R^textbf{2AI}: Towards Resistant and Resilient AI in an Evolving World

Youbang Sun, Xiang Wang, Jie Fu, Chaochao Lu, Bowen Zhou

R^textbf{2AI}: Towards Resistant and Resilient AI in an Evolving World: 3 upvotes on Hugging Face Daily Papers, #20 of 25 papers on 2025-09-09. Day-by-day upvote history.

In this position paper, we address the persistent gap between rapidly growing AI capabilities and lagging safety progress. Existing paradigms divide into ``Make AI Safe'', which applies post-hoc alignment and guardrails but remains brittle and reactive, and ``Make Safe AI'', which emphasizes intrinsic safety but struggles to address unforeseen risks in open-ended environments. We therefore propose safe-by-coevolution as a new formulation of the ``Make Safe AI'' paradigm, inspired by biological immunity, in which safety becomes a dynamic, adversarial, and ongoing learning process. To operationalize this vision, we introduce R^2AI -- Resistant and Resilient AI -- as a practical framework that unites resistance against known threats with resilience to unforeseen risks. R^2AI integrates fast and slow safe models, adversarial simulation and verification through a safety wind tunnel, and continual feedback loops that guide safety and capability to coevolve. We argue that this framework offers a scalable and proactive path to maintain continual safety in dynamic environments, addressing both near-term vulnerabilities and long-term existential risks as AI advances toward AGI and ASI.

Paper page on Hugging Face · arXiv

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