Add pipeline tag and improve documentation

README.md

@@ -1,4 +1,7 @@
 ---
+library_name: shesha-geometry
+license: mit
+pipeline_tag: other
 tags:
 - arxiv:2601.09173
 - geometric-stability
@@ -9,13 +12,17 @@ tags:
 - steering
 - interpretability
 - computational-biology
-license: mit
-library_name: shesha-geometry
 ---
 
 # Shesha: Geometric Stability Metric
 
-This is the official Hugging Face hub for the **Shesha** geometric stability metric, as presented in the paper *Geometric Stability: The Missing Axis of Representations* (arXiv:2601.09173).
+This is the official Hugging Face hub for the **Shesha** geometric stability metric, as presented in the paper [Geometric Stability: The Missing Axis of Representations](https://huggingface.co/papers/2601.09173).
+
+## Overview
+
+Analysis of learned representations typically focuses on *similarity*, measuring how closely embeddings align with external references. However, similarity reveals only what is represented, not whether that structure is robust. 
+
+**Shesha** is a framework for measuring **geometric stability**, a distinct dimension that quantifies how reliably representational geometry holds under perturbation. Across 2,463 configurations in seven domains, research shows that stability and similarity are empirically uncorrelated ($\rho \approx 0.01$). This distinction makes Shesha a necessary complement to similarity for auditing representations across biological and computational systems.
 
 ## 🚀 Quick Links
 * **📄 Paper:** [arXiv:2601.09173](https://arxiv.org/abs/2601.09173)
@@ -25,4 +32,26 @@ This is the official Hugging Face hub for the **Shesha** geometric stability met
 ## 📦 Installation
 
 ```bash
-pip install shesha-geometry
+pip install shesha-geometry
+```
+
+## Key Applications
+
+Geometric stability provides actionable insights across multiple domains:
+- **Safety Monitoring:** Acts as a functional geometric canary to detect structural drift nearly 2$\times$ more sensitively than CKA.
+- **Controllability:** Supervised stability predicts linear steerability with high correlation ($\rho = 0.89$-$0.96$).
+- **Model Selection:** Dissociates from transferability, revealing the "geometric tax" that transfer optimization incurs.
+- **Scientific Analysis:** Predicts CRISPR perturbation coherence and neural-behavioral coupling.
+
+## Citation
+
+If you use Shesha or geometric stability in your research, please cite:
+
+```bibtex
+@article{raju2026geometric,
+  title={Geometric Stability: The Missing Axis of Representations},
+  author={Raju, Prashant C.},
+  journal={arXiv preprint arXiv:2601.09173},
+  year={2026}
+}
+```