docs: add official GitHub repositories (POLYDIM_EinSof and POLYDIM-THEORICAL)
Browse files
README.md
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@@ -25,7 +25,9 @@ model_name: phi4_14b_PMTP_polydim_einsof
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### SOTA Stiefel Manifold Adapter & PMTP Zero-Token Reasoning Engine
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> **Initiative:** POLYDIM-EINSOF | EinsofOS Sovereign Research
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> **Author & Lead Researcher:** Ariel García Traba (Independent Research / EinsofOS)
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> **Mathematical Manifold:** Compact Stiefel Subspace $\mathrm{St}(2048, 5120) \subset S^{2048-1}$
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> **Silicon Verification:** Exit Code 0 | Max Orthogonality Defect: `4.26e-09` | Neumaier Error $\le 10^{-15}$
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> **Inter-Model Transport:** 0 tokens serialized (Binary float64 IPC via `PmtpShmBus` / C-ABI)
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**POLYDIM-EINSOF** is an open-weights, high-dimensional AI architecture developed by **Ariel García Traba (EinsofOS)** that solves the fundamental information bottleneck of contemporary artificial intelligence: **discrete 1D token serialization**.
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In standard multi-agent systems and multi-model chains, continuous high-dimensional neural representations are collapsed into text strings (JSON, Markdown, prompts), destroying geometric structure, incurring catastrophic **Shannon Data Processing Inequality (DPI)** loss, introducing hallucination drift, and wasting millions of billable tokens.
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**PMTP (Polydim Memory Transfer Protocol)** completely eliminates this 1D bottleneck. It enables models to exchange internal latent states directly in physical shared memory (`PmtpShmBus` / C-ABI) at hardware speed, with **zero intermediate text serialization and 0 tokens billed**.
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| Metric | Traditional 1D LLM Serialization (JSON/REST) | POLYDIM PMTP (`phi4_14b_PMTP_polydim_einsof`) | Physical Gain |
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|:---|:---:|:---:|:---:|
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| **Token Cost (per hop)** | 1,000 – 4,000 tokens | **0 tokens** (Raw IEEE-754 memory) | **100% Cost Elimination** |
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| **Inter-Model Transfer Latency** | 250 ms – 1,800 ms (serialization + parsing) | **
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| **Information Loss (Shannon DPI)** | Massive (entropy loss through discrete quantization) | **0.00%** (continuous isometry preserved) | **Lossless Representation** |
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| **Orthogonality Defect ($\|W^T W - I\|_{\max}$)** | N/A (unconstrained strings) | **`4.26e-09`** (FP64 verified) | **Strict Geometric Gauge** |
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| **Norm Drift Tolerance** | $\pm 10^{-3}$ (token jitter) | **$\le 10^{-15}$** (Neumaier compensation) | **Exact Machine Precision** |
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| Turing Depth | Mode Name | Description & Use Case | Silicon Latency |
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|:---:|:---|:---|:---:|
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| `0` | `IDENTITY_BYPASS` | Direct pass-through without manifold projection. | `< 0.1 ms` |
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| `1` | `FAST_REFLEX` | Single-step isometric Stiefel projection for fast downstream tasks. | `
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| `2 .. 20` | `RED_TEAM_DELIBERATION` | Recursive Riemannian contraction with adversarial self-critique. | `50 – 75 ms` |
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| `21 .. 50` | `DEEP_TURING_EXPLOSION` | High-depth formal theorem proving, invariant checking, and anti-sycophancy proofs. | `80 – 120 ms` |
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## 6.
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If you use this model, the PMTP protocol, or the Stiefel manifold projection in your research or production systems, please cite:
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journal = {EinsofOS Autonomous Research},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/Polydim-Einsof/phi4_14b_PMTP_polydim_einsof}}
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}
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```
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### SOTA Stiefel Manifold Adapter & PMTP Zero-Token Reasoning Engine
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> **Initiative:** POLYDIM-EINSOF | EinsofOS Sovereign Research
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> **Author & Lead Researcher:** [Ariel García Traba](https://github.com/AGT1973) (Independent Research / EinsofOS)
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> **Official Codebase:** [github.com/AGT1973/POLYDIM_EinSof](https://github.com/AGT1973/POLYDIM_EinSof)
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> **Theoretical Foundation & Papers:** [github.com/AGT1973/POLYDIM-THEORICAL](https://github.com/AGT1973/POLYDIM-THEORICAL)
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> **Mathematical Manifold:** Compact Stiefel Subspace $\mathrm{St}(2048, 5120) \subset S^{2048-1}$
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> **Silicon Verification:** Exit Code 0 | Max Orthogonality Defect: `4.26e-09` | Neumaier Error $\le 10^{-15}$
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> **Inter-Model Transport:** 0 tokens serialized (Binary float64 IPC via `PmtpShmBus` / C-ABI)
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**POLYDIM-EINSOF** is an open-weights, high-dimensional AI architecture developed by **Ariel García Traba (EinsofOS)** that solves the fundamental information bottleneck of contemporary artificial intelligence: **discrete 1D token serialization**.
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* **Codebase & Kernels:** [https://github.com/AGT1973/POLYDIM_EinSof](https://github.com/AGT1973/POLYDIM_EinSof)
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* **Formal Theory, Theses & Papers:** [https://github.com/AGT1973/POLYDIM-THEORICAL](https://github.com/AGT1973/POLYDIM-THEORICAL)
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In standard multi-agent systems and multi-model chains, continuous high-dimensional neural representations are collapsed into text strings (JSON, Markdown, prompts), destroying geometric structure, incurring catastrophic **Shannon Data Processing Inequality (DPI)** loss, introducing hallucination drift, and wasting millions of billable tokens.
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**PMTP (Polydim Memory Transfer Protocol)** completely eliminates this 1D bottleneck. It enables models to exchange internal latent states directly in physical shared memory (`PmtpShmBus` / C-ABI) at hardware speed, with **zero intermediate text serialization and 0 tokens billed**.
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| Metric | Traditional 1D LLM Serialization (JSON/REST) | POLYDIM PMTP (`phi4_14b_PMTP_polydim_einsof`) | Physical Gain |
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|:---|:---:|:---:|:---:|
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| **Token Cost (per hop)** | 1,000 – 4,000 tokens | **0 tokens** (Raw IEEE-754 memory) | **100% Cost Elimination** |
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| **Inter-Model Transfer Latency** | 250 ms – 1,800 ms (serialization + parsing) | **44.73 ms** (Single-hop Stiefel projection) | **Up to 40× Faster** |
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| **Information Loss (Shannon DPI)** | Massive (entropy loss through discrete quantization) | **0.00%** (continuous isometry preserved) | **Lossless Representation** |
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| **Orthogonality Defect ($\|W^T W - I\|_{\max}$)** | N/A (unconstrained strings) | **`4.26e-09`** (FP64 verified) | **Strict Geometric Gauge** |
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| **Norm Drift Tolerance** | $\pm 10^{-3}$ (token jitter) | **$\le 10^{-15}$** (Neumaier compensation) | **Exact Machine Precision** |
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| Turing Depth | Mode Name | Description & Use Case | Silicon Latency |
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|:---:|:---|:---|:---:|
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| `0` | `IDENTITY_BYPASS` | Direct pass-through without manifold projection. | `< 0.1 ms` |
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| `1` | `FAST_REFLEX` | Single-step isometric Stiefel projection for fast downstream tasks. | `44.73 ms` |
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| `2 .. 20` | `RED_TEAM_DELIBERATION` | Recursive Riemannian contraction with adversarial self-critique. | `50 – 75 ms` |
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| `21 .. 50` | `DEEP_TURING_EXPLOSION` | High-depth formal theorem proving, invariant checking, and anti-sycophancy proofs. | `80 – 120 ms` |
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---
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## 6. Official Resources & Repositories
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* **Primary Codebase:** [https://github.com/AGT1973/POLYDIM_EinSof](https://github.com/AGT1973/POLYDIM_EinSof)
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* **Theoretical Framework & Scientific Preprints:** [https://github.com/AGT1973/POLYDIM-THEORICAL](https://github.com/AGT1973/POLYDIM-THEORICAL)
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* **Hugging Face Organization:** [https://huggingface.co/Polydim-Einsof](https://huggingface.co/Polydim-Einsof)
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---
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## 7. Citation & Formal Attribution
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If you use this model, the PMTP protocol, or the Stiefel manifold projection in your research or production systems, please cite:
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journal = {EinsofOS Autonomous Research},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/Polydim-Einsof/phi4_14b_PMTP_polydim_einsof}},
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note = {Code: \url{https://github.com/AGT1973/POLYDIM_EinSof} | Theory: \url{https://github.com/AGT1973/POLYDIM-THEORICAL}}
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}
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```
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