Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design. To bridge this gap, we propose SCOPE, a Scientific COmprehensive Planning Evaluation Benchmark constructed from 300 high-quality latest papers across 19 research domains from top-tier venues (e.g., ICML, NeurIPS, and ICLR),evaluating LLMs on two dimensions: High-Level planning completeness (main, ablation, and analysis experiments) ...
| Authors | Zejun Liu, Jian Wu, Ru Peng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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