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This paper proposes an Edge-AI-driven decentralized task allocation framework for circular smart manufacturing that uses learning-to-rank to align with the ordering-based nature of winner selection. Simulation results show improved delay, deadline adherence, and energy efficiency under high-load and tight-deadline scenarios.