Digital Red Queen: Adversarial Program Evolution in Core War with LLMs

Large language models (LLMs) are increasingly used to evolve solutions to problems. However, most LLM-evolution frameworks solve static optimization problems, overlooking the open-ended adversarial dynamics that characterize real-world evolutionary processes. Here, we study Digital Red Queen (DRQ), a simple self-play algorithm that embraces these "Red Queen" dynamics via a changing objective. DRQ uses an LLM to evolve assembly programs (warriors) which compete for control of a virtual machine in the game of Core War, a Turing-complete environment studied in artificial life and connected to cybersecurity. In each round of DRQ, the model evolves a new warrior to defeat all previous ones. Over many rounds, warriors become increasingly general (relative to a set of held-out human warriors). Interestingly, across independent runs, we observe a convergence pressure toward a single generalpurpose behavioral strategy, much like convergent evolution in nature. Our work positions Core War as a rich, controllable sandbox for studying adversarial adaptation in artificial systems and for evaluating LLM-based evolution methods. More broadly, the simplicity and effectiveness of DRQ suggest that similarly minimal self-play approaches could prove useful in practical multi-agent adversarial domains, like real-world cybersecurity or combating drug resistance.