low-quality multilingual reasoning paths, cutting >50% token cost while improving accuracy and robustness on low-resource languages.
Researchers introduce x1, a family of reasoning models that adaptively select optimal languages for reasoning on a per-instance basis, demonstrating that language choice impacts reasoning quality in multilingual and cultural tasks.