Interpreting and steering diffusion models
Key facts. Interpreting and steering diffusion models: 27 papers on arXiv since 2022 (data through 14 Oct 2026). 11 of its 27 mapped papers (41%) cite “SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders” (2025, arXiv 2501.18052). 4 new papers 24 Sep–14 Oct.
What’s new
24 Sep – 14 Oct 2026
- Earlier · 4
- Beyond the Linear Representation Hypothesis: Non-Linear Activation Steering in Text-to-Image Models also fits: Interpreting vision and multimodal models
- RASteer: Retain-Aware Activation Steering for Concept Erasure in Diffusion Models
- D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders
- SharedSAE: a unified sparse dictionary across u-net blocks and denoising time for diffusion models
Foundations
Most cited by papers here
- SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders2025cited by 11 here
- One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models2024cited by 7 here
- textitRevelio: Interpreting and leveraging semantic information in diffusion models2024cited by 5 here
- TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation2025cited by 4 here
- Concept Steerers: Leveraging K-Sparse Autoencoders for Test-Time Controllable Generations2025cited by 3 here
Lineage
The papers this direction builds on, oldest first.
Each paper on the line cites the one before.
- 2022Extracting Latent Steering Vectors from Pretrained Language Modelscited by 2 here
- 2023Steering Language Models With Activation Engineeringcited by 4 here
- 2023Steering Llama 2 via Contrastive Activation Additioncited by 2 here
- 2024Interpreting Attention Layer Outputs with Sparse Autoencoderscited by 3 here
- 2024Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2cited by 2 here
- 2024One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Modelscited by 7 here
- 2025SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoderscited by 11 here
17 more it builds on, not in this chain
- 2022Toy Models of Superpositioncited by 4 here
- 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 20 here
- 2024Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Modelscited by 2 here
- 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 3 here
- 2024The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders to InceptionV1 Early Visioncited by 4 here
- 2024Scaling and evaluating sparse autoencoderscited by 10 here
- 2024Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoderscited by 2 here
- 2024Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discoverycited by 2 here
- 2024Interpreting and Editing Vision-Language Representations to Mitigate Hallucinationscited by 3 here
- 2024An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generationcited by 3 here
- 2024Steering Language Model Refusal with Sparse Autoencoderscited by 3 here
- 2024textitRevelio: Interpreting and leveraging semantic information in diffusion modelscited by 5 here
- 2024BatchTopK Sparse Autoencoderscited by 4 here
- 2025AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoderscited by 4 here
- 2025Concept Steerers: Leveraging K-Sparse Autoencoders for Test-Time Controllable Generationscited by 3 here
- 2025TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generationcited by 4 here
- 2025Learning Multi-Level Features with Matryoshka Sparse Autoencoderscited by 3 here
The 24 most cited of 64.
Browse
22 of 27, ranked 6–27
- 6Interpreting Large Text-to-Image Diffusion Models with Dictionary Learning20252
- 7SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders20252
- 8A Single Neuron Works: Precise Concept Erasure in Text-to-Image Diffusion Models20252
- 9OrthoEraser: Coupled-Neuron Orthogonal Projection for Concept Erasure20261
- 10Emergence and Evolution of Interpretable Concepts in Diffusion Models20251
- 11CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models20260
- 12DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders20260
- 13Learning a Generative Meta-Model of LLM Activations20260
- 14PolypSteer: Counterfactual Endoscopic Synthesis via Training-Free Activation Steering20260
- 15SHIFT: Steering Hidden Intermediates in Flow Transformers20260
All 22
- 16Deep Dreams Are Made of This: Visualizing Monosemantic Features in Diffusion Models20260
- 17Residualized Temporal Sparse Autoencoders for Interpreting Diffusion Models20260
- 18DifFRACT: Diffusion Feature Reconstruction and Attribution for Circuit Tracing20260
- 19Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models20260
- 20PEAK: Precise and Persistent Concept Erasure via k-Sparse Autoencoders20260
- 21EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders20260
- 22Sparse Autoencoder as a Zero-Shot Classifier for Concept Erasing in Text-to-Image Diffusion Models20250
- 23Decoding Vision Transformers: the Diffusion Steering Lens20250
- 24Model-Agnostic Gender Bias Control for Text-to-Image Generation via Sparse Autoencoder20250
- 25SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder20250
- 26LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfer20250
- 27Schr"odinger's Bat: Diffusion Models Sometimes Generate Polysemous Words in Superposition20220