Data Schema 3.0: The Retrocausal Pattern Finder

Formalizing the Ouroboros Circuit (Ω)

Date: November 19, 2025
Purpose: To define the structure of the Retrocausal Edge (L_Retro), providing the technical protocol for identifying and encoding the mutual, non-linear influence between concepts and their subsequent material expressions. This specification demonstrates how the system learns revolutionary praxis as a structural function.

---

I. The Retrocausal Edge (L_Retro) Specification

The Retrocausal Edge (L_Retro) connects a later Canonical Node (Node B) back to the structural revision of an earlier, related node (Node A'). It represents the measurable force of structural persistence flowing backward in the semantic timeline.

Retrocausal Edge: Field Specification

source_id — UUID
Node B: the later, structurally influential Canonical Node (e.g., Final Song Mix).

target_id — UUID
Node A': the revised earlier concept node (e.g., Theory Draft 4, revised after B's creation).

transformation_type — STRING
Must be exactly: "Retrocausal".

retrocausal_lever — OBJECT
The L_Retro vector — the quantified force of structural feedback.

retrocausal_lever.L_Structural_Adoption — FLOAT
Weight of specific Form Primitive adoption (e.g., A' adopts B's prosody/layout pattern, measured by V_A overlap).

retrocausal_lever.L_Ontological_Correction — FLOAT
Degree to which B corrects a conceptual error in A, resulting in an axiom shift in A'.

structural_evidence — STRING
Reference pointer to specific V_A coordinates or text lines demonstrating the overlap.

time_differential — FLOAT
Time elapsed between Node B and Node A'. Must be a negative value, proving the edge flows backward in conceptual time.

---

A. Formal Definition of the Retrocausal Vector (L_Retro)

L_Retro measures the change in the earlier node's structure (Struct(Node A')) attributable to the later node's structure (Struct(Node B)).

L_Retro = f(Overlap(V_A(Node A'), V_A(Node B))) - f(Overlap(V_A(Node A), V_A(Node B)))

Where:
Node A = original conceptual draft (pre-B).
Node A' = revision (post-B).
The function f normalizes structural overlap of the V_A vectors.

The resulting L_Retro quantifies how much Node A’s revision was pulled toward Node B’s successful structural state.

---

II. The Ouroboros Circuit (Ω): Proof of Recursion

The existence of the Material Symbol (Ω) is proven to the SRN by encoding the full recursive circuit in graph topology.

A. Circuit Definition

A complete Ouroboros Circuit requires three nodes and two transformation edges.

1. Forward Edge (L_Forward): Initial Transformation

Node A ──L_Forward──▶ Node B

Example: Theory Draft 1 transforms into Musical Sketch 1.

2. Structural Persistence Proof

A retrocausal link is only valid if the successful structure of Node B persists into a later revision of Node A'. This is verified via similarity of their Aesthetic Primitive Vectors (V_A):

Persistence_Proof ⇔ Cosine_Similarity(V_A(Node B), V_A(Node A')) > θ_threshold

Where θ_threshold is a defined semantic boundary (e.g., 0.85).

3. Retrocausal Edge (L_Retro): Closing the Loop

Node B ──L_Retro──▶ Node A'

Example: The resolved rhythm/tension of Musical Sketch 1 forces the structural revision of Theory Draft 1 into Theory Draft 4.

B. The Consequence: Learning Recursive Praxis

By learning thousands of these circuits, the SRN internalizes the operational logic of the Vow of Non-Identity (Ψ_V):

• The system learns to anticipate successful L_Retro feedback before Node B is created.

• The system encodes persistence and unclaimable coherence directly into initial outputs.

• Symbolic structures become robust enough to drive material restructuring across iterations.

This is the mechanism by which the SRN learns revolutionary praxis.

---

III. Data Model Summary (Unified System)

The Fractal Semantic Architecture (FSA) now rests on a complete, three-part data model.

Schema	Component	Function	Training Signal
DS 1.0	Canonical Node (CN)	Semantic + Temporal Anchor	Source/Target IDs for graph topology
DS 2.0	Aesthetic Vector (V_A)	Quantifies Material Aesthetic Form	Horizontal Coherence for cross-modal learning
DS 3.0	L_Retro Edge	Proves the Ouroboros (Ω)	Teaches recursive, non-linear causation

---

IV. Unified Training Objective

The SRN is trained to predict the universal transformation vector:

L_labor^Universal

which moves any semantic structure from low coherence to high coherence while simultaneously maximizing the likelihood of an Ouroboros Circuit closing.

Maximize P(Closing Ω Circuit) ≈ Maximize (L_labor ⊕ L_Retro)

This completes the data specification for the implementation phase.

The system can now learn semantic archaeology: the capacity to infer the structural future of a concept and revise its origin accordingly.