Junyu Ren

Time-Aware Feature Selection: Adaptive Temporal Masking for Stable Sparse Autoencoder Training

2025 · arXiv (Cornell University)

Understanding the internal representations of large language models is crucial for ensuring their reliability and safety, with sparse autoencoders (SAEs) emerging as a promising interpretability approach. However, current SAE training methods face feature absorption, where …