Multi-agentMT: Deploying AI Agent in the WMT25 Shared Task

AbstractWe present Multi-agentMT, our system for the WMT25 General Shared Task. The model adopts Prompt Chaining, a multi-agent workflow combined with Rubric-MQM, an automatic MQM-based error annotation metric. Our primary submission follows a Translate–Postedit–Proofread pipeline, in which error positions are explicitly marked and iteratively refined. Results suggest that a semi-autonomous agent scheme for machine translation is feasible with a smaller, earlier-generation model in low-resource settings, achieving comparable quality at roughly half the cost of larger systems.