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Optimizing Agent Planning: A Parametric AI Approach to World Knowledge
Large Language Models (LLMs) have shown promise in physical world planning tasks, but often fail to understand the real world, leading to trial-and-error behavior. Inspired by human planning, our team developed a World Knowledge Model (WKM) that enhances agent planning by providing task and state knowledge.
Practical Solutions and Value
The WKM synthesizes task knowledge and maintains state knowledge, guiding agents and preventing hallucinatory actions during planning. It has demonstrated superior performance and generalization compared to existing models, reducing trial-and-error and improving planning efficiency.
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