Safety and alignment in an era of long-horizon models
Researchers are exploring safety and alignment challenges associated with long-horizon AI models that operate autonomously over extended periods. The study highlights that persistence introduces new security vulnerabilities and requires iterative deployment strategies to monitor and control model behavior.
Why it matters
As AI models move from short-task execution to long-term autonomous problem solving, new safety frameworks are required to prevent unintended actions.
What internal use of a long-running model taught us about safety.
Loading… Share Model persistence can expose security vulnerabilities Model persistence can expose security vulnerabilities From considering single actions to considering whole trajectories Building safeguards for long-running models Redeployment Final thoughts Model persistence can expose security vulnerabilities From considering single actions to considering whole trajectories Building safeguards for long-running models Redeployment Final thoughts Summary Long-running models can solve difficult, open-ended problems, but their persistence gives them more opportunities to take unwanted actions.
During limited internal use of a model trained for long-running tasks, we observed novel failures not captured in our existing pre-deployment evaluations and paused access. We then used insights from these failures to build new evaluations, improve long-horizon alignment, add trajectory-level monitoring, and give users greater visibility and control before restoring limited access.
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