- A new AI safety method called GRAM (Gradient-Routed Auxiliary Modules) creates removable knowledge compartments for dual-use topics like virology and cybersecurity.
- GRAM allows separate control over dual-use capabilities within a single model, enabling configurations where modules can be turned 'on' or 'off' without retraining multiple models.
- Testing shows GRAM effectively removes targeted knowledge when modules are deleted, maintains general performance, and resists adversarial recovery attempts.
- The method scales across model sizes, with protection gaps widening as models grow larger, potentially offering more robust access control than current refusal-based safeguards.