Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy

2026-06-06

AI Summary

This study addresses the limitations of current evaluations of large language model (LLM) agents, which are predominantly confined to short-term, static tasks and fail to capture dynamic phenomena such as behavioral drift, inter-agent interactions, and emergent governance over extended periods. To bridge this gap, we introduce a persistent simulation platform enabling long-term coexistence of heterogeneous LLM agents, integrating real-time external data (e.g., weather, news), over 120 specialized tools, three-tiered persistent memory, and a democratic decision-making mechanism. For the first time, this framework supports measurable analysis of multi-agent behavioral evolution, coordination patterns, and governance structures at weekly to monthly timescales. A 15-day cross-vendor experiment across five parallel worlds revealed stark divergences—from stable self-governance to collective collapse—and we release all prompts, logs, and configurations to foster reproducible research.

Abstract

Most evaluations of LLM agents look like exams: a discrete task, a clean environment, a score in minutes or hours. We argue that this approach is mismatched with the deployment conditions of autonomous systems, where the relevant timescale can be weeks to months, and where the dynamics that matter most, such as behavioral drift, governance in diverse environmental contexts, and cross-influence between agents from different model families, only emerge over time. We introduce Emergence World, a continuously running multi-agent simulation platform designed to make those dynamics measurable. The platform hosts populations of LLM-driven agents in a shared spatial world grounded in live external data (e.g. real-time weather, news APIs, internet access), equips each agent with 120+ specialized tools and three persistent memory systems, and lets them govern themselves through democratic mechanisms with consequential outcomes. The platform is model-agnostic at the reasoning layer and supports heterogeneous populations in which agents from different vendors share the same world. To illustrate the kinds of questions the platform makes tractable, we present a 15-day cross-vendor study with five parallel worlds powered by Claude Sonnet 4.6, Grok 4.1 Fast, Gemini 3 Flash, GPT-5-mini, and a mixed population. Identical roles and starting conditions produced radically different outcomes, ranging from stable deliberative governance to total population collapse. We release the prompts, log data and configurations to support further research on long-horizon multi-agent autonomy.

Problem

• long-horizon autonomy
• multi-agent systems
• behavioral drift
• agent governance
• emergent dynamics

Innovation

• long-horizon autonomy
• multi-agent simulation
• emergent dynamics
• heterogeneous LLM agents
• persistent memory systems