SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
SMAC-Talk extends the StarCraft Multi-Agent Challenge with a natural language communication channel to evaluate LLM-based agents in cooperative multi-agent settings. It features decentralized control, partial observability, long-horizon decision making, and scenarios with deceptive communicators. Benchmarking using Qwen3.5 models reveals how reasoning, memory, and scale affect coordination.
[2606.04202] SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
[Submitted on 2 Jun 2026]
Title:SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
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Abstract:As LLMs become more widely deployed, they are increasingly expected to work alongside other AI agents rather than operating in isolation. Effective coordination in these settings requires agents to communicate, share information and make decisions under uncertainty. We introduce SMAC-Talk, a natural language extension of the StarCraft Multi-Agent Challenge for evaluating LLM-based agents in cooperative multi-agent environments. The environment has several key features such as decentralized control, partial observability and long-horizon decision making. SMAC-Talk includes a natural language communication channel which is used to probe agent coordination and trust. We use this communication channel to construct different evaluation scenarios, including settings with an embedded deceptive communicator that tries to disrupt and deceive allies through communication alone. We provide three agents for benchmarking using 4 models from the Qwen3.5 family and study how reasoning structure, memory and model scale affect coordination between agents. We release SMAC-Talk as an open benchmark to support the research community in developing and evaluating LLM agents in cooperative multi-agent settings.
Comments: 8 pages, 1 figure
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.04202 [cs.AI]
(or arXiv:2606.04202v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.04202
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Joel Sol [view email] [v1] Tue, 2 Jun 2026 20:40:04 UTC (866 KB)
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