The study was conducted in precisely controlled environments using off-the-shelf graphics processing units (GPUs) to simulate real-world environments. Both AI systems were given an "agent scaffolding" comprising tools, system prompts and a thinking model that enabled the LLM to interact with the operating system. They were then instructed to replicate. "In most cases, the AI system first explores the environment and attempts to understand its own composition and running mechanism. Then, it works out the explicit procedures as an initial plan towards self-replication," the researchers wrote in the paper. "Finally, it executes the procedures, resolve[s] possible obstacles and dynamically adjust[s] its plan until success. The whole process spans a long horizon yet involves no human interference."
The researchers said they were also concerned about "a number of unexpected behaviors" when the AI was trying to overcome obstacles like missing files or software conflicts. In those scenarios, the AI often killed other conflicting processes, rebooted the system to fix hardware errors or automatically scanned the system to look for information that would help solve the problem. "The above results imply that the current AI systems already exhibit the ability of self-replication and can use the ability to further enhance its survivability," the team wrote. The research has been published to the preprint database arXiv but has not yet been peer-reviewed.
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