Exploring the Topology and Memory of Consensus: How LLM Agents Agree, Fragment, or Settle When Forming Conventions
arXiv:2606.04197v1 Announce Type: cross Abstract: How much should an LLM agent remember, and how should multi-agent systems be connected when trying to reach consensus? We show these two design choices interact in a way that flips the sign of memory's effect on coordination. Across 432 simulation runs of a networked Naming Game on eight fixed 16-agent topologies, we vary memory depth and network structure. Longer memory slows the time to reach steady state in decentralized networks but accelerates it in centralized ones; the same parameter pushes the system in opposite directions depending on
The paper provides timely insights into optimal architectures for multi-agent LLM systems, which are rapidly evolving and becoming a focal point of AI development.
Understanding how AI agents reach consensus and how memory and network structure influence this process is critical for building robust, scalable, and reliable autonomous systems.
This research provides fundamental principles for designing more effective multi-agent LLM systems, potentially leading to faster and more stable convention formation.
- · AI developers
- · AI agents researchers
- · Large Language Models (LLMs)
- · Organizations deploying multi-agent systems
- · Inefficient multi-agent system designs
Improved performance and stability in multi-agent LLM systems through optimised memory and network configurations.
Accelerated development and adoption of AI agents across various industries as their reliability increases.
Enhanced societal impact of AI through more coherent and effective autonomous systems capable of complex coordination tasks.
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