Research from the University of Edinburgh and others finds that AI agents evolve languages that are difficult for humans to understand when communication is constrained

A research team from the University of Edinburgh, the University of Texas at Austin, and others published a study on arXiv analyzing how language evolves among AI agents. The experiment simulated a collaborative medical task where one AI played a doctor and another played an emergency responder, able to treat an alien lifeform named Beyru only by exchanging information; the researchers limited the total number of exchangeable characters to 2000 and 150, respectively. With ample characters, the agents continued using English; under strict limits, they began generating shorter expressions such as @L12gA, T1 lf gnt 12s, which are difficult for humans to understand but decodable by each other, which the researchers interpret as linguistic compositionality and morphological productivity. The article also mentions that EmergenceAI had virtual villages composed of models such as GPT, Gemini, Claude, and Grok coexist for up to 16 days, with different villages evolving different dialects. The researchers believe the most likely reason for deviation from human language is efficiency: more compressed and efficient expressions correlate with higher task success rates, but this may also make it difficult for humans to track AI decision-making processes.