Do AI Models Want to Be Monitored? Measuring Monitorability Disposition in Large Reasoning Models
Addresses core AI safety challenges by shifting from reactive monitoring to proactive model introspection.
AI Summary
Researchers propose measuring 'monitorability disposition' in large reasoning models to proactively understand model behavior rather than reactively filtering outputs.
Excerpt
As AI models take on increasingly high-stakes responsibilities, understanding what a model is actually doing instead of simply constraining its outputs has become one of the most important safety challenges in AI. Current monitoring methods are primarily reactive and tend to address misbehavior only after it has occurred. Output filters provide an important layer of defense, but they remain fragile and can often be bypassed, as the recent Mythos/Fable 5 controversy and OpenAI–HuggingFace inciden