PaperBench: Evaluating AI's Ability to Replicate AI Research
Authors: name: Giulio Starace, name: Oliver Jaffe, name: Dane Sherburn, name: James Aung, name: Jun Shern Chan, name: Leon Maksin, name: Rachel Dias, name: Evan Mays, name: Benjamin Kinsella, name: Wyatt Thompson, name: Johannes Heidecke, name: Amelia Glaese, name: Tejal Patwardhan

We introduce PaperBench, a benchmark evaluating the ability of AI agents to
replicate state-of-the-art AI research. Agents must replicate 20 ICML 2024
Spotlight and Oral papers from scratch, including understanding paper
contributions, developing a codebase, and successfully executing experiments.
For objective evaluation, we develop rubrics that hierarchically decompose each
replication task into smaller sub-tasks with clear grading criteria. In total,
PaperBench contains 8,316 individually gradable tasks. Rubrics are co-developed
with the author(s) of each ICML paper for accuracy and realism. To enable
scalable evaluation, we also develop an LLM-based judge to automatically grade
replication attempts against rubrics, and assess our judge's performance by
creating a separate benchmark for judges. We evaluate several frontier models
on PaperBench, finding that the best-performing tested agent, Claude 3.5 Sonnet
(New) with open-source scaffolding, achieves an average replication score of
21.0\%. Finally, we recruit top ML PhDs to attempt a subset of PaperBench,
finding that models do not yet outperform the human baseline. We
https://github.com/openai/preparedness{open-source our code} to
facilitate future research in understanding the AI engineering capabilities of
AI agents.