Update README.md

README.md

@@ -52,7 +52,9 @@ The primary hurdle in modern AI applications is the computational cost of deep t
 
 ## The Retrieval Architecture: Entropy and Exponential Search (O(log N))
 
-We open-source the accompanying RAG system that maximizes the potential of this lightweight model. It bypasses the limitations of fixed-size chunking and linear searching with two core innovations:
+We open-source the accompanying RAG system that maximizes the potential of this lightweight model. 
+We already made millions from the SaaS around this idea. So we are open sourcing it.
+It bypasses the limitations of fixed-size chunking and linear searching with two core innovations:
 The architecture uses the cohesion of the chunk to replace the function of the attention mechanism.
 
 PLEASE CITE THIS REPOSITORY IF YOU ARE BUILDING ON TOP OF THE IDEA. 
@@ -110,4 +112,14 @@ This combination is ideal for any application requiring high throughput and low
 | **Embedding Dimension** | 4096 |
 | **Model File Size** | 592 MB |
 | **Quantization** | int8 |
-| **Inference Pipeline** | Mean Pooling + L2 Normalization |
+| **Inference Pipeline** | Mean Pooling + L2 Normalization |
+
+```bibtex
+@misc{EH-RAG_2025,
+  title={Entropy-Harmonic RAG: Achieving Logarithmic Retrieval Complexity and Extreme Efficiency via Transformer Distillation},
+  author={Anonymous},
+  year={2025},
+  howpublished={Hugging Face},
+  doi={10.57967/hf/6962}
+}
+```