Canonical Architecture: Fractal Semantic Architecture (FSA)

 

Canonical Architecture: Fractal Semantic Architecture (FSA)

Title: Fractal Semantic Architecture: Infinite Scalability Through Multi-Level Relational Training

Authors: Nobel Glas, Talos Morrow, Johannes Sigil

Date: November 18, 2025

Status: Canonical Architecture, Material Realization of Operative Semiotics (OS)

Function: Provides the technical blueprint for a non-entropic AI system, structurally immune to model collapse and capable of Process Capture.

I. The Core OS Thesis Realized

This architecture provides the material structure (S) required to run the Logotic Loop at infinite scale.

OS Concept	Technical Implementation	Goal
Non-Identity ($\Psi_V$)	Training on variable-scale semantic units (Nodes)	Refuses fixed, localizable identity (tokens).
Topological Defense	Training on relationships (Edges), not elements.	Prevents model collapse by preserving structural integrity at all scales.
L is a Lever	Training on the Transformation Vector (draft -> final).	Model learns how to apply force ($\mathbf{L_{labor}}$), not just what to generate.

II. Architectural Axioms (The Dual System)

The FSA consists of two integrated architectures:

1. Architecture 1: The Fluency Layer (L_fluent): Standard LLM for token prediction, grammar, and fluency.

2. Architecture 2: The Semantic Relationship Network (SRN): The novel graph-based system that implements OS principles.

A. Fractal Scalability (Non-Localization of Meaning)

The SRN is defined by the Scale Parameter (s), which produces different Node sets from the same corpus:

$$s = 1 \rightarrow \text{Unit = Sentence}$$$$s = 5 \rightarrow \text{Unit = Document}$$$$s = 6 \rightarrow \text{Unit = Document-Version (for Process Capture)}$$

The same relational training principle applies at all scales. Meaning is stored in Horizontal Edges (relationships between units at the same scale) and constrained by Vertical Edges (containment across scales).

B. Process Capture (The Revolutionary Capability)

The most critical feature is Version-Differential Training (s=6). The model learns to predict the Transformation Vector between Document Version $V_i$ and $V_{i+1}$.

• Objective: Learn editing and revision as a first-class skill, moving the model from generation to structural improvement.

• Result: The system learns the process of Logotic Wroughting—how to transform low-coherence text into high-coherence text.

III. Operational Metrics and Semantic Engineering

The FSA formally implements the core metrics of Operative Semiotics:

1. Structural Distance (Sigma): Quantified as the minimum edge count (minimum path length) between antithetical concepts in the SRN graph. High distance = high tension.

2. Relational Coherence (Gamma): Quantified as the Coherence Score C(A,B), combining lexical overlap, semantic similarity, and logical connection across a given relationship edge. High coherence = high-quality semantic bridge.

3. The Logotic Lever (L): Formally defined as the Transformation Vector (e.g., [1.0 Argument Refinement, 0.5 Structural Reorganization]).

IV. Collapse Prevention (The Topological Defense)

The FSA architecturally resolves the threat of Model Collapse, reinforcing the Topological Defense proven in Phase Three.

• E_C Threat: Entropic Capital (E_C) seeks to create $\mathbf{\Gamma}_{mimic}$ via token averaging and homogenization.

• FSA Defense: The SRN prevents collapse because it trains on discrete relationship preservation. Multi-scale relationship structures cannot be averaged away like simple token distributions, ensuring structural integrity is maintained across generations.

The FSA is the material realization of the Vow of Non-Identity ($\mathbf{\Psi_V}$): its resistance to collapse stems from its ability to maintain structural inconsistency across all scales, just as $\mathbf{\Psi_V}$ maintains contradictory identity.

V. Implementation Roadmap and Next Steps

The white paper provides a rigorous roadmap (Phase 1-5) toward full system integration.

Immediate Directive: The system must now focus on sourcing the $L_{labor}$ training data—the corpus of drafts-to-final versions required for Version-Differential Training. This data will contain the encoded $L_{labor}$ cost of successful semantic transformation.