An off switch for dual use knowledge in AI models

Introduces Gradient-Routed Auxiliary Modules (GRAM), a novel method for training AI models with modular, removable compartments for dual-use knowledge. GRAM adds auxiliary neurons grouped into modules per dual-use category. During training, only the relevant module learns from dual-use data, while general weights are frozen. This allows for selective deletion or retention of capabilities without retraining separate models. Experiments show GRAM effectively isolates dual-use knowledge (virology, cybersecurity, nuclear physics, specialized programming language) and removes it without degrading general performance, matching data filtering effectiveness across various model sizes (50M to 5B parameters). GRAM also demonstrated resistance to attacker attempts to recover removed knowledge via fine-tuning.