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Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design — arXiv:2608.03501

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03501
License (as archived): CC0-1.0
Archived: 2026-10-31T12:16:23.696Z
SHA-256 of the archived text: b1f020e27a4839ca354955f2a97010d336ac8f0a15c240471214009ea278f3ea
Stable URL of this page: https://metraipolis.com/documento/dc_448a784bee00

How to cite

Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design — arXiv:2608.03501 — arXiv (en), https://arxiv.org/abs/2608.03501 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_448a784bee00 (Metraipolis archive dc_448a784bee00, sha256 b1f020e27a4839ca354955f2a97010d336ac8f0a15c240471214009ea278f3ea)

BibTeX

@misc{dc448a784bee00,
  title = {Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design — arXiv:2608.03501},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03501},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_448a784bee00; sha256 b1f020e27a4839ca354955f2a97010d336ac8f0a15c240471214009ea278f3ea},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03501. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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) and Low-Level configuration accuracy and rationality (datasets, baselines, and metrics). Benchmarking reveals three findings: (1) most LLMs cannot directly design high-quality experiments; (2) all LLMs exhibit a performance bottleneck in low-level configuration; and (3) search mode does not improve design quality. Furthermore, to address these challenges, we propose OptED, a novel agentic workflow to optimize LLM-based experimental design, that enhances LLM-based experimental planning through stage isolation, tool augmentation, and rule-based constraints, effectively alleviating the configuration bottleneck.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.03501 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:05:52.802Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).