arxiv:2610.01257

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems

Published on Oct 1

· Submitted by

Yiqiao Jin on Oct 5

Georgia Institute of Technology

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Authors:

Lucheng Fu ,

Abstract

Scientific progress emerges from a longitudinal ecosystem in which researchers, institutions, funding agencies, collaboration networks, and the scientific literature co-evolve. As AI becomes increasingly involved throughout the scientific research cycle, understanding these interconnected and evolving processes becomes increasingly important. We introduce SciUtopia, a persistent, closed-loop LLM-agent simulation framework for studying academic research ecosystems. SciUtopia models interconnected scientific processes such as research-direction choice, collaboration, submission, peer review, resubmission, citation, funding, and researcher attrition, while maintaining evolving states across simulated years. Its configurable institutional mechanisms and information channels provide a controlled testbed for matched counterfactual experiments and targeted interventions. Across 61 simulation worlds, SciUtopia simulates over 40,000 researchers from 8,000 institutions, producing around 400,000 publication decisions and 1.2 million LLM-generated peer reviews. Using these longitudinal simulations, we find that rejection-driven resubmission substantially amplifies reviewer burden beyond population growth alone, cautious exploration balances citation impact with career success and long-term topic diversity, and resource inequality can emerge even without detectable cumulative advantage from narrowly winning early funding. Code is available at https://github.com/Ahren09/ScienceUtopia.

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Ahren09

Paper submitter about 20 hours ago

Top conferences now have over 30,000 submissions. What's next?

AI is accelerating research. How can our institutions keep pace? 🔬

As the research community grows, peer review, funding, and career incentives help shape which ideas advance—and who can continue pursuing them.

Our new paper, “Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems,” introduces SUTO: a persistent, large-scale simulation of a scientific community, where thousands of AI researchers navigate evolving institutions, collaboration networks, and a growing body of literature.

Research choices, peer review, funding, and career trajectories unfold together over simulated decades—with each decision shaping the opportunities and constraints agents encounter next. This creates a testbed for studying how individual behaviors and institutional rules interact to produce long-term, system-wide outcomes.

Across 61 simulated worlds with 40,000+ researcher agents, we found some interesting patterns:

📈 Allowing more submissions increased publication output, but also reduced the share of researchers who remained active over time—raising questions about how to support both productivity and sustained participation.
🧭 Cautious exploration beyond existing expertise offered a promising balance between citation impact and career success, while helping sustain topic diversity.
⚖️ Narrowly winning an early grant did not necessarily translate into a lasting funding edge.

As AI expands what individual researchers can do, we hope SUTO can help us ask what a thriving scientific community needs.

Which rule or incentive in academia would you test first?