Ensemble Monitoring for AI Control: Diverse Signals Outweigh More Compute
As AI systems are increasingly deployed in autonomous agentic settings at scale, it is important to ensure the actions they take are safe and aligned with user intent. Monitoring agent actions is a key safety mechanism, yet reliable monitors remain difficult to build and the scale of these systems makes human oversight impractical. We show that combining signals from diverse monitors into an ensemble improves detection of misaligned actions. We build 12 GPT-4.1-Mini monitors using both prompting and fine-tuning strategies. We evaluate them on coding tasks where candidate solutions pass standard tests but fail on adversarial inputs. In this setting, diverse ensembles outperform both indivi...
| Authors | Eugene Koran, Yejun Yun, Samantha Tetef et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 18 May 2026 |
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