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Proposes the Neuro-Symbolic Lexical Discovery (NSLD) framework where LLM-based agents autonomously develop shared vocabularies for unknown visual referents in unknown environments, enabling pre-deployment planning for autonomous exploration missions.
This paper identifies autonomous exploration as a critical capability for LLM agents and proposes the Explore-then-Act paradigm, which decouples information gathering from task execution to improve adaptability and real-world performance. It also introduces Exploration Checkpoint Coverage as a verifiable metric for evaluating exploration breadth.