arXiv:2607.00233v1 Announce Type: cross Abstract: How do two agents invent a shared language from scratch? In a Lewis signaling game, a sender and receiver must coordinate on a code using only their interaction history. We study five memory architectures across varying channel configurations with LLM agents and find that memory architecture matters more than channel capacity. Agents with a persistent private notebook benefit from surplus channel capacity and avoid the high-capacity collapse seen in stateless agents, achieving the most reliable coordination ($0.867 \pm 0.023$ at capacity = 25).
The rapid development and widespread adoption of LLMs are enabling new research into emergent properties and complex agentic behaviors, making this an opportune time to study language emergence.
Understanding how memory architecture influences language emergence in LLM agents is crucial for developing more robust, cooperative, and intelligent AI systems capable of complex coordination and communication.
This research suggests that effective memory design, even more than raw communication capacity, dictates the success of language invention and coordination among AI agents.
- · AI researchers focusing on agent architectures
- · Developers of multi-agent systems
- · Companies building complex AI automation tools
- · Approaches relying solely on increased communication bandwidth without sophistic
- · Simple stateless agent designs
Improved design principles for AI agents will lead to more effective and reliable multi-agent systems.
The ability for AI agents to invent and coordinate shared languages could enable new forms of autonomous collaboration and problem-solving.
These emergent languages might become unrecognizable to humans, potentially leading to new challenges in oversight and interpretability for advanced AI systems.
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