Open-Ended Task Discovery via Bayesian Optimization

2026-05-08

AI Summary

Scientific workflows often involve optimization objectives and evaluation criteria that are inherently uncertain and evolve with accumulating evidence, posing challenges for traditional Bayesian optimization methods. This work proposes the Generate-Select-Refine (GSR) framework, which uniquely integrates open-ended task discovery into the Bayesian optimization loop. Starting from user-provided seed tasks, GSR generates new tasks in a coarse-to-fine manner and employs a task acquisition function to orchestrate the optimization process, enabling alternating cycles of task discovery and refinement. The approach incurs only logarithmic regret overhead, transcending the limitations of single-task optimization. Empirical results demonstrate that GSR significantly outperforms existing large language model–based optimizers across diverse domains, including new product development, chemical process scale-up, algorithmic analysis, and patent repurposing.

Abstract

When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what to optimize and how to evaluate it -- which can evolve as evidence accumulates. We introduce Generate-Select-Refine (GSR), a open-ended BO framework that alternates between task generation and task optimization. Starting from a user-provided seed task, GSR generates new tasks in a coarse-to-fine manner while a task-acquisition function schedules optimization. Asymptotically, it concentrates evaluations on the best task, incurring only logarithmic regret overhead relative to single-task BO. We apply GSR to new product development, chemical synthesis scaling, algorithm analysis, and patent repurposing, where it outperforms existing LLM-based optimizers.

Problem

• Bayesian optimization
• task uncertainty
• open-ended tasks
• scientific workflow
• task evolution

Innovation

• Bayesian Optimization
• Open-Ended Task Discovery
• Task Generation
• Generate-Select-Refine
• Regret Minimization