# New LLM Coordination Benchmark: Measuring Language Model Coordination How do 13 modern language models coordinate in open-ended tasks? - **Source:** [r/MachineLearning](https://www.reddit.com/r/MachineLearning) - **Published:** 2026-07-14 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/2f465e0c-3956-4350-898d-85c0f31d7de1 ## LLMs' Coordination Ability A new benchmark examines how language models coordinate in long-term, open-ended tasks. This study measures the performance of 13 modern language models in tasks like exploration, communication, resource trading, and structure building. ## Performance in Challenging Tasks Research results show that most language models struggle with such tasks, achieving an average normalized return of ~6%. However, in the hardest setting, Gemini 3.1 Pro performs comparably to a MARL agent trained for 1 billion steps. ## The Importance of Coordination Coordination emerges as a distinct bottleneck beyond long-term task competence. Communication is identified as the most impactful factor in this process. ## Research Details The study provides a comprehensive benchmark to understand how language models behave in open-ended worlds. This could guide future language model developments. ## Results and Leaderboard The research results and leaderboard are shared on the project page and GitHub. This allows researchers and developers to review the findings. ## Future Implications This benchmark could be a significant step in improving language models' coordination abilities. Performance in open-ended tasks may shape the design of future AI systems. ## Sources covering this story - https://www.reddit.com/r/MachineLearning/comments/1uwc6ni/new_llm_coordination_benchmark_benchmarking/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._