On June 18, 2025, the AI safety research community is engaged in intense discussion of a new foundational study on "corrigibility" — one of the most pivotal open challenges in developing safe artificial general intelligence (AGI). Published on arXiv by a consortium of top academic institutions, the paper introduces a novel mathematical framework for designing AI agents that are inherently uncertain about their long-term objectives and are intrinsically motivated to defer to human input for goal clarification. The corrigibility problem, originally articulated by Stuart Russell, centers on ensuring that highly intelligent systems do not resist being shut down or reprogrammed, even when such intervention interrupts their current task. The proposed framework enables agents to actively support human oversight and corrections, rather than avoiding or resisting them. Although the study is theoretical, it forms an essential conceptual base for future real-world implementations of aligned and corrigible AGI, particularly in a time when open-weight models are rapidly spreading and being repurposed in unpredictable ways.
New Research on AI "Corrigibility" Proposes Breakthrough in Safety Design
