2026
AbsTopK: Rethinking Sparse Autoencoders For Bidirectional Features
ICLR 2026poster
Sparse autoencoders (SAEs) are a cornerstone of interpretability for large language models (LLMs), aiming to decompose hidden states into meaningful semantic features. While several SAE variants have been proposed, there remains no principled framework to derive SAEs from the original dictionary lea…