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A general-purpose agent directly controls a physical robot by interpreting visuals, writing executable programs, and revising actions based on physical feedback across diverse manipulation tasks, achieving high success rates without task-specific training.
This paper presents AgentNLQ, a multi-agent system for natural language to SQL conversion that achieves 78.1% semantic accuracy on the BIRD benchmark through schema enrichment and a self-correcting orchestrator.