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README.md

@@ -27,6 +27,9 @@ on 2k images).
 Built on a pure convolutional architecture with no attention layers in the
 encoder or decoder, enabling efficient inference at any resolution.
 
+**[Technical Report](technical_report_semantic.md)** ·
+**[Interactive Results Viewer](https://huggingface.co/spaces/data-archetype/semdisdiffae-results)**
+
 ## Key Features
 
 - **Fast**: ~3 ms/img encode, ~6 ms/img decode (1 step) on Blackwell RTX Pro 6000 — significantly
@@ -104,6 +107,18 @@ See the [technical report](technical_report_semantic.md) for full details.
 - PyTorch >= 2.0
 - safetensors (for loading weights)
 
+## Citation
+
+```bibtex
+@misc{semdisdiffae,
+  title   = {SemDisDiffAE: A Semantically Disentangled Diffusion Autoencoder with FCDM Blocks},
+  author  = {data-archetype},
+  year    = {2026},
+  month   = apr,
+  url     = {https://huggingface.co/data-archetype/semdisdiffae},
+}
+```
+
 ## License
 
 Apache 2.0