Paper: arxiv.org/abs/2610.24521
This research was supported by the Anthropic Fellows Program, White Circle and BlueDot.
Thanks to Ben Thompson, @_gngdb, @CurtTigges, @tomjiralerspong, @Mcn_S7, and @parvmahajan0.
nev retweeted
Today, we're releasing The Circuit Analysis Research Landscape: an interpretability post extending & open sourcing Anthropic's circuit tracing work, co-authored by @Anthropic, @GoogleDeepMind, @GoodfireAI @AiEleuther, and @decode_research.
Here's a quick demo, details follow: ⤵️
🧵1/6 SAEs have become a staple of LLM interpretability, but what if we applied them to image generation models?
My recent paper with @dmhook, @Yixiong_Hao, @afterlxss, @Sheikheddy, and @ArthurConmy adapts SAEs to understand the SOTA diffusion transformer FLUX.1 ⬇️
5/6 Steering image generation via SAE features through activation addition is demonstrably effective. However, steering efficacy depends strongly on the initial prompt's relevance to the steered features, indicating localized representation in diffusion models.
6/6 Future research avenues include adapting SAE architectures for spatial coherence and understanding the effects of text/time conditioning and distillation.
Preprint: arxiv.org/abs/2505.24360
Code: github.com/neverix/fae, github.com/kisate/flux-saes-…
We quantized all Gemma Scopes into 4 bits, reducing memory and storage requirements by about 4 times with ~20% higher variance unexplained. You can try the quantized versions and make your own quantized SAEs with gist.github.com/neverix/79d5…
DM me if you see bugs or have requests
