✨ReCode: Unify Plan and Action for Universal Granularity Control
📝 Summary:
ReCode unifies planning and action in LLM agents via recursive code generation. It treats plans as abstract functions recursively decomposed into primitive actions, enabling dynamic decision granularity. This significantly improves performance and data efficiency.
🔹 Publication Date: Published on Oct 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23564
• PDF: https://arxiv.org/pdf/2510.23564
• Github: https://github.com/FoundationAgents/ReCode
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #AI #CodeGeneration #Planning #GranularityControl
📝 Summary:
ReCode unifies planning and action in LLM agents via recursive code generation. It treats plans as abstract functions recursively decomposed into primitive actions, enabling dynamic decision granularity. This significantly improves performance and data efficiency.
🔹 Publication Date: Published on Oct 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23564
• PDF: https://arxiv.org/pdf/2510.23564
• Github: https://github.com/FoundationAgents/ReCode
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMAgents #AI #CodeGeneration #Planning #GranularityControl