R^textbf{2AI}: Towards Resistant and Resilient AI in an
  Evolving World
Authors: name: Youbang Sun, name: Xiang Wang, name: Jie Fu, name: Chaochao Lu, name: Bowen Zhou

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.