What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, and the key factors driving the success or failure of agent trajectories are not fully understood. We examine the role that ideation diversity plays in agent performance. First, we analyse agent trajectories on MLE-bench, a well-known benchmark to evaluate AI research agents, across different models and agent scaffolds. Our analysis reveals that different models and agent scaffolds yield varying degrees of ideation diversity, and that higher-performing agents tend to have increased ideation diversity. Further, we run a controlled experiment where we modify the degree of ideation diversity, demonstrating that higher ideation diversity results in stronger performance. Finally, we strengthen our results by examining additional evaluation metrics beyond the standard medal-based scoring of MLE-bench, showing that our findings still hold across other agent performance metrics.
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Alexis Audran-Reiss
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2025-11-21T09:01:47.713Z
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Alexis Audran-Reiss
aaudranreiss
user
aaudranreiss
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Jordi Armengol Estapé
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Karen Hambardzumyan
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Amar Budhiraja
claimed_verified
2025-11-21T09:01:45.720Z
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Amar Budhiraja
ambud26
user
ambud26
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Martin Josifoski
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Edan Toledo
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Rishi Hazra
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Despoina Magka
claimed_verified
2026-04-13T19:26:37.466Z
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Despoina Magka
marlamagka
user
marlamagka
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Michael Shvartsman
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Parth Pathak
claimed_verified
2026-02-11T11:21:36.372Z
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Pathak
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user
papatha
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Justine T Kao
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Lucia Cipolina-Kun
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Bhavul Gauri
claimed_verified
2025-11-21T09:01:43.883Z
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Bhavul Gauri
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user
bhavul
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Jean-Christophe Gagnon-Audet
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Emanuel Tewolde
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Jenny Zhang
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Taco Cohen
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Yossi Adi
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Tatiana Shavrina
691efc453c64d32b03645a40
Yoram Bachrach
What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, and the key factors driving the success or failure of agent trajectories are not fully understood. We examine the role that ideation diversity plays in agent performance. First, we analyse agent trajectories on MLE-bench, a well-known benchmark to evaluate AI research agents, across different models and agent scaffolds. Our analysis reveals that different models and agent scaffolds yield varying degrees of ideation diversity, and that higher-performing agents tend to have increased ideation diversity. Further, we run a controlled experiment where we modify the degree of ideation diversity, demonstrating that higher ideation diversity results in stronger performance. Finally, we strengthen our results by examining additional evaluation metrics beyond the standard medal-based scoring of MLE-bench, showing that our findings still hold across other agent performance metrics.
Ideation diversity significantly enhances the performance of AI research agents across various models and scaffolds on the MLE-bench benchmark.
ideation diversity AI research agents MLE-bench agent trajectories agent scaffolds