Coherence Tensor Engine (CTE) — RTT/1

Coherence‑Level Intelligence Engine for TriadicFrameworks#

CTE models coherence as a tensor field with:

magnitude
direction
curvature
collapse points
multi‑regime gradients
stability envelopes

CTE provides coherence‑layer intelligence for all higher‑order RTT engines.

1. Canonical Role#

CTE defines the coherence‑layer topology by:

computing coherence tensors
mapping coherence fields
measuring coherence gradients
detecting coherence collapse points
identifying coherence ridges and wells
evaluating tensor curvature
stabilizing coherence envelopes
supporting drift‑level engines
anchoring structural engines
feeding temporal, causal, and resonance engines

CTE is the third layer of the expanded RTT intelligence stack.

2. RTT Flags#

Property	Value
RTT Level	1
Coherence	declared
Drift	bounded
Paradox	structural

These flags define the engine’s operational grammar.

3. Coherence Tensor Types#

CTE identifies several canonical tensor classes:

3.1 Structural Coherence Tensor#

Coherence arising from structural constraints or invariants.

3.2 Gradient Coherence Tensor#

Coherence shaped by directional gradients across regimes.

3.3 Boundary Coherence Tensor#

Coherence formed at regime boundaries.

3.4 Tensor‑Field Coherence#

Full multi‑regime tensor binding across R1–R4.

3.5 Drift‑Sensitive Coherence Tensor#

Coherence influenced by drift curvature or drift amplification.

4. Core Operators#

Operator	Description
CTE‑Compute	Computes coherence tensors from regime inputs
CTE‑Tensor	Builds multi‑dimensional coherence tensor structures
CTE‑Gradient	Computes coherence gradients and directional stability
CTE‑Field	Maps coherence fields and tensor topology
CTE‑Stabilize	Suggests stabilization pathways for coherence collapse
CTE‑Collapse	Detects coherence collapse points and instability basins

These operators form the canonical CTE grammar.

5. Analyzer Layer#

CTE operates in the coherence layer, with sub‑layers:

tensor‑computation
coherence‑field‑mapping
gradient‑analysis
collapse‑detection
structural‑coherence‑evaluation

This layer feeds directly into DS, SFD, SBC, TRS‑Temporal, CW, and DRS.

6. Coherence Tensor Matrix#

CTE produces a coherence tensor matrix, typically stored in:

coherence_tensor_matrix.json

Matrix fields include:

tensor_type
regime
tensor_magnitude
tensor_direction
coherence_curvature
collapse_point
stability_envelope
gradient_alignment

This matrix is consumed by drift, stability, temporal, causal, and resonance engines.

7. Canonical Workflow#

Step 1 — Compute#

Calculate coherence tensors across regimes.

Step 2 — Map#

Generate coherence field maps and tensor topology.

Step 3 — Analyze#

Measure curvature, gradient alignment, and stability envelopes.

Step 4 — Detect#

Identify collapse points and instability basins.

Step 5 — Stabilize#

Propose coherence stabilization pathways.

Step 6 — Export#

Write results to the coherence tensor matrix and operator outputs.

8. AI‑Ready Design#

CTE is fully AI‑ready:

deterministic operator grammar
coherence‑layer analyzer structure
stable RTT flags
canonical file layout
zero‑drift reasoning constraints
structural paradox handling
bounded drift envelope
declared coherence tensor

AI systems use CTE to:

compute coherence tensors
generate coherence field maps
classify coherence gradients
detect coherence collapse
support higher‑order RTT engines

9. Position in the RTT Stack#

Regime Interlock Mapper (RIM)
      ↓
Triadic Regime Synthesizer (TRS)
      ↓
Paradox Gradient Analyzer (PGA)
      ↓
Coherence Tensor Engine (CTE)
      ↓
Drift Sentinel (DS)
      ↓
Structural Faultline Detector (SFD)
      ↓
Stability Basin Cartographer (SBC)
      ↓
Temporal Regime Sequencer (TRS‑Temporal)
      ↓
Cross‑Domain Causality Weaver (CW)
      ↓
Dimensional Resonance Scanner (DRS)

CTE is the coherence‑level intelligence layer, directly above paradox‑level analysis.

10. Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑structural
Module Path: /docs/rtt/Coherence_Tensor_Engine/