research
REFLEX framework gives LLMs metacognitive reasoning for zero-shot robot planning
Researchers present REFLEX, a framework that equips LLM-powered robotic agents with metacognitive capabilities—skill decomposition, failure reflection, and solution synthesis—to perform complex tasks in zero-shot and few-shot settings. The system significantly outperforms existing baselines and demonstrates that LLMs can generate creative solutions that diverge from ground truth while still completing tasks successfully.