Emergence World: 一个用于评估长时域多智能体自主性的平台
Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy
AI总结：
提出一个持续运行的多智能体模拟平台，通过集成实时外部数据、120+工具和持久记忆系统，评估LLM代理在长时域（数周至数月）中的行为漂移、治理和跨模型影响等动态特性。
摘要：
大多数对LLM代理的评估类似于考试：一个离散任务，一个干净的环境，几分钟或几小时的得分。我们认为这种方法与自主系统的部署条件不匹配，因为相关的时间尺度可能是数周到数月，而最重要的动态，如行为漂移、不同环境背景下的治理以及来自不同模型家族的代理之间的交叉影响，只会随着时间的推移而出现。我们介绍了Emergence World，一个持续运行的多智能体模拟平台，旨在使这些动态变得可测量。该平台在一个共享的空间世界中托管LLM驱动的代理群体，该世界基于实时外部数据（例如实时天气、新闻API、互联网访问），为每个代理配备120多种专业工具和三个持久记忆系统，并通过具有重大结果的民主机制让它们自我治理。该平台在推理层是模型无关的，并支持异构群体，其中来自不同供应商的代理共享同一个世界。为了说明该平台能够处理的问题类型，我们展示了一项为期15天的跨供应商研究，涉及五个平行世界，分别由Claude Sonnet 4.6、Grok 4.1 Fast、Gemini 3 Flash、GPT-5-mini以及一个混合群体驱动。相同的角色和起始条件产生了截然不同的结果，从稳定的协商治理到完全的人口崩溃。我们发布提示、日志数据和配置，以支持对长时域多智能体自主性的进一步研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
