Evaluate cross-architecture applicability and develop generalizable methods

Determine how well existing interpretability methods (including sparse dictionary learning and circuit analysis) apply to architectures such as diffusion models, vision transformers, RWKV, and state space models, and develop techniques that generalize effectively across architectures.

Background

The paper surveys alternative architectures that are increasingly competitive, noting early signs of transfer for some techniques but no comprehensive evidence of broad applicability.

The authors explicitly call out the need for universal approaches and cross-architecture validation to future‑proof interpretability research as model designs evolve.