✨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

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✓ https://t.iss.one/DataScienceT

#LLMAgents #AI #CodeGeneration #Planning #GranularityControl

✨Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

📝 Summary:
PaperCoder is a multi-agent LLM framework that automates converting machine learning papers into functional code repositories. It uses planning, analysis, and generation stages with specialized agents. Evaluations show it effectively creates high-quality implementations, outperforming strong base...

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.17192
• PDF: https://arxiv.org/pdf/2504.17192
• Project Page: https://huggingface.co/papers/2504.15080
• Github: https://github.com/going-doer/Paper2Code

✨ Datasets citing this paper:
• https://huggingface.co/datasets/iaminju/paper2code

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#CodeGeneration #MachineLearning #LLM #AI #Automation