Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems
AI-Driven Research Systems (ADRS) couple LLMs with automated evaluation to discover algorithms, proofs, and designs. This paper introduces GAMBLe, a framework that decomposes ADRS behavior into four parameters (generator, assessor, discovery mechanism, budget) and the effective landscape. Experiments on 760+ runs reveal no total ordering of components; correct choices can improve performance by 13-67% and search efficiency by 6-39x.
[2606.02863] Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems
[Submitted on 1 Jun 2026]
Title:Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems
View a PDF of the paper titled Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems, by Marquita Ellis and 1 other authors
View PDF HTML (experimental)
Abstract: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 object, the effective landscape $L_{\text{eff}} = \mathcal{A} \circ G$, which reveals that distinct generator-assessor pairs induce structurally different per-problem optimization landscapes. We exercise the framework on 760+ replicated runs (>46,000 iterations) spanning generators from single LLMs to dynamically-adaptive ensembles, mechanisms from greedy selection to co-evolutionary meta-search, and three NP-hard problems whose assessors range from continuous scoring to cliff functions. The experiments reveal no total ordering of generators or mechanisms: frontier models can underperform open-source alternatives and the simplest mechanism sometimes outperforms state-of-the-art meta-search. Results show that even under limited budgets (60 iterations per run), the right component choices can improve performance by 13-67% and search efficiency by 6-39x.
Comments: Preprint. 21 pages (10 main, 11 appendix). 6 figures (2 in main, 4 in appendix)
Subjects:
Artificial Intelligence (cs.AI)
ACM classes: I.2.8; I.2.6; I.2.4; I.2.11; G.1.6; F.2.2
Cite as: arXiv:2606.02863 [cs.AI]
(or arXiv:2606.02863v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.02863
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Marquita Ellis [view email] [v1] Mon, 1 Jun 2026 20:26:28 UTC (178 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems, by Marquita Ellis and 1 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)