CORE-Bench: Fostering the Credibility of Published Research Through a
  Computational Reproducibility Agent Benchmark
Authors: name: Zachary S. Siegel, name: Sayash Kapoor, name: Nitya Nagdir, name: Benedikt Stroebl, name: Arvind Narayanan

AI agents have the potential to aid users on a variety of consequential
tasks, including conducting scientific research. To spur the development of
useful agents, we need benchmarks that are challenging, but more crucially,
directly correspond to real-world tasks of interest. This paper introduces such
a benchmark, designed to measure the accuracy of AI agents in tackling a
crucial yet surprisingly challenging aspect of scientific research:
computational reproducibility. This task, fundamental to the scientific
process, involves reproducing the results of a study using the provided code
and data. We introduce CORE-Bench (Computational Reproducibility Agent
Benchmark), a benchmark consisting of 270 tasks based on 90 scientific papers
across three disciplines (computer science, social science, and medicine).
Tasks in CORE-Bench consist of three difficulty levels and include both
language-only and vision-language tasks. We provide an evaluation system to
measure the accuracy of agents in a fast and parallelizable way, saving days of
evaluation time for each run compared to a sequential implementation. We
evaluated two baseline agents: the general-purpose AutoGPT and a task-specific
agent called CORE-Agent. We tested both variants using two underlying language
models: GPT-4o and GPT-4o-mini. The best agent achieved an accuracy of 21% on
the hardest task, showing the vast scope for improvement in automating routine
scientific tasks. Having agents that can reproduce existing work is a necessary
step towards building agents that can conduct novel research and could verify
and improve the performance of other research agents. We hope that CORE-Bench
can improve the state of reproducibility and spur the development of future
research agents.