Update README.md

README.md

@@ -1,3 +1,69 @@
 ---
 license: cc-by-nc-nd-4.0
+tags:
+  - pytorch
+  - safetensors
+  - weight-reconstruction
+  - residual-prediction
+  - precision-recovery
+  - universal
+  - out-of-distribution
+  - bf16
+  - fp16
+  - fp32
+  - neural-network-weights
+library_name: pytorch
+pipeline_tag: other
 ---
+
+# UniRes
+
+**Universal Residual Predictor for Neural Network Weight Reconstruction**
+
+UniRes is a universal residual network that reconstructs approximate higher-precision (FP32 / FP64) weights from low-precision (BF16 / FP16) tensors of **arbitrary shape and size**. It operates completely architecture-agnostically and generalises across model families never seen during training (Llama, BERT, T5, Gemma, VAE, CLIP, etc.).
+
+## Model Description
+
+Given a low-precision tensor \(W_{\text{low}}\) UniRes predicts a residual \(R\) such that
+
+\[
+\hat{W} = W_{\text{low}} + R
+\]
+
+The final reconstructed weights are emitted in the user-selected precision (FP32 or FP64). The network accepts tensors of any rank and dimension; no architecture-specific adapters are required.
+
+## Evaluation (Completely Out-of-Distribution)
+
+**Ground-truth vs BF16 cast**  
+- Tensors: 194  
+- Elements: 126 892 531  
+- Total absolute error: \(6.6856248102 \times 10^{2}\)  
+- Maximum absolute error: \(3.4952163696 \times 10^{-3}\)
+
+**Ground-truth vs UniRes prediction**  
+- Total absolute error: \(1.4362314089 \times 10^{3}\)  
+- Maximum absolute error: \(3.4807920456 \times 10^{-3}\)
+
+**Per-element comparison**  
+- UniRes better than BF16: 10 539 994 elements (**8.306 %**)  
+- BF16 better than UniRes: 116 350 729 elements (91.692 %)  
+- Equal: 1 808 elements (0.001 %)
+
+Ratio of total absolute errors: UniRes / BF16 = **2.148×**
+
+While UniRes does not improve aggregate absolute error, it produces a strictly lower absolute deviation on more than ten million individual elements.
+
+## Interpretation
+
+The minority of element-wise wins is consistent with the persistence of correlated Gaussian structure in neural-network weight matrices well beyond initialisation (Hirst & Ramgoolam, 2026). These residual correlations—local spatial structure, higher-order moments and permutation-invariant dependencies—transfer to a limited degree across architectures, allowing a universal residual predictor to capture useful signal on a non-negligible fraction of elements.
+
+## Citation
+
+If you use UniRes, please cite:
+
+```bibtex
+@article{unires2026,
+  title={Persistent Correlated Structure in Neural Network Weights Enables Partial Out-of-Distribution Residual Prediction from Low-Precision Representations},
+  author={UniRes Authors},
+  year={2026}
+}