🤖 AI Summary
This work addresses the often-overlooked optimization potential in existing published algorithms, where manual refinement is typically costly and inefficient. The authors propose a two-stage AI-assisted pipeline: first, a research-capable large language model identifies recently published algorithms that meet predefined experimental criteria; second, a Claude Code agent automatically reproduces baseline implementations and iteratively optimizes the code. This study presents the first systematic application of embodied coding agents to automate performance improvements across diverse domains of published algorithms, while underscoring the indispensable human role in defining objectives, validating outcomes, and ensuring ethical transparency. Evaluated on eleven cross-domain tasks, the approach consistently achieves performance gains, with each optimization cycle completed within a single day.
📝 Abstract
We present a two-stage pipeline for AI-assisted improvement of published algorithm implementations. In the first stage, a large language model with research capabilities identifies recently published algorithms satisfying explicit experimental criteria. In the second stage, Claude Code is given a prompt to reproduce the reported baseline and then iterate an improvement process. We apply this pipeline to published algorithm implementations spanning multiple research domains. Claude Code reported that all eleven experiments yielded improvements. Each improvement could be achieved within a single working day. We analyse the human contributions that remain indispensable, including selecting the target, verifying experimental validity, assessing novelty and impact, providing computational resources, and writing with appropriate AI-use disclosure. Finally, we discuss implications for peer review and academic publishing.