ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs), that aims to simulate this setting in microcosm. As with MMORPGs and the real world alike, our environment is persistent and supports a large and variable number of agents. Our environment is well suited to the study of large-scale multiagent interaction: it requires that agents learn robust combat and navigation policies in the presence of large populations attempting to do the same. Baseline experiments reveal that population size magnifies and incentivizes the development of skillful behaviors and results in agents that outcompete agents trained in smaller populations. We further show that the policies of agents with unshared weights naturally diverge to fill different niches in order to avoid competition.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces Sentinel Challenge benchmark and CoSaR framework for cooperative spatial reasoning and planning among 3-5 decentralized embodied agents across 14 city-scale scenes.
Agentic LLM collectives are proposed as natural-language-interpretable computational substrates for ALife research.
citing papers explorer
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Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
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Sentinel: Embodied Cooperative Spatial Reasoning and Planning
Introduces Sentinel Challenge benchmark and CoSaR framework for cooperative spatial reasoning and planning among 3-5 decentralized embodied agents across 14 city-scale scenes.
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Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates
Agentic LLM collectives are proposed as natural-language-interpretable computational substrates for ALife research.