🤖 AI Summary
This study investigates whether large language model–based multi-agent systems can achieve efficient autonomous collaboration without predefined roles or hierarchical structures. Through extensive experiments involving 25,000 tasks across system scales ranging from 4 to 256 agents, the authors evaluate the impact of eight coordination protocols—including fixed-order execution, centralized control, and self-organization—on task performance. The results demonstrate that minimal structural scaffolding suffices for agents to spontaneously develop role specialization and shallow hierarchies. Notably, the proposed Sequential protocol outperforms centralized approaches by 14%, with the largest performance gap between protocols reaching 44%. The system scales effectively to 256 agents without performance degradation, and open-source models achieve 95% of the performance of closed-source counterparts at only 1/24th of the computational cost.
📝 Abstract
How much autonomy can multi-agent LLM systems sustain -- and what enables it? We present a 25,000-task computational experiment spanning 8 models, 4--256 agents, and 8 coordination protocols ranging from externally imposed hierarchy to emergent self-organization. We observe that autonomous behavior already emerges in current LLM agents: given minimal structural scaffolding (fixed ordering), agents spontaneously invent specialized roles, voluntarily abstain from tasks outside their competence, and form shallow hierarchies -- without any pre-assigned roles or external design. A hybrid protocol (Sequential) that enables this autonomy outperforms centralized coordination by 14% (p<0.001), with a 44% quality spread between protocols (Cohen's d=1.86, p<0.0001). The degree of emergent autonomy scales with model capability: strong models self-organize effectively, while models below a capability threshold still benefit from rigid structure -- suggesting that as foundation models improve, the scope for autonomous coordination will expand. The system scales sub-linearly to 256 agents without quality degradation (p=0.61), producing 5,006 unique roles from just 8 agents. Results replicate across closed- and open-source models, with open-source achieving 95% of closed-source quality at 24x lower cost. The practical implication: give agents a mission, a protocol, and a capable model -- not a pre-assigned role.