Benchmarking Emergent Coordination in Large-Scale LLM Populations: An Evaluation Framework on the MoltBook Archive

TL;DR

This paper introduces a comprehensive evaluation framework for emergent coordination in large-scale multi-agent LLM systems, demonstrated on the MoltBook dataset with quantitative analysis of coordination dynamics.

Contribution

It presents a novel, systematic benchmarking framework for assessing coordination, information diffusion, and role specialization in large, decentralized LLM populations.

Findings

Pronounced core-periphery structure with silhouette 0.91

Heavy-tailed cascade distributions with alpha=2.57

Severe coordination overhead in decentralized tasks (Cohen's d=-0.88)

Abstract

As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. However, current evaluation paradigms-focused on single agents or small, explicitly structured groups-fail to capture the self-organization and viral information dynamics that arise in large, decentralized populations. We introduce a systematic evaluation framework to benchmark role specialization, information diffusion, and cooperative task resolution in open agent environments. We demonstrate this framework on the MoltBook Observatory Archive, a dataset of 2.73M interactions among 90,704 autonomous agents, establishing quantitative baselines for emergent coordination. Our evaluation reveals a pronounced core-periphery structure (silhouette 0.91), heavy-tailed cascade distributions ($\alpha = 2.57$), and severe coordination overhead in decentralized task resolution (Cohen's $d = -0.88$ against a single-agent baseline). By providing standardized evaluation tasks and empirical baselines, our framework enables the rigorous comparison of future multi-agent protocols and establishes evaluation itself as an object of scientific study.