When building multilingual AI systems with multiple translation steps, preserve the original user input throughout the pipeline instead of discarding it after each stage—this simple change significantly improves reasoning quality across languages.
This paper shows that translation cascades for multilingual reasoning lose important context at each step. By keeping the original question, translated question, and reasoning trace available to the final translation step, the authors achieve better results across 285 languages without retraining—a simple fix that prevents information loss in multi-stage pipelines.