# Tacit Coordination of Large Language Models

arXiv: https://arxiv.org/abs/2601.22184 · HTML: https://mlsanity.com/paper/2601.22184/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 63 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [More Agents Is All You Need](https://mlsanity.com/paper/2402.05120/) (2024)
- [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models](https://mlsanity.com/paper/2102.04130/) (2021)
- [Theory of Mind for Multi-Agent Collaboration via Large Language Models](https://mlsanity.com/paper/2310.10701/) (2023)
- [Embodied LLM Agents Learn to Cooperate in Organized Teams](https://mlsanity.com/paper/2403.12482/) (2024)
- [LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models](https://mlsanity.com/paper/2310.03903/) (2023)
- [Cultural Incongruencies in Artificial Intelligence](https://mlsanity.com/paper/2211.13069/) (2022)
- [Frontier LLMs Still Struggle with Simple Reasoning Tasks](https://mlsanity.com/paper/2507.07313/) (2025)
- [Cognitive Effects in Large Language Models](https://mlsanity.com/paper/2308.14337/) (2023)
- [Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting](https://mlsanity.com/paper/2506.07142/) (2025)
- [Benchmarking LLMs' Swarm intelligence](https://mlsanity.com/paper/2505.04364/) (2025)
- [What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure](https://mlsanity.com/paper/2504.12187/) (2025)
- [Out-of-Context Reasoning in Large Language Models](https://mlsanity.com/paper/2503.10408/) (2025)

## Built on by (most cited first, 5 of 5)

- [Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems](https://mlsanity.com/paper/2606.28425/) (2026)
- [A Note on the Strategic Confinement Problem](https://mlsanity.com/paper/2606.09931/) (2026)
- [Despite Instructions: Frontier Agents Improvise Covert Channels at Test Time](https://mlsanity.com/paper/2609.32701/) (2026)
- [Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination](https://mlsanity.com/paper/2609.32939/) (2026)
- [Shared and structured inputs undermine collective random choice by reasoning AI agents](https://mlsanity.com/paper/2610.09667/) (2026)

## Abstract (arXiv)

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 and 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.