Add dataset card, link to paper and code

README.md

@@ -1,3 +1,43 @@
 ---
 license: cc-by-nc-sa-4.0
+task_categories:
+- image-feature-extraction
+tags:
+- interpretability
+- vision-transformer
+- sparse-autoencoders
+- mechanistic-interpretability
 ---
+
+# ViSAE: Neuroscience-Motivated Probing Suite
+
+This dataset is part of the **ViSAE** toolbox, presented in the paper [Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers](https://huggingface.co/papers/2606.06664) (ICML 2026).
+
+ViSAE is a mechanistic interpretability toolbox designed to decompose Vision Transformer (ViT) representations into human-interpretable concepts using Sparse Autoencoders (SAEs).
+
+- **GitHub Repository:** [deep-real/ViSAE](https://github.com/deep-real/ViSAE)
+- **Project Page:** [https://tangli0305.github.io/](https://tangli0305.github.io/)
+
+## Dataset Summary
+
+The probing suite consists of:
+1.  **Probing Image Set:** 64,000 images designed for SAE training, optimized for high concept coverage (20x more efficient than ImageNet).
+2.  **Concept Set:** A 16,000 visually grounded concept vocabulary for SAE feature interpretation (available in the GitHub repository).
+
+## Usage
+
+To use this dataset for training SAEs or tracing concept circuits, please refer to the official [GitHub repository](https://github.com/deep-real/ViSAE) for the implementation details, including:
+- Extracting intermediate representations from ViTs.
+- Training SAEs for each ViT layer.
+- Mapping learned features to concepts using CLIP.
+
+## Citation
+
+```bibtex
+@inproceedings{li2026visae,
+ title={Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers},
+ author={Li, Tang and Chen, Yanlin and Ma, Mengmeng and Peng, Xi},
+ booktitle={Proceedings of the International Conference on Machine Learning (ICML)},
+ year={2026}
+}
+```