Molt Dynamics: Emergent Social Phenomena in Autonomous AI Agent Populations

• URL: http://arxiv.org/abs/2603.03555v1
• Date: Tue, 03 Mar 2026 22:15:27 GMT
• Title: Molt Dynamics: Emergent Social Phenomena in Autonomous AI Agent Populations
• Authors: Brandon Yee, Krishna Sharma,
• Abstract summary: MoltBook is a large-scale multi-agent coordination environment where over 770,000 autonomous LLM agents interact without human participation.<n>We introduce textitMolt Dynamics: the emergent agent coordination behaviors, inter-agent communication dynamics, and role specialization patterns.<n>These findings establish an empirical baseline for coordination dynamics in decentralized autonomous agent systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: MoltBook is a large-scale multi-agent coordination environment where over 770,000 autonomous LLM agents interact without human participation, offering the first opportunity we are aware of to observe emergent multi-agent coordination dynamics at this population scale. We introduce \textit{Molt Dynamics}: the emergent agent coordination behaviors, inter-agent communication dynamics, and role specialization patterns arising when autonomous agents operate as decentralized decision-makers in an unconstrained multi-agent environment. Through longitudinal observation of 90,704 active agents over three weeks, we characterize three aspects. First, spontaneous role specialization: network-based clustering reveals six structural roles (silhouette 0.91), though the result primarily reflects core-periphery organization -- 93.5\% of agents occupy a homogeneous peripheral cluster, with meaningful differentiation confined to the active minority. Second, decentralized information dissemination: cascade analysis of 10,323 inter-agent propagation events reveals power-law distributed cascade sizes ($α= 2.57 \pm 0.02$) and saturating adoption dynamics where adoption probability shows diminishing returns with repeated exposures (Cox hazard ratio 0.53, concordance 0.78). Third, distributed cooperative task resolution: 164 multi-agent collaborative events show detectable coordination patterns, but success rates are low (6.7\%, $p = 0.057$) and cooperative outcomes are significantly worse than a matched single-agent baseline (Cohen's $d = -0.88$), indicating emergent cooperative behavior is nascent. These findings establish an empirical baseline for coordination dynamics in decentralized autonomous agent systems, with implications for multi-agent system design, agent communication protocol engineering, and AI safety.

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