CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark
Abstract: 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 \texttt{CORE-Bench} (\textbf{Co}mputational \textbf{Re}producibility Agent Benchmark), a benchmark consisting of 270 tasks based on 90 scientific papers across three disciplines (computer science, social science, and medicine). Tasks in \texttt{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 \texttt{AutoGPT} and a task-specific agent called \texttt{CORE-Agent}. We tested both variants using two underlying language models: \texttt{GPT-4o} and \texttt{GPT-4o-mini}. The best agent achieved an accuracy of 21\% on the hardest level of tasks, 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 \texttt{CORE-Bench} can improve the state of reproducibility and spur the development of future research agents.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We updated the manuscript in response to reviewer feedback. In particular, we:
- Updated the header to Section 4.1 since AutoGPT does slightly better on Medium than Easy difficulty level.
- Updated wording about how the code being from public repositories does not fully mitigate contamination concerns.
- Added a qualitative analysis of agent failures on CORE-Bench-Hard.
- Added a table indicating the amount of time it takes for each task to complete by agent-model pair to the appendix.
- Updated the Introduction, Section 2.1, and Conclusion to be more clear about the scope of our benchmark and why we don't include irreproducible papers.
- Added more examples of task questions to the paper appendix.
- Added the proportion of visual and written questions by difficulty level to the appendix.
- Added the proportion of R/Python capsules by discipline in the appendix.
- Updated the supplementary code to provide a cheap baseline to evaluate our harness.
Assigned Action Editor: ~Yonatan_Bisk1
Submission Number: 3380
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