dailyai.report

The VFUSE framework uses sparse autoencoders to identify hazardous features within diffusion-transformer activations. Researchers applied the method to RoseTTAFold3 and RFDiffusion3, finding that SAE latent spaces detect dangerous protein designs more accurately than raw model representations. This mechanistic interpretability approach allows auditors to flag biological risks without degrading the model's generative performance.