This research explores Digital Red Queen (DRQ), a self-play algorithm that uses large language models to evolve assembly programs for the game Core War. In this competitive environment, digital "warriors" battle for control of a virtual machine by attempting to crash their opponents' processes. The DRQ framework moves beyond static optimization by forcing models to continually adapt against a growing history of previous champions, mimicking the evolutionary arms races found in biological systems. Results demonstrate that this adversarial process incentivizes the emergence of robust, generalist strategies that can defeat diverse human-designed opponents. Interestingly, independent runs of the algorithm show phenotypic convergence, where different programs independently evolve toward similar effective behaviors. This work positions Core War as a safe, Turing-complete sandbox for studying open-ended evolution and the potential for LLMs to navigate complex, adversarial domains like cybersecurity.

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