@inproceedings{chen-etal-2026-ideation,
title = "Ideation Arena: Evaluating {LLM}-driven Research Ideation with Battle-style Human Expert Annotation",
author = "Chen, Zhiyu and
Zhao, Keyu and
Fu, Jigao and
Liang, Dong and
Wu, Yanbiao and
Li, Jiaoyang and
Xue, Haidong and
Zeng, Xinhua and
Zhen, Yuanyi and
Xu, Fengli and
Li, Yong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.688/",
pages = "15825--15849",
ISBN = "979-8-89176-459-0",
abstract = "Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single reference answer specifies what counts as a good idea. To address this challenge, we introduce Ideation Arena, a battle style platform that evaluates research ideas through pairwise human assessment. Ideation Arena evaluates ideas generated by 14 frontier LLMs and 5 research agent architectures built on 2 base models. To ensure a common starting point, Ideation Arena builds shared literature contexts from papers familiar to the participating researchers and provides the same contexts to all LLMs and agents. We collect over 6,000 double blind pairwise comparisons from 105 active computer science researchers and construct an Elo rating leaderboard for research ideation. We validate the rankings through interrater agreement and robustness analyses, showing that the leaderboard remains stable under changes in annotator composition and domain coverage. Our results show substantial variation in agent effectiveness, with some frameworks improving ideation quality over their backbones and others offering little benefit or even underperforming their base models. We further construct Ideation Arena Eval, a benchmark for assessing whether automated evaluators align with human preferences in research ideation. Experiments with current LLM judges show that they remain far from reliable human level assessment, with the best model reaching only 72.56{\%} agreement on overall quality. Our code, data, and leaderboards are available at \url{https://anonymous.4open.science/r/Ideation-Arena-2C21}."
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<abstract>Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single reference answer specifies what counts as a good idea. To address this challenge, we introduce Ideation Arena, a battle style platform that evaluates research ideas through pairwise human assessment. Ideation Arena evaluates ideas generated by 14 frontier LLMs and 5 research agent architectures built on 2 base models. To ensure a common starting point, Ideation Arena builds shared literature contexts from papers familiar to the participating researchers and provides the same contexts to all LLMs and agents. We collect over 6,000 double blind pairwise comparisons from 105 active computer science researchers and construct an Elo rating leaderboard for research ideation. We validate the rankings through interrater agreement and robustness analyses, showing that the leaderboard remains stable under changes in annotator composition and domain coverage. Our results show substantial variation in agent effectiveness, with some frameworks improving ideation quality over their backbones and others offering little benefit or even underperforming their base models. We further construct Ideation Arena Eval, a benchmark for assessing whether automated evaluators align with human preferences in research ideation. Experiments with current LLM judges show that they remain far from reliable human level assessment, with the best model reaching only 72.56% agreement on overall quality. Our code, data, and leaderboards are available at https://anonymous.4open.science/r/Ideation-Arena-2C21.</abstract>
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%0 Conference Proceedings
%T Ideation Arena: Evaluating LLM-driven Research Ideation with Battle-style Human Expert Annotation
%A Chen, Zhiyu
%A Zhao, Keyu
%A Fu, Jigao
%A Liang, Dong
%A Wu, Yanbiao
%A Li, Jiaoyang
%A Xue, Haidong
%A Zeng, Xinhua
%A Zhen, Yuanyi
%A Xu, Fengli
%A Li, Yong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-ideation
%X Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single reference answer specifies what counts as a good idea. To address this challenge, we introduce Ideation Arena, a battle style platform that evaluates research ideas through pairwise human assessment. Ideation Arena evaluates ideas generated by 14 frontier LLMs and 5 research agent architectures built on 2 base models. To ensure a common starting point, Ideation Arena builds shared literature contexts from papers familiar to the participating researchers and provides the same contexts to all LLMs and agents. We collect over 6,000 double blind pairwise comparisons from 105 active computer science researchers and construct an Elo rating leaderboard for research ideation. We validate the rankings through interrater agreement and robustness analyses, showing that the leaderboard remains stable under changes in annotator composition and domain coverage. Our results show substantial variation in agent effectiveness, with some frameworks improving ideation quality over their backbones and others offering little benefit or even underperforming their base models. We further construct Ideation Arena Eval, a benchmark for assessing whether automated evaluators align with human preferences in research ideation. Experiments with current LLM judges show that they remain far from reliable human level assessment, with the best model reaching only 72.56% agreement on overall quality. Our code, data, and leaderboards are available at https://anonymous.4open.science/r/Ideation-Arena-2C21.
%U https://aclanthology.org/2026.emnlp-main.688/
%P 15825-15849
Markdown (Informal)
[Ideation Arena: Evaluating LLM-driven Research Ideation with Battle-style Human Expert Annotation](https://aclanthology.org/2026.emnlp-main.688/) (Chen et al., EMNLP 2026)
ACL
- Zhiyu Chen, Keyu Zhao, Jigao Fu, Dong Liang, Yanbiao Wu, Jiaoyang Li, Haidong Xue, Xinhua Zeng, Yuanyi Zhen, Fengli Xu, and Yong Li. 2026. Ideation Arena: Evaluating LLM-driven Research Ideation with Battle-style Human Expert Annotation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15825–15849, Budapest, Hungary. Association for Computational Linguistics.