An LLM-driven gate between robot planning and execution labels plans accept, reject, or escalate, reporting 81 percent accuracy and no direct accept/reject errors on small test sets.
Self-Consistency Improves Chain of Thought Reasoning in Language Models,
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Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
An LLM-driven gate between robot planning and execution labels plans accept, reject, or escalate, reporting 81 percent accuracy and no direct accept/reject errors on small test sets.