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Controlled experiments evaluate translation quality factors in role-based AI systems, suggesting effective design principles.
This paper explores the engineering principles of role-based AI translation systems. A configurable experimental framework was developed based on a review of existing multi-agent translation architectures to evaluate role allocation, model selection, glossary integration, and review strategies. Controlled experiments have identified the key factors that influence translation quality, and have formulated practical design principles for the development of efficient, domain-specific Role-Based AI translation systems.
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Petrov et al. (2026) studied this question.
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