Generation efficiency without sacrificing code quality in scientific coding agents

Determine strategies to increase generation efficiency without compromising code quality in scientific coding agents that implement and reproduce entire codebases from high-level ideas and academic papers, ensuring that improvements in speed or resource usage do not degrade functional correctness, maintainability, or fidelity of implementations.

Background

The paper distinguishes scientific coding agents from general-purpose coding tools, noting that these agents aim to generate and reproduce full codebases based on high-level concepts and academic papers. Examples include Paper2Code for transforming machine learning papers into executable repositories, CodeScientist for generating experimental code through iterative execution, and AlphaEvolve for algorithmic discovery via codebase mutations.

Despite advances, the authors explicitly state that achieving higher generation efficiency while preserving code quality remains unresolved. This highlights a tension between the speed and computational cost of repository synthesis and the reliability and maintainability of generated code—an issue central to scaling autonomous scientific reproduction.