dailyai.report

RNA sequencing models hide capabilities that standard evaluations fail to detect. Unlike LLMs, surface-level outputs in biological models poorly represent their internal knowledge. Using Sparse Autoencoders, researchers aim to extract these hidden features. This shift in interpretability methodology allows practitioners to unlock existing model utility without requiring larger, more expensive training runs.