R^textbf{2AI}: Towards Resistant and Resilient AI in an
  Evolving World
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.
68bf8d90207285de11b07b4a
Youbang Sun
68bf8d90207285de11b07b4b
Xiang Wang
claimed_verified
2025-09-09T13:48:21.076Z
65fca775fa59bdf4737b1a84
/avatars/a161b510bde8f57e7686cbb0b4aa6a52.svg
Xiang Wang
user
xiangwang1223
68bf8d90207285de11b07b4c
Jie Fu
68bf8d90207285de11b07b4d
Chaochao Lu
68bf8d90207285de11b07b4e
Bowen Zhou
R^textbf{2AI}: Towards Resistant and Resilient AI in an
  Evolving World
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.
A new framework, R²AI, is proposed to enhance AI safety through coevolution, combining resistance to known threats with resilience to unforeseen risks using fast and slow safe models and adversarial simulation.
safe-by-coevolution R²AI Resistant and Resilient AI fast and slow safe models safety wind tunnel continual feedback loops AGI ASI