dailyai.report

Biological foundation models differ from LLMs because surface-level outputs fail to reveal internal knowledge. Sparse Autoencoders offer a way to extract hidden capabilities from existing RNA sequencing models without increasing scale. This approach shifts focus from building larger models to interpretability. Practitioners can now uncover biological insights already latent in current architectures.