Artificial Intelligence #machine learning#autoencoder
New Rational Sparse Autoencoder Improves AI Interpretability with Trainable Activation Function
Researchers introduce the Rational Sparse Autoencoder (RSAE), which replaces fixed encoder nonlinearities with a trainable rational function. Across three language models and three baseline activation families, RSAE strictly improves reconstruction and downstream-behaviour metrics while preserving feature-level interpretability, adding only a few scalar parameters per autoencoder.
Jun 16, 2026 1 source