<ul data-eligibleForWebStory="true"><li>Large Language Models (LLMs) often require guidance to perform well in complex environments.</li><li>A new framework has been introduced to enhance LLM agent planning through in-context learning.</li><li>The framework uses atomic fact augmentation and lookahead search to improve planning capabilities.</li><li>The agent extracts task-critical 'atomic facts' from interaction trajectories.</li><li>These facts augment prompts for LLM-based components for better decision-making.</li><li>Planning involves a depth-limited lookahead search guided by accumulated facts and history.</li><li>The approach helps the agent improve understanding and decision-making without weight updates.</li><li>Theoretical motivation links performance to fact-based abstraction and LLM simulation accuracy.</li><li>Empirically, the agent shows improved performance and adaptability on interactive tasks like TextFrozenLake and ALFWorld.</li></ul>

Improving LLM Agent Planning with In-Context Learning via Atomic Fact Augmentation and Lookahead Search

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