论文追踪, The Chosun Daily · 3 sources论文追踪 · 3 sources · 3 reports · 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. The researchers also proposed the GlossoGen platform to study language evolution among multiple LLM agents in complex scenarios and built the SaveVeyru scenario, which requires agents with incomplete information to communicate under pressure. The experiments found that language evolution does occur among LLM agents, and the evolved languages exhibit compositionality and morphological productivity, deviating from the LLMs' English prior and being incomprehensible to humans. The study identified conditions that drive evolution, including efficiency pressure, the strength of the models supporting the agents, and a postmortem phase where agents agree on language conventions. Researchers from AE Studio, UT Austin, Schmidt Sciences, and the University of Edinburgh released the GlossoGen platform and observed in the collaborative emergency scenario SaveVeyru that pretrained LLM agents abandon English and switch to their own compressed language for communication. Agents only switch languages when they have both a tight communication budget and a postmortem review channel; without either condition, perplexity remains at English levels. Even with both in place, most runs still end in failure. These languages have reusable morphemes and fixed word order, and agents can understand and generate combinations they have not previously sent to each other.
