DifFRACT: Diffusion Feature Reconstruction and Attribution for Circuit Tracing
Mechanistic interpretability seeks to explain neural network behavior by decomposing model computations into interpretable features and circuits. While transcoder-based circuit tracing has recently enabled detailed causal analyses of large language models, multimodal diffusion transformers for image generation remain comparatively opaque. We still lack tools for understanding how semantic information propagates across denoising steps and how text and image representations interact within double-stream MM-DiT architectures. Existing methods provide only partial insight: attention maps expose a limited view of token interactions, while sparse autoencoders can discover interpretable features...
| Authors | Artyom Mazur, Nina Konovalova, Aibek Alanov |
|---|---|
| Primary category | cs.CV |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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