Aligning, Fast and Slow: A Control-Theoretic Oversight Framework for Long-Horizon Trajectories and Maintained Deployments
Summary
- Researchers introduced a control-theoretic oversight framework combining a fast loop for proposed actions and a slow loop for reviewing persistent changes in AI deployments.
- The framework derives a deployment-level bound and review-effort allocation rule when measurement errors and changes between checks are bounded.
- In simulation and stress tests involving coding and trading, combined controls significantly reduced deviations from intended behavior and shortfalls compared to un-gated runs.
Why it matters
This control-theoretic framework provides developers and auditors with a principled method to oversee long-horizon AI agent trajectories and maintained deployments, mitigating cumulative behavioral drift.