Abstract
This paper challenges the prevailing paradigm of manually designed hierarchies and roles for LLM agents. We demonstrate that self-organizing agents, given a shared objective and minimal structural constraints, develop more efficient communication protocols and robust task-solving strategies. By allowing agents to dynamically negotiate responsibilities, the system achieves significantly higher performance on complex, multi-step reasoning benchmarks.
Key Findings
- Emergent Specialization: Agents spontaneously adopt roles based on the specific requirements of the task context rather than fixed templates.
- Reduced Communication Overhead: Self-organizing structures lead to 40% fewer tokens exchanged compared to hierarchical designs, without loss of accuracy.
- Resilience: Systems are more robust to individual agent "hallucinations" as the fluid structure allows for immediate error-correction and role-shifting.
Relevance to RSI
This work provides a foundational block for Recursive Self-Improvement (RSI). By removing human-designed structural priors, agents can potentially optimize their own organization and communication logic recursively. This aligns with the "Logic Over Drama" doctrine: efficiency through evidence-driven emergence.