AIMay 30, 2025

EXP-Bench: Can AI Conduct AI Research Experiments?

arXiv:2505.24785v220 citationsh-index: 13Has Code
Originality Incremental advance
AI Analysis

This addresses the challenge of accelerating AI research by providing a tool to systematically assess and improve AI agents' experimental capabilities, though it is incremental as it builds on existing benchmarking approaches.

The paper tackles the problem of automating AI research by introducing EXP-Bench, a benchmark with 461 tasks from 51 top-tier papers that evaluates AI agents on end-to-end experimentation; results show leading agents achieve only 0.5% success rate for complete experiments, with partial aspect scores up to 35%.

Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. We introduce EXP-Bench, a novel benchmark designed to systematically evaluate AI agents on complete research experiments sourced from influential AI publications. Given a research question and incomplete starter code, EXP-Bench challenges AI agents to formulate hypotheses, design and implement experimental procedures, execute them, and analyze results. To enable the creation of such intricate and authentic tasks with high-fidelity, we design a semi-autonomous pipeline to extract and structure crucial experimental details from these research papers and their associated open-source code. With the pipeline, EXP-Bench curated 461 AI research tasks from 51 top-tier AI research papers. Evaluations of leading LLM-based agents, such as OpenHands and IterativeAgent on EXP-Bench demonstrate partial capabilities: while scores on individual experimental aspects such as design or implementation correctness occasionally reach 20-35%, the success rate for complete, executable experiments was a mere 0.5%. By identifying these bottlenecks and providing realistic step-by-step experiment procedures, EXP-Bench serves as a vital tool for future AI agents to improve their ability to conduct AI research experiments. EXP-Bench is open-sourced at https://github.com/Just-Curieous/Curie/tree/main/benchmark/exp_bench.

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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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