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

2026-01-06

arXiv.org

AI Summary

This work proposes the Digital Red Queen (DRQ) algorithm, which introduces the Red Queen hypothesis into large language model (LLM)-driven program evolution for the first time. Unlike conventional approaches that rely on static objectives, DRQ simulates the dynamic co-evolutionary arms race observed in nature by embedding LLMs in a Core War environment. There, the model generates assembly-level adversarial programs (warriors) through self-play, with each new generation required to outperform all historical versions, thereby forming a continuously evolving adversarial sequence. This behavior-driven feedback loop enables automatic evolution and evaluation, yielding programs that demonstrate significantly improved generalization against both historical variants and human-designed warriors. Notably, independent runs exhibit convergent evolution, revealing underlying mechanisms of strategic convergence and enhanced generalization in open-ended adversarial settings.

Technology Category

• Machine Learning: Evolutionary Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Evolutionary Computation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) are increasingly being used to evolve solutions to problems in many domains, in a process inspired by biological evolution. However, unlike biological evolution, most LLM-evolution frameworks are formulated as 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 so-called"Red Queen"dynamics via continual adaptation to a changing objective. DRQ uses an LLM to evolve assembly-like programs, called warriors, which compete against each other 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, producing a sequence of adapted warriors. Over many rounds, we observe that warriors become increasingly general (relative to a set of held-out human warriors). Interestingly, warriors also become less behaviorally diverse across independent runs, indicating a convergence pressure toward a general-purpose behavioral strategy, much like convergent evolution in nature. This result highlights a potential value of shifting from static objectives to dynamic Red Queen objectives. 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 other more practical multi-agent adversarial domains, like real-world cybersecurity or combating drug resistance.

Problem

• Red Queen dynamics
• adversarial evolution
• LLM-based evolution
• Core War
• open-ended adaptation

Innovation

• Red Queen dynamics
• adversarial program evolution
• Core War
• LLM-based evolution
• self-play