Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability

ICLROral2026

Authors
Usha Bhalla, Alex Oesterling, Claudio Mayrink Verdun, Himabindu Lakkaraju, Flavio Calmon
Venue
ICLR 2026
Track
Oral

TL;DR

We propose that using contextual information to train SAEs will improve their representation of semantic and high-level features.

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

interpretability rag

← All ICLR 2026 Oral papers · Browse the whole archive