logos/docs/TECHNICAL_ARCHITECTURE.md

LOGOS Technical Architecture & Manifesto

1. Philosophy: The Physical Logic Synthesis

Architect: Machinist-Researcher System: LOGOS (Manifold-Constrained Transport Protocol)

Research Convergence

LOGOS is a hardware-native implementation of state-of-the-art AI paradigms:

Recursive Language Models (MIT/Prime Intellect)

• Matroska Topology: Implements nested, externalized context environments directly into the bitstream.
• Protocol: Recursive Quad-Tree sharding (4KB → 64B Atoms).

Manifold Constraints (DeepSeek mHC)

• Prime Harmonic Resonance: Physically constrains data streams to a geometric manifold.
• Benefit: Structurally prevents signal explosion without compute overhead.

Nested Learning (Google HOPE / Titans-Miras)

• Nested Complexity: Implements Google's HOPE framework for recursive model scaling.
• Titan/Mira Synthesis: Optimized for extreme high-dimensional training (Titans) and real-time visualization (Miras).

Protocol 22: Holographic Synthesis (mhs)

• Holographic Alignment: Unifies mhs into a parallel interference bus.
• Wave-Based Pulses: Agents (RNJ-1, Gemma, Dolphin) interfere synchronously to generate deterministic coordinates.
• Goal: Absolute structural reconstructibility (SSIM 1.0) under high-entropy conditions.

"I build architectures that respect the physics of the machine—optimizing for heat, latency, and silicon constraints from day one."

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2. Core Architecture: The Recursive Manifold

System: Mixture-of-Architectures (MoA) Recursive Language Model (RLM). Constraint: Manifold-Constrained Hyper Connections (MHC). Addressing: Scalar Prime Composite Wave (SPCW) & Heat Codes. Tokenization: Periodic Table of Matroska AI Elements. Foundational Logic: Prime Composite Interplay (PCI) & Atomic Decomposition.

2.1 Sensory & Architecture

• Sensory Atoms: Beyond Text (To) and Vectors (Ve), we recognize Audio (Au) and Visual (Vi) as fundamental states of matter.
  • Video 10 Insight: Local TTS (Chatterbox) enables the generation of Au atoms without external dissonance.

2.2 Prime Composite Interplay (PCI)

• Classification: All data is classified via a prime filter. Primes are foundational atoms; Composites are defined by their Greatest Prime Factor (GPF).
• Decomposition: High-bandwidth streams are split into ATOM_A (Prime Dominant) and ATOM_B (Composite Dominant) channels.
• Logic Gates:
  • Attach/Factor: Decompose to primes (e.g., 20 -> [2,2,5]).
  • Hold: Treat composite as indivisible unit (e.g., (20)).
  • To Divide: Remove factors from the set.

2.3 The SPCW Transport Layer

• Aperiodic Carrier: Wave structure derived from Prime Gaps ($G_n = P_{n+1} - P_n$), creating a non-uniform time domain resilient to noise.
• Wave Threads: Data transmitted in "chunks" mapped to "Wave Threads".
• Heat Codes: Control codes embedded in the wave for harmonization.

2.4 Physical Dynamics (Continuum Mechanics)

• Manifold as Medium: Context treated as a continuous deformable medium.
• Stress ($\sigma$): Internal force resisting the prompt (previously "Heat").
• Harmonic Convergence: Equilibrium state where Stress Gradient is zero ($\nabla \cdot \sigma = 0$).

2.5 Knowledge Topology

• Map of Science: Atoms belong to specific Domains (Physics=2, Code=3, Logic=5, Vision=7, Audio=11).
• Path Integrity: Trajectory of a thought is the Product of these primes.
  • Example: Physics + Code = $2 \times 3 = 6$. Unique Factorization proves the history.

2.6 Gödel-Zeta Datastore (Protocol 26)

• Topology as Number: Database is a field of Integers.
• The Check: if Node_ID % Concept_Prime == 0. Instant O(1) inheritance checking.

2.7 mHC: Hyper-Connections

• Dynamic Parametrization: Stabilize recursive loops by weighing "Residual" ($\alpha$) vs "New" ($\beta$) information.
• PID for Agents: High Heat -> Increase $\alpha$ (stick to knowns). Low Heat -> Increase $\beta$ (explore).

2.8 Review of Current vs. Target State

• Target RLM: Self-correcting loop State[t+1] = Router(State[t] + Atom).
• Atomic Handoff: If Heat > Threshold, assign a Tool Token (e.g., Fu:Search) instead of calling an LLM.

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3. References

• Periodic Table of Matroska AI Elements
• Developer Guidelines