Personalized Diversity: Adaptive Modulation of Ideation Diversity Based on Task Genetics

Research Question: Can AI research agents dynamically adjust their ideation diversity to maximize performance based on the “genetics” (complexity, ambiguity, novelty) of each research task?

Hypothesis: Adaptive tuning of ideation diversity—using real-time feedback—will outperform static diversity configurations by tailoring exploration to the specific demands of each problem.

Experiment Plan: - Methodology: Implement a meta-learning controller within the agent that adjusts diversity parameters (e.g., search breadth, model ensemble heterogeneity) based on continuous performance evaluation.

• Data/Materials: MLE-bench, plus a suite of tasks with varying characteristics (ill-defined, well-defined, high/low novelty).
• Measurements: Comparative performance across static vs. adaptive diversity agents, time-to-solution, and dynamic diversity trajectories.
• Expected Outcomes: Demonstration that adaptive agents consistently outperform fixed-diversity peers, especially on complex or novel tasks.

References:

• Audran-Reiss, A., et al. (2025). What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity.
• Catta-Preta, M., Trejo Omeñaca, A., Ferrer i Picó, J., & Monguet-Fierro, J. M. (2025). Innovation Flow: A Human–AI Collaborative Framework for Managing Innovation with Generative Artificial Intelligence. Applied Sciences.