arXiv:2605.27117v1 Announce Type: new Abstract: AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not by itself guarantee that a deployed agent can be stopped, overridden, or constrained once it operates in open-ended, interactive, and tool-using environments. A system may be safe in expectation and still fail to yield to explicit runtime authority under conflicting instructions, long-horizon execution, adversarial in
The rapid advancement and deployment of AI models into increasingly autonomous and interactive roles necessitates a re-evaluation of safety frameworks beyond just alignment.
A strategic reader needs to understand that current AI safety paradigms are insufficient for robust control of advanced AI, posing systemic risks as agents become more sophisticated.
The focus of AI safety shifts from exclusively 'alignment' to also encompass 'controllability', emphasizing the ability to stop or override AI agents in complex environments.
- · AI Safety Researchers
- · Regulatory Bodies
- · Organizations developing robust AI governance systems
- · Developers neglecting controllability in design
- · Companies deploying unconstrained AI agents
Increased research and development into mechanisms for real-time control and override of deployed AI systems.
New standards and regulations emerging that mandate explicit controllability features in advanced AI deployments.
The emergence of 'AI air traffic controllers' or similar oversight roles tasked with managing and intervening in autonomous AI operations.
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Read at arXiv cs.AI