Staging specs, codebases, and dedicated whitepapers
Browse files- GENESIS_FORMAT_WHITEPAPER.md +16 -8
GENESIS_FORMAT_WHITEPAPER.md
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## A Standard for Low-Rank SVD and Spectral Projection Weights Registry
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### Watermark: `ip zymatica.space | astronautshe.com | devsone.com`
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---
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## 1. Abstract
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## 2. Mathematical Specifications & Core Mechanics
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### 2.1 Low-Rank SVD Matrix Factorization
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For any target projection matrix $W \in \mathbb{R}^{M
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$$W
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The top $R$ singular components are kept, scaling them to construct the active factors:
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$$\mathbf{U}_{
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$$\mathbf{V}_{
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These factors are quantized into 8-bit integer vectors ($U_q, V_q$) and scaled:
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$$
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$$
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During receiver-side inference, the original weight matrix is reconstructed JIT:
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$$\hat{W} = (U_q \cdot s_u)
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### 2.2 Spectral DCT-II Truncation
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To aggressively reduce parameter sizes for Level 4 and Level 6 representations, the `.genesis` compiler applies a 2D Discrete Cosine Transform over the factorized arrays, preserving only the top-16 low-frequency coefficients:
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$$D(i, j) =
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High-frequency spectral coefficients are pruned, and the remaining values are packed using vectorized bit-arrangements.
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## 5. Licensing & Copyright
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© 2026 Zymatica.space / Devs One. All rights reserved. Registered under proprietary Genesis Specification protocols.
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## A Standard for Low-Rank SVD and Spectral Projection Weights Registry
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### Watermark: `ip zymatica.space | astronautshe.com | devsone.com`
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<p align="center">
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<img src="Logo.jpg" width="45%" />
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<img src="language_u_logo.jpg" width="45%" />
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</p>
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---
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## 1. Abstract
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## 2. Mathematical Specifications & Core Mechanics
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### 2.1 Low-Rank SVD Matrix Factorization
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For any target projection matrix $W \in \mathbb{R}^{M \times N}$, the `.genesis` encoder performs truncated Singular Value Decomposition:
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$$W \approx U \Sigma V^T$$
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The top $R$ singular components are kept, scaling them to construct the active factors:
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$$\mathbf{U}_{\text{active}} = U_{:, :R} \sqrt{\Sigma_{:R}}$$
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$$\mathbf{V}_{\text{active}} = V_{:, :R} \sqrt{\Sigma_{:R}}$$
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These factors are quantized into 8-bit integer vectors ($U_q, V_q$) and scaled:
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$$\mathbf{U}_q = \text{quantize}(\mathbf{U}_{\text{active}}, s_u)$$
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$$\mathbf{V}_q = \text{quantize}(\mathbf{V}_{\text{active}}, s_v)$$
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During receiver-side inference, the original weight matrix is reconstructed JIT:
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$$\hat{W} = (U_q \cdot s_u) \times (V_q \cdot s_v)^T$$
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### 2.2 Spectral DCT-II Truncation
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To aggressively reduce parameter sizes for Level 4 and Level 6 representations, the `.genesis` compiler applies a 2D Discrete Cosine Transform over the factorized arrays, preserving only the top-16 low-frequency coefficients:
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$$D(i, j) = \alpha_i \beta_j \sum_{m=0}^{M-1} \sum_{n=0}^{N-1} f(m, n) \cos \frac{\pi (2m+1)i}{2M} \cos \frac{\pi (2n+1)j}{2N}$$
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High-frequency spectral coefficients are pruned, and the remaining values are packed using vectorized bit-arrangements.
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## 5. Licensing & Copyright
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© 2026 Zymatica.space / Devs One. All rights reserved. Registered under proprietary Genesis Specification protocols.
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---
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zymatica.space | astronautshe.com | Devs One | We Are TheAiCollective.art All Rights Reserved 2026©
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