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Add pipeline tag and improve documentation

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Hi! I'm Niels from the community science team at Hugging Face.

I've opened this PR to improve the model card for Shesha:
- Added `pipeline_tag: other` to the metadata to better categorize the repository.
- Included a summary of the Shesha framework and the concept of geometric stability based on your paper "Geometric Stability: The Missing Axis of Representations".
- Added a citation section to help users correctly cite your research.

Please let me know if you have any questions!

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  1. README.md +33 -4
README.md CHANGED
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  ---
 
 
 
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  tags:
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  - arxiv:2601.09173
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  - geometric-stability
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  - steering
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  - interpretability
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  - computational-biology
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- license: mit
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- library_name: shesha-geometry
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  ---
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  # Shesha: Geometric Stability Metric
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- 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).
 
 
 
 
 
 
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  ## ๐Ÿš€ Quick Links
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  * **๐Ÿ“„ Paper:** [arXiv:2601.09173](https://arxiv.org/abs/2601.09173)
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  ## ๐Ÿ“ฆ Installation
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  ```bash
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- pip install shesha-geometry
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ library_name: shesha-geometry
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+ license: mit
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+ pipeline_tag: other
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  tags:
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  - arxiv:2601.09173
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  - geometric-stability
 
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  - steering
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  - interpretability
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  - computational-biology
 
 
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  ---
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  # Shesha: Geometric Stability Metric
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+ 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).
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+
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+ ## Overview
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+
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+ 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.
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+
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+ **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.
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  ## ๐Ÿš€ Quick Links
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  * **๐Ÿ“„ Paper:** [arXiv:2601.09173](https://arxiv.org/abs/2601.09173)
 
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  ## ๐Ÿ“ฆ Installation
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  ```bash
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+ pip install shesha-geometry
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+ ```
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+
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+ ## Key Applications
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+
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+ Geometric stability provides actionable insights across multiple domains:
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+ - **Safety Monitoring:** Acts as a functional geometric canary to detect structural drift nearly 2$\times$ more sensitively than CKA.
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+ - **Controllability:** Supervised stability predicts linear steerability with high correlation ($\rho = 0.89$-$0.96$).
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+ - **Model Selection:** Dissociates from transferability, revealing the "geometric tax" that transfer optimization incurs.
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+ - **Scientific Analysis:** Predicts CRISPR perturbation coherence and neural-behavioral coupling.
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+
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+ ## Citation
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+
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+ If you use Shesha or geometric stability in your research, please cite:
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+
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+ ```bibtex
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+ @article{raju2026geometric,
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+ title={Geometric Stability: The Missing Axis of Representations},
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+ author={Raju, Prashant C.},
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+ journal={arXiv preprint arXiv:2601.09173},
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+ year={2026}
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+ }
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+ ```