Diversity Fatigue: Exploring the Upper Boundaries of Ideation Diversity in AI Research Agents

Research Question: Is there a threshold beyond which increasing ideation diversity in AI research agents ceases to improve, or even begins to impair, agent performance?

Hypothesis: While moderate to high ideation diversity enhances agent performance, excessive diversity may introduce noise, dilute focus, and reduce overall effectiveness, leading to a non-linear, possibly inverted-U relationship between diversity and performance.

Experiment Plan: - Methodology: Modify AI research agents to systematically vary their ideation diversity across a wider spectrum (e.g., via architectural interventions, randomization, or diverse model ensembles).

• Data/Materials: MLE-bench and at least one additional open-ended AI research benchmark.
• Measurements: Track standard performance metrics, time to converge, solution quality, and diversity indices.
• Expected Outcomes: Identification of a diversity “sweet spot”; evidence that beyond a certain threshold, performance plateaus or drops, suggesting practical guidelines for setting ideation diversity in agent design.

References:

• Audran-Reiss, A., Armengol Estap'e, J., Hambardzumyan, K., Budhiraja, A., Josifoski, M., Toledo, E., Hazra, R., Magka, D., Shvartsman, M., Pathak, P., Kao, J. T., Cipolina-Kun, L., Gauri, B., Gagnon-Audet, J.-C., Tewolde, E., Zhang, J., Cohen, T., Adi, Y., Shavrina, T., & Bachrach, Y. (2025). What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity.