Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching
arXiv:2606.10582v1 Announce Type: new Abstract: Generalization in emergent communication has largely focused on novel inputs or linguistic structures, yet the capacity for agents to communicate with strangers from strictly disjoint communities remains relatively unexplored. In this work, we formalize this capability as \textit{zero-shot mutual intelligibility (ZMI)}: successful communication between independently trained populations without prior exposure. Leveraging emergent sketching -- in which agents communicate through sets of drawn strokes -- as a visually grounded modality, we find that
Emergent communication research is rapidly advancing, and the focus is naturally expanding to more complex and realistic scenarios like zero-shot interaction between independently trained AI populations.
Achieving zero-shot mutual intelligibility (ZMI) is a critical step towards robust, general AI communication systems that do not require extensive pre-coordination or paired training, essential for practical multi-agent deployment.
This research suggests a pathway for AI systems to communicate effectively even when developed by different entities with no prior shared training, pointing towards truly interoperable AI agents.
- · AI agents developers
- · Open-source AI communities
- · Multi-agent system integrators
- · Proprietary AI communication protocols (if ZMI becomes widespread)
AI systems from diverse origins could more readily collaborate and share information.
This could accelerate the development of complex emergent behaviors in large-scale AI ecosystems, potentially leading to new forms of collective intelligence.
The ability for 'stranger' AIs to communicate could reduce the need for monolithic AI infrastructures, fostering a more decentralized and resilient AI landscape.
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Read at arXiv cs.LG