[2603.28990] Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures

Computer Science > Artificial Intelligence arXiv:2603.28990 (cs) [Submitted on 30 Mar 2026] Title:Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures Authors:Victoria Dochkina View a PDF of the paper titled Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures, by Victoria Dochkina View PDF HTML (experimental) 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...