Forcing agents to talk to each other in English costs Hindi users 30.6 points of exact-match accuracy

arXiv·medium signalXBlueskyLinkedInCopy link

A September 14 arXiv paper (EMNLP 2026 agent language models workshop) measures the cost of the common design where a multi-agent pipeline converts a non-English request to English for inter-agent communication and back-translates the answer. Testing a two-agent extraction-answer system on Aya-23-8B across Hindi, Chinese, Spanish and Arabic with 300 samples per language, English-forced routing lost 13.0 exact-match points for Spanish and 30.6 for Hindi versus a native-language pipeline. chrF overlap with English references correlated with failures, pointing at translation loss, and the authors argue for native-language routing in agent frameworks especially when source and target are typologically distant.