Title: Prioritizing Safety Before Launching Open-Ended AI Systems

Abstract:

The rapid evolution of artificial intelligence has largely been propelled by the synergy between foundation models and curiosity-driven learning, strategies designed to enhance both capability and adaptability. In this evolving environment, the concept of open-endedness—characterized by AI agents autonomously and continuously producing novel behaviors, representations, or solutions—has attracted growing attention. This dynamic is particularly pertinent to the fields of self-evolving agents and long-horizon discovery.

This position paper contends that the unique attributes of open-ended AI systems create a distinct and previously underexplored category of safety risks. These include a loss of predictability, emergent misalignment, and significant challenges in maintaining control as systems progress beyond their original design parameters. These issues must be tackled proactively. Such risks differ qualitatively from those linked to task-bound or static models and are unlikely to be mitigated by current safety frameworks alone. Consequently, these dangers require careful examination prior to large-scale deployment. The paper outlines a taxonomy of these primary challenges, explores potential research avenues, and urges coordinated efforts to ensure the safe and responsible advancement of open-ended AI.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC