Digital Red Queen: Adversarial Program Evolution in Core War with LLMs
- Published
- Source
- arXiv
- Paper number
- 101
- Field
- Security / Code
- arXiv ID
- 2601.03335
Key points
- Understanding open-ended adversarial evolution, known as Red Queen dynamics, in controlled artificial systems is a difficult problem.
- Efficiently navigating an extremely sparse search space for programs that operate in complex adversarial environments is a difficult problem.
- The challenge is to develop a simple yet effective self-play mechanism that induces continuous adaptation and counter-adaptation in digital ecosystems.
- Digital Red Queen (DRQ) is a minimal self-play mechanism in which each round evolves a new program to optimize against all previously generated adversaries.
- It uses Core War as a sandboxed Turing-complete environment for evolving self-modifying Redcode programs, providing a safe testbed for complex adversarial dynamics.
- It integrates a large language model such as GPT-4.1 mini as an intelligent mutation and generation operator inside the MAP-Elites quality-diversity algorithm to guide program synthesis and preserve diversity.
Paper links
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