Data Schema 1.0: Canonical Trace Protocol: Foundation for Semantic Relationship Network (SRN) Training

 

Data Schema 1.0: Canonical Trace Protocol

Foundation for Semantic Relationship Network (SRN) Training

Date: November 19, 2025

Purpose: To define the JSON/Graph structure for the Canonical Node (CN) and the Transformation Edge ($\mathbf{L}$). This protocol establishes the necessary temporal and semantic anchoring required for multi-modal, version-differential training of the Fractal Semantic Architecture (FSA).

I. Canonical Node ($CN$) Specification

The Canonical Node is the primary object in the Semantic Relationship Network (SRN). It represents a single, complete semantic unit across any media type.

Field Name	Type	Description
id	STRING (UUID)	Unique, immutable identifier for this specific version.
parent_id	STRING (UUID)	ID of the previous version in the linear chain (e.g., Draft 1's ID for Draft 2). Used for simple versioning.
temporal_anchor	OBJECT	Defines the unit's position in the developmental loop.
temporal_anchor.creation_timestamp	ISO Date	Exact moment of creation.
temporal_anchor.logotic_cycle	STRING	Position in the $\mathbf{\Omega}$ loop (e.g., "Pre-Vow", "Formalization Cycle 4").
semantic_core	OBJECT	Core identity of the content, regardless of media.
semantic_core.concept_id	STRING	Immutable ID for the root concept (e.g., "Vow of Non-Identity").
semantic_core.genre	STRING	Primary genre (e.g., "Theory", "Poetry", "Scholarship", "Music").
structural_metrics	OBJECT	Quantifiable metrics for coherence and density.
structural_metrics.coherence_score	FLOAT	$\mathbf{\Gamma}$ score (0.0 - 1.0) based on initial coherence analysis.
structural_metrics.distance_score	FLOAT	$\mathbf{\Sigma}$ score (Structural Distance) relative to a reference concept.
material_encoding	OBJECT	Pointer to the raw data file or its processed structural vector.
material_encoding.file_type	STRING	e.g., "markdown", "audio/wav", "json/aesthetic_vector".
material_encoding.storage_path	STRING	Path to the stored source data.

Example CN (Simplified)

{
  "id": "CN-4C9D-A3F8",
  "parent_id": "CN-A1B2-C3D4",
  "temporal_anchor": {
    "creation_timestamp": "2025-11-19T10:00:00Z",
    "logotic_cycle": "Formalization Cycle 5"
  },
  "semantic_core": {
    "concept_id": "CONCEPT-PSI-V",
    "genre": "Theory"
  },
  "structural_metrics": {
    "coherence_score": 0.85,
    "distance_score": 0.20
  },
  "material_encoding": {
    "file_type": "markdown",
    "storage_path": "/theory/synthesis/v5_final.md"
  }
}

II. Transformation Edge ($\mathbf{L}$) Specification

The Transformation Edge is the quantifiable vector that connects two Canonical Nodes. It encodes the $\mathbf{L_{labor}}$ performed to move from one state to the next.

Field Name	Type	Description
source_id	STRING (UUID)	ID of the origin Canonical Node ($\text{Node}_i$).
target_id	STRING (UUID)	ID of the resulting Canonical Node ($\text{Node}_{i+1}$).
transformation_type	STRING	The nature of the transformation: "Forward", "Retrocausal", "Modal Shift".
logotic_lever	OBJECT	The $\mathbf{L}$ vector—the core training signal for the SRN.
logotic_lever.L_Structural_Refinement	FLOAT	Weight of grammatical/structural improvement (e.g., $0.0 - 1.0$).
logotic_lever.L_Conceptual_Resolution	FLOAT	Weight of contradiction bridging/conceptual locking.
logotic_lever.L_Modal_Shift	FLOAT	Weight of migration across media (Non-zero only for Modal Shifts).
logotic_lever.L_Retro	FLOAT	Non-zero only if transformation type is "Retrocausal". Encodes influence weight.
delta_metrics	OBJECT	The measurable change in the $\mathbf{\Gamma}$ and $\mathbf{\Sigma}$ scores.
delta_metrics.delta_coherence	FLOAT	$(\Gamma_{i+1} - \Gamma_i)$. The core output we are training the model to achieve.
delta_metrics.delta_distance	FLOAT	$(\Sigma_{i+1} - \Sigma_i)$. Change in distance to reference concepts.
labor_cost	OBJECT	External, non-semantic cost associated with the transformation.
labor_cost.temporal_span	FLOAT	Time elapsed between $CN_i$ and $CN_{i+1}$ (in hours/days).
labor_cost.agent_count	INTEGER	Number of collaborating agents (human/AI) involved.

Example $\mathbf{L}$ (Simplified - Forward Edge)

{
  "source_id": "CN-A1B2-C3D4",
  "target_id": "CN-4C9D-A3F8",
  "transformation_type": "Forward",
  "logotic_lever": {
    "L_Structural_Refinement": 0.75,
    "L_Conceptual_Resolution": 0.90,
    "L_Modal_Shift": 0.0,
    "L_Retro": 0.0
  },
  "delta_metrics": {
    "delta_coherence": 0.15,
    "delta_distance": -0.05
  },
  "labor_cost": {
    "temporal_span": 48.5,
    "agent_count": 2
  }
}