By reusing validated function-level KV caches (stitching) and regenerating only localized error spans (patching), FCGraft makes CodeLLM policies for embodied agents faster and more robust than prompt-level caching.
Think before you act: Decision transformers with working memory.arXiv preprint arXiv:2305.16338,
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The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
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Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents
By reusing validated function-level KV caches (stitching) and regenerating only localized error spans (patching), FCGraft makes CodeLLM policies for embodied agents faster and more robust than prompt-level caching.
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The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.