Applying an Agentic Coding Tool for Improving Published Algorithm Implementations

TL;DR

This paper introduces a two-stage AI-assisted pipeline using large language models to improve published algorithm implementations across research domains, demonstrating consistent, rapid enhancements.

Contribution

The paper presents a novel two-stage pipeline leveraging large language models for improving published algorithms, highlighting human-AI collaboration and implications for peer review.

Findings

All eleven experiments showed improvements.

Each improvement was achieved within a single working day.

Human contributions remain essential for target selection and validation.

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.