arXiv:2601.22184v2 Announce Type: replace-cross 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 and rescue scenario
The increasing deployment of LLMs in multi-agent systems necessitates understanding how they coordinate, especially without explicit communication, a challenge that becomes more pressing as these systems grow in complexity and autonomy.
Understanding tacit coordination in LLMs is crucial for developing robust, safe, and efficient AI systems, particularly in contexts like human-AI interaction and safety-critical operations where explicit communication is impossible or undesirable.
This research provides the first large-scale evaluation of focal point emergence in LLMs, comparing their behavior to humans in cooperative and competitive games, thereby advancing our understanding of inter-agent AI behavior.
- · AI developers
- · Multi-agent system designers
- · Research institutions
- · Systems reliant on explicit messaging
- · AI agents unable to adapt to implicit cues
Improved design principles for multi-agent LLM systems that can coordinate effectively without direct communication.
Accelerated development of autonomous AI agents capable of nuanced, human-like cooperation and competition.
Enhanced trust and reliability in AI systems deployed in complex, uncontrolled environments, potentially reducing the need for constant human oversight.
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Read at arXiv cs.LG