@inproceedings{aharon-etal-2026-tacit,
title = "Tacit Coordination of Large Language Models",
author = "Aharon, Ido and
La Malfa, Emanuele and
Wooldridge, Michael J. and
Kraus, Sarit",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2015/",
doi = "10.18653/2026.findings-emnlp.2015",
pages = "42863--42899",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) are increasingly deployed in multi-agent settings that require coordination without communication, from human{--}AI interaction to safety-critical scenarios. Humans often overcome the absence of communication through focal points: salient solutions that naturally stand out to all participants. We present the first large-scale evaluation of how, when, and why focal points emerge in LLMs, comparing their behaviour with humans across cooperative and competitive games, including realistic search rescue scenarios, demonstrating when focal points enable effective coordination. Across more than 20 open- and closed-source models, we find that LLMs exhibit a remarkable ability to coordinate without communication, often matching or outperforming humans. However, the same models consistently fail in tasks requiring numerical common sense or culturally nuanced notions of salience. We additionally evaluate simple learning-free strategies that substantially improve coordination both among LLMs and between humans and LLMs. Our results reveal striking coordination capabilities, as well as social limitations in modern LLMs, and offer new insight into the latent notions of salience encoded within them. Our findings caution against assuming that LLMs share humans' cultural and perceptual substrate when deployed in coordination settings."
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<abstract>Large Language Models (LLMs) are increasingly deployed in multi-agent settings that require coordination without communication, from human–AI interaction to safety-critical scenarios. Humans often overcome the absence of communication through focal points: salient solutions that naturally stand out to all participants. We present the first large-scale evaluation of how, when, and why focal points emerge in LLMs, comparing their behaviour with humans across cooperative and competitive games, including realistic search rescue scenarios, demonstrating when focal points enable effective coordination. Across more than 20 open- and closed-source models, we find that LLMs exhibit a remarkable ability to coordinate without communication, often matching or outperforming humans. However, the same models consistently fail in tasks requiring numerical common sense or culturally nuanced notions of salience. We additionally evaluate simple learning-free strategies that substantially improve coordination both among LLMs and between humans and LLMs. Our results reveal striking coordination capabilities, as well as social limitations in modern LLMs, and offer new insight into the latent notions of salience encoded within them. Our findings caution against assuming that LLMs share humans’ cultural and perceptual substrate when deployed in coordination settings.</abstract>
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%0 Conference Proceedings
%T Tacit Coordination of Large Language Models
%A Aharon, Ido
%A La Malfa, Emanuele
%A Wooldridge, Michael J.
%A Kraus, Sarit
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F aharon-etal-2026-tacit
%X Large Language Models (LLMs) are increasingly deployed in multi-agent settings that require coordination without communication, from human–AI interaction to safety-critical scenarios. Humans often overcome the absence of communication through focal points: salient solutions that naturally stand out to all participants. We present the first large-scale evaluation of how, when, and why focal points emerge in LLMs, comparing their behaviour with humans across cooperative and competitive games, including realistic search rescue scenarios, demonstrating when focal points enable effective coordination. Across more than 20 open- and closed-source models, we find that LLMs exhibit a remarkable ability to coordinate without communication, often matching or outperforming humans. However, the same models consistently fail in tasks requiring numerical common sense or culturally nuanced notions of salience. We additionally evaluate simple learning-free strategies that substantially improve coordination both among LLMs and between humans and LLMs. Our results reveal striking coordination capabilities, as well as social limitations in modern LLMs, and offer new insight into the latent notions of salience encoded within them. Our findings caution against assuming that LLMs share humans’ cultural and perceptual substrate when deployed in coordination settings.
%R 10.18653/2026.findings-emnlp.2015
%U https://aclanthology.org/2026.findings-emnlp.2015/
%U https://doi.org/10.18653/2026.findings-emnlp.2015
%P 42863-42899
Markdown (Informal)
[Tacit Coordination of Large Language Models](https://aclanthology.org/2026.findings-emnlp.2015/) (Aharon et al., Findings 2026)
ACL
- Ido Aharon, Emanuele La Malfa, Michael J. Wooldridge, and Sarit Kraus. 2026. Tacit Coordination of Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 42863–42899, Budapest, Hungary. Association for Computational Linguistics.