# Automated Table Reproduction via Code Generation

arXiv: https://arxiv.org/abs/2602.10046 · HTML: https://mlsanity.com/paper/2602.10046/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [Writing Bug Reports for Software Repair Agents: What Information Matters Most?](https://mlsanity.com/paper/2607.09553/) (2026)

## Abstract (arXiv)

Reproducibility is an important goal in computer science research, e.g., for artifact evaluation and to build upon experimental results of prior work. Recently, LLM agents are being used to automatically reproduce research results, but they fail to provide executable evidence of reproduction and do not consider the method of reproduction, which limits their usefulness. We present Artisan, an LLM agent that reproduces tables of numeric results, given a paper and its artifact. The approach is enabled by two key contributions: First, we frame the reproduction problem as a code generation task, enabling users to audit and re-run the resulting reproduction script independently of the agent. Second, we design automated judging mechanisms that steer the agent toward correct results without exposing them, while preventing shortcuts like copying precomputed results. To evaluate Artisan, we introduce Artisan-Bench, the first benchmark assessing the ability to generate code that reproduces research results. Artisan-Bench comprises 60 tasks derived from 23 software engineering papers. Our experiments show that Artisan is effective and efficient, with the added benefit of aiding the discovery of 20 new errors in either the paper or artifact.