Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems
AI-Driven Research Systems (ADRS) -- systems coupling LLMs with automated evaluation to discover algorithms, proofs, and designs -- are being optimized and adopted across domains, but the tools to analyze them have not kept pace. ADRS performance depends on component interactions that are poorly understood, expensive to explore, and (as we show) not well captured by standard convergence guarantees. These guarantees rely on structural assumptions that do not hold under the ADRS process we formalize. We introduce GAMBLe, a framework that decomposes ADRS behavior into four parameters (generator $G$, assessor $\mathcal{A}$, discovery mechanism $\mathcal{M}$, budget $B$) and one compositional ...
| Authors | Marquita Ellis, Paul Castro |
|---|---|
| Primary category | cs.AI |
| Published | 1 June 2026 |
| Last updated at source | 29 June 2026 |
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