Only 21.23% of machine learning papers include their code, creating a massive reproducibility bottleneck for researchers. PaperCoder changes this with an AI framework that automatically converts research papers into fully functional code repositories.
Comparison between naive direct generation and PaperCoder’s structured three-stage approach.
Planning Stage: Creating the Blueprint
Research papers contain substantial information not directly relevant to implementation. The planning stage distills the paper into structured components essential for code development:
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