Add dataset card and documentation

README.md

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+---
+task_categories:
+- other
+---
+
+# When to Align, When to Predict: A Phase Diagram for Multimodal Learning
+
+This repository contains pretrained encoders, cached features, and phase diagnostics data associated with the paper "[When to Align, When to Predict: A Phase Diagram for Multimodal Learning](https://huggingface.co/papers/2606.11190)".
+
+The paper characterizes when contrastive (Cross-Alignment, CA) vs. predictive (Cross-Prediction, CP) self-supervised objectives recover shared signals in multimodal data, particularly for scientific domains.
+
+[Project Website](https://ilaymalinyak.github.io/mm_align_vs_pred/) | [GitHub Repository](https://github.com/IlayMalinyak/mm_align_vs_pred)
+
+## Dataset Description
+
+The data stored here supports experiments including:
+- **Astro Data**: Pretrained encoders and cached features for LAMOST × Kepler/TESS spectra.
+- **Phase Diagnostics**: Data used to locate real-world datasets in the phase diagram regimes (Both, CA only, CP only, or Neither).
+
+## Sample Usage
+
+You can download the checkpoints, cached features, and phase diagnostics data using the `huggingface_hub` library:
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download("Ilayk/mm_align_vs_pred", repo_type="dataset", local_dir="hf_data")
+```
+
+After downloading, you can point your environment to the data for the astrophysical experiments:
+```bash
+export MULTIDESA_ROOT=hf_data
+```
+
+## Citation
+
+```bibtex
+@article{kamai2026align,
+  title   = {When to Align, When to Predict? A Phase Diagram for Multimodal Self-Supervised Learning},
+  author  = {Kamai, Ilay and Van Assel, Hugues and Regev, Aviv and Perets, Hagai B. and Balestriero, Randall},
+  journal = {arXiv preprint arXiv:2606.11190},
+  year    = {2026}
+}
+```