A Society of Researchers: Designing Institutions for Populations of Autonomous Research Agents
cs.MA, cs.AI
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: 4 pages. Short version of A Society of Researchers: Institutional Design for Populations of Autonomous Scientific Agents, doi:10.5281/zenodo.22922325
License: http://creativecommons.org/licenses/by/4.0/
The gist: Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave the population unorganized.
Abstract
Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave the population unorganized. We argue that such a population will acquire an organization whether or not its designers provide one, so designers should provide it explicitly, and that the multi-agent systems community holds the tools to do so. We propose a society of agents, a population of persistent agents under explicit institutions, and develop it for science as a society of researchers built on six principles. Principal investigators compete for compute through requests for proposals, independent review, and grants; a human governor, the mayor, allocates resources and assigns no tasks. In a running society of ten thousand researchers, asked only to improve the pretraining of language models, one lab reported a way to reach the same quality with about 30% less compute, a result the labs that tested it do not yet agree on. We close with six open problems for the agents community.
Sources
- Concrete Problems in AI Safety
- Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
- Exploring the use of AI authors and reviewers at Agents4Science
- Demonstrating specification gaming in reasoning models
- Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs
- AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
- Algorithmic Collusion by Large Language Models
- Accelerating scientific discovery with Co-Scientist
- Population Based Training of Neural Networks
- Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
- Towards a Science of Scaling Agent Systems
- Large Language Models Miss the Multi-Agent Mark
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
- Hypothesis Hunting with Evolving Networks of Autonomous Scientific Agents
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- Frontier Models are Capable of In-context Scheming
- AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
- AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power
- AgentRxiv: Towards Collaborative Autonomous Research
- Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
Related papers
- Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control
- ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents
- You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents
- Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems
- MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization
- PeroMAS: A Multi-agent System of Perovskite Material Discovery