Self-Organizing LLM Agents
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How much autonomy can multi-agent LLM systems sustain? This research tests the question at unprecedented scale: 25,000 tasks across 8 models, up to 256 agents, and 8 coordination protocols ranging from externally imposed hierarchy to emergent self-organization. The central finding is that agents allowed to figure out their own roles consistently outperform systems with pre-assigned structures.
Autonomous protocols beat centralized coordination: A hybrid sequential protocol that enables autonomy outperforms centralized coordination by 14% (p<0.001), with a 44% quality spread between the best and worst protocols. The result holds across both open-source and closed-source models, with open-source achieving 95% of closed-source quality at 24x lower cost.
Emergent role specialization: From just 8 initial agents, the system produces 5,006 unique emergent roles. Rather than collapsing into generic behaviors, agents spontaneously specialize and form shallow hierarchies that adapt to task demands without any external role assignment.
Model capability gates self-organization: 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. This suggests that self-organizing multi-agent architectures will become increasingly viable as base models improve.
Sub-linear scaling to 256 agents: The system scales to 256 agents without quality degradation (p=0.61). This sub-linear scaling property means that adding more agents does not introduce the coordination overhead that typically limits multi-agent systems, at least under the tested protocols.
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