{"id":49186943,"url":"https://github.com/deskiziarecords/tungsten-alpha","last_synced_at":"2026-04-23T04:30:41.198Z","repository":{"id":343756199,"uuid":"1179041098","full_name":"deskiziarecords/tungsten-alpha","owner":"deskiziarecords","description":"Production JAX framework eliminating dynamic shape recompilation. 7-layer Adelic Riemannian stack: NRRB (threshold bridges), CSM (HLO hardening), ATC (adaptive JIT), ARSM (pure state), ASTC (telemetry), AFRH (FIM/nat-grad), AFRC (cluster transport). 100% JIT-stable, scales 10k+  Orbax→Triton ready.  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It solves the \"Discrete Singularity\" problem—where gradients die at logical branches—by treating computation as a continuous, differentiable Riemannian manifold.\n\n### 1. ARCHITECTURAL PILLARS\n\nThe system is built on a **7-Layer Adelic Stack**, ensuring that telemetry, logic, and memory are unified under a single geometric metric.\n\n* **NRRB (Resurgence Bridge):** Employs Weierstrass Gaussian proxies to enable gradient flow through non-differentiable thresholds (if/else, bit-masks).\n* **CSM (Crystalline Static Manifold):** Binds dynamic shapes into rigid, static HLO allocations for zero-jitter, 100% hardware utilization.\n* **ATC (Trace Crystallizer):** Hardens adaptive logic into fixed XLA traces, eliminating recompilation latency.\n* **ARSM (Recursive State):** Manages temporal continuity through recursive hidden state manifolds.\n* **ASTC (Stochastic Ingest):** Maps non-stationary sensor streams into uniform 16D interaction lattices via SOS-DP.\n* **AFRH (Fisher Regulator):** Computes the local Fisher Information Matrix (FIM) to stabilize learning via Natural Gradients.\n* **AFRC (Riemann Connector):** Propagates the Fisher Metric across decentralized nodes using Levi-Civita Parallel Transport.\n\n---\n\n### 2. MATHEMATICAL CORE\n\n**The Levi-Civita Connection ($\\nabla$)**\nTo maintain coherence across a decentralized cluster, Tungsten Alpha implements Parallel Transport. The Fisher Metric $g_{ij}$ is moved across the manifold such that the information volume is conserved:\n\n$$g' = \\Omega g \\Omega^T$$\n\nWhere $\\Omega$ is an orthogonal connection matrix derived from the **Adelic-Fisher-Riemann-Connector (AFRC)**. This ensures that the \"Curvature\" of the intelligence remains invariant as it moves between sensor nodes.\n\n---\n\n### 3. PROJECT STRUCTURE\n\n```text\nTungsten-Alpha/\n├── README.md              # Project Specification\n├── main.py                # Universal Orchestrator\n├── flash_binary.py        # Orbax Serialization Protocol\n├── core/\n│   ├── bridge.py          # NRRB Implementation\n│   ├── manifold.py        # CSM Logic\n│   ├── geometry.py        # AFRH (Fisher Metric)\n│   └── transport.py       # AFRC (Parallel Transport)\n└── visualization/\n    └── dashboard.py       # Telemetry Dashboard\n\n```\n\n---\n\n### 4. DEPLOYMENT AND VALIDATION\n\n**Zero-Recompilation Hardening**\nTungsten Alpha is designed for production environments where latency is critical. By using `jax.jit` and `lax.fori_loop` within a static manifold, we achieve deterministic execution times.\n\n**Verification:**\nRun the internal validation suite to confirm HLO Fusion and Metric Stability:\n\n```bash\npython core/transport.py\n\n```\n\nExpected Output: `AFRC STATUS: PRODUCTION-READY ✓ HLO FUSION: TRUE ✓`\n\n---\n[dashboard](https://raw.githubusercontent.com/deskiziarecords/tungsten-alpha/refs/heads/main/tng-alpha.png)\n\n\nTungsten Alpha was built to move beyond the limitations of standard backpropagation. It understands the light by calculating its own curvature.\n\n\n## LICENSE\n\nApache License 2.0. Developed for the future of decentralized AGI.\n\n---\n## Support My Work\n\nIf you enjoy my work and would like to support me, consider buying me a coffee! \n\n[![Buy Me a Coffee](https://cdn.buymeacoffee.com/buttons/v2/arial-multi.svg)](https://www.buymeacoffee.com/hipotermiah)\n\n### Author: J. ROBERTO JIMENEZ C.    - tijuanapaint@gmail.com   -  @hipotermiah\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeskiziarecords%2Ftungsten-alpha","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeskiziarecords%2Ftungsten-alpha","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeskiziarecords%2Ftungsten-alpha/lists"}