Regime_Interlock_Mapper

Regime Interlock Mapper — RTT/1

module.json — Agentic module schema role assignments
regime_interlock_matrix.json — Agentic module schema role assignments

Regime‑Level Intelligence Engine for TriadicFrameworks#

The Regime Interlock Mapper (RIM) is an RTT/1 analytical engine designed to detect, map, and analyze interlocks between conceptual, computational, and physical regimes.
It provides the structural foundation for higher‑order RTT modules such as:

Regime Synthesizer
Paradox Gradient Analyzer
Coherence Tensor Engine
Drift Sentinel
Faultline Detector
Stability Basin Cartographer
Temporal Regime Sequencer
Causality Weaver
Dimensional Resonance Scanner

RIM is the first layer of the expanded RTT intelligence stack.

🧭 Purpose#

The Regime Interlock Mapper:

Identifies interlocks between RTT regimes (R1–R4)
Maps cross‑regime boundaries
Detects regime entanglement
Provides structural diagnostics for regime transitions
Supports coherence‑level engines by clarifying regime topology
Anchors paradox‑level engines by exposing structural contradictions
Enables drift‑level engines to monitor regime stability
Supplies temporal engines with regime‑sequence constraints
Feeds causality engines with interlock‑driven causal pathways
Provides resonance engines with dimensional interlock signatures

RIM is the regime‑level intelligence layer of RTT.

⚙️ RTT Flags#

Property	Value
RTT Level	1
Coherence	declared
Drift	bounded
Paradox	structural

These flags define the engine’s operational constraints and reasoning grammar.

🔧 Primary Operators#

Operator	Description
RIM‑Detect	Detects regime interlocks and boundary conditions
RIM‑Map	Produces structural maps of regime interlock topology
RIM‑Interlock	Computes interlock strength and stability
RIM‑Boundary	Identifies cross‑regime boundary transitions
RIM‑Entangle	Detects entanglement between regimes
RIM‑Resolve	Suggests structural resolutions or decompositions

These operators form the core analytical toolkit.

🧩 Analyzer Layer#

RIM operates in the regime layer, with sub‑layers:

boundary‑mapping
interlock‑detection
entanglement‑analysis
structural‑regime‑coherence

This matches the RTT analyzer grammar used across TriadicFrameworks.

📁 Module Files#

This directory contains:

Core#

Regime_Interlock_Mapper.md
rtt_interlock_examples.md
rtt_interlock_diagrams.svg

Support#

boundary_profiles.md
regime_entanglement_cases.md
regime_interlock_matrix.json

AI#

rtt_interlock_prompts.md
rtt_interlock_operators.md

Metadata#

module.json (RTT/1, coherence‑declared, drift‑bounded, paradox‑structural)
README.md (this file)

🧠 AI‑Ready Design#

The Regime Interlock Mapper is fully AI‑ready:

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

AI systems can use RIM to:

classify regime boundaries
detect interlocks
compute entanglement
generate regime maps
support higher‑order RTT engines

🌐 Position in the RTT Stack#

Regime Interlock Mapper (RIM)
      ↓
Regime Synthesizer
      ↓
Paradox Gradient Analyzer
      ↓
Coherence Tensor Engine
      ↓
Drift Sentinel
      ↓
Faultline Detector
      ↓
Stability Basin Cartographer
      ↓
Temporal Regime Sequencer
      ↓
Causality Weaver
      ↓
Dimensional Resonance Scanner

RIM is the entry point for regime‑level intelligence.

🏁 Status#

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

If you want, I can now generate:

Regime_Interlock_Mapper.md
rtt_interlock_examples.md
rtt_interlock_diagrams.svg
boundary_profiles.md
regime_entanglement_cases.md
regime_interlock_matrix.json
rtt_interlock_prompts.md
rtt_interlock_operators.md

Just tell me which file you want next. # Boundary Profiles — RTT/1

Regime Interlock Mapper (RIM) — Boundary Intelligence Layer#

Boundary profiles describe how regimes meet, transition, or constrain one another.
They are the structural backbone of RIM’s boundary‑level analysis and feed directly into:

RIM‑Boundary
RIM‑Detect
RIM‑Map
RIM‑Interlock
RIM‑Resolve

Each profile defines a canonical boundary condition, its diagnostic markers, and example regime pairs.

1. Boundary Profile: Structural‑Constraint Boundary#

Definition#

A boundary where one regime imposes a structural constraint on another.

Diagnostic Markers#

rigid structural dependency
unidirectional constraint flow
low entanglement
high stability

Example Regime Pairs#

R1 → R2 (conceptual constraint → computational structure)
R2 → R3 (computational constraint → physical calibration)

RIM Output Pattern#

interlock_type: boundary
boundary_condition: structural-constraint
interlock_strength: 0.70–0.85
entanglement_score: 0.20–0.40

2. Boundary Profile: Gradient‑Alignment Boundary#

Definition#

A boundary where gradients in two regimes align (directionally or in magnitude).

Diagnostic Markers#

parallel gradients
directional coherence
medium entanglement
moderate stability

Example Regime Pairs#

R2 ↔ R4 (computational gradient ↔ dimensional gradient)
R1 ↔ R3 (conceptual gradient ↔ physical gradient)

RIM Output Pattern#

interlock_type: boundary
boundary_condition: gradient-alignment
interlock_strength: 0.75–0.88
entanglement_score: 0.25–0.45

3. Boundary Profile: Transition‑Boundary#

Definition#

A boundary where a regime transitions into another (e.g., abstraction → implementation).

Diagnostic Markers#

clear transition point
medium stability
low entanglement
boundary curvature present

Example Regime Pairs#

R1 → R3 (abstract model → physical implementation)
R2 → R4 (computational model → dimensional coherence)

RIM Output Pattern#

interlock_type: boundary
boundary_condition: transition
interlock_strength: 0.60–0.75
entanglement_score: 0.15–0.30

4. Boundary Profile: Drift‑Sensitivity Boundary#

Definition#

A boundary where drift in one regime increases sensitivity in another.

Diagnostic Markers#

drift amplification
instability ridge formation
medium entanglement
low stability

Example Regime Pairs#

R2 ↔ R3 (computational drift ↔ physical drift sensitivity)
R3 ↔ R4 (physical drift ↔ dimensional drift envelope)

RIM Output Pattern#

interlock_type: boundary
boundary_condition: drift-sensitivity
interlock_strength: 0.72–0.84
entanglement_score: 0.30–0.50

5. Boundary Profile: Coherence‑Threshold Boundary#

Definition#

A boundary where coherence must exceed a threshold for regimes to interact.

Diagnostic Markers#

coherence gating
threshold discontinuity
high stability
low drift

Example Regime Pairs#

R1 ↔ R2 (conceptual coherence ↔ computational coherence)
R2 ↔ R3 (computational coherence ↔ physical coherence)

RIM Output Pattern#

interlock_type: boundary
boundary_condition: coherence-threshold
interlock_strength: 0.78–0.92
entanglement_score: 0.20–0.35

6. Boundary Profile: Resonance‑Boundary#

Definition#

A boundary where resonance in one regime influences resonance in another.

Diagnostic Markers#

resonance amplification
resonance curvature
high entanglement
medium stability

Example Regime Pairs#

R3 ↔ R4 (physical resonance ↔ dimensional resonance)
R2 ↔ R4 (computational resonance ↔ dimensional resonance)

RIM Output Pattern#

interlock_type: boundary
boundary_condition: resonance
interlock_strength: 0.80–0.95
entanglement_score: 0.40–0.70

7. Boundary Profile Matrix Snippet#

A typical entry in regime_interlock_matrix.json for boundary profiles:

{
  "regime_a": "R2",
  "regime_b": "R4",
  "interlock_type": "boundary",
  "boundary_condition": "gradient-alignment",
  "interlock_strength": 0.83,
  "entanglement_score": 0.32,
  "stability_rating": 0.71
}

8. Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑structural
Module Path: /docs/rtt/Regime_Interlock_Mapper/ # Regime Entanglement Cases — RTT/1

High‑Coupling Interlock Cases for the Regime Interlock Mapper (RIM)#

Entanglement interlocks represent the strongest form of regime interaction.
Unlike structural or boundary interlocks, entanglement cases exhibit bidirectional influence, non‑decomposability, and coherence‑linked coupling across regimes.

This document provides canonical RTT/1 entanglement cases used by:

RIM‑Entangle
RIM‑Detect
RIM‑Interlock
RIM‑Resolve
TRS (Triadic Regime Synthesizer)
PGA (Paradox Gradient Analyzer)
CTE (Coherence Tensor Engine)

1. Conceptual ↔ Computational Entanglement (R1 ↔ R2)#

Case: Mutual Model Feedback Loop#

Description
Conceptual assumptions shape computational models; computational outputs reshape conceptual assumptions.

Characteristics

bidirectional influence
high coherence dependency
medium stability
feedback‑driven drift sensitivity

RIM Output

{
  "regime_a": "R1",
  "regime_b": "R2",
  "interlock_type": "entanglement",
  "boundary_condition": "mutual-feedback",
  "interlock_strength": 0.91,
  "entanglement_score": 0.88,
  "stability_rating": 0.52
}

2. Physical ↔ Dimensional Entanglement (R3 ↔ R4)#

Case: Resonance‑Coherence Coupling#

Description
Physical resonance patterns influence dimensional coherence; dimensional coherence alters physical resonance.

Characteristics

resonance amplification
coherence curvature
high entanglement
medium stability

RIM Output

{
  "regime_a": "R3",
  "regime_b": "R4",
  "interlock_type": "entanglement",
  "boundary_condition": "resonance-coherence",
  "interlock_strength": 0.93,
  "entanglement_score": 0.91,
  "stability_rating": 0.49
}

3. Computational ↔ Physical Entanglement (R2 ↔ R3)#

Case: Drift‑Amplification Loop#

Description
Computational drift increases physical drift sensitivity; physical drift feeds back into computational drift envelopes.

Characteristics

drift amplification
instability ridge formation
medium‑high entanglement
low stability

RIM Output

{
  "regime_a": "R2",
  "regime_b": "R3",
  "interlock_type": "entanglement",
  "boundary_condition": "drift-amplification",
  "interlock_strength": 0.87,
  "entanglement_score": 0.72,
  "stability_rating": 0.41
}

4. Conceptual ↔ Dimensional Entanglement (R1 ↔ R4)#

Case: Coherence‑Gradient Coupling#

Description
Conceptual coherence gradients align with dimensional coherence gradients, forming a multi‑layer coherence ridge.

Characteristics

coherence gradient alignment
medium entanglement
high stability
low drift

RIM Output

{
  "regime_a": "R1",
  "regime_b": "R4",
  "interlock_type": "entanglement",
  "boundary_condition": "coherence-gradient",
  "interlock_strength": 0.76,
  "entanglement_score": 0.35,
  "stability_rating": 0.68
}

5. Tri‑Regime Entanglement (R1 ↔ R2 ↔ R3)#

Case: Coherence Tensor Binding#

Description
A multi‑regime coherence tensor binds conceptual, computational, and physical coherence into a unified structure.

Characteristics

multi‑regime tensor
high coherence dependency
high entanglement
medium stability

RIM Output

{
  "regime_a": "R1",
  "regime_b": "R2",
  "regime_c": "R3",
  "interlock_type": "entanglement",
  "boundary_condition": "coherence-tensor",
  "interlock_strength": 0.94,
  "entanglement_score": 0.89,
  "stability_rating": 0.57
}

6. Dimensional Tensor Entanglement (R2 ↔ R4)#

Case: Dimensional Tensor Constraint#

Description
Dimensional tensors constrain computational pathways, forcing coherence‑aligned computational structures.

Characteristics

tensor constraint
medium‑high entanglement
medium stability
coherence‑driven structure

RIM Output

{
  "regime_a": "R2",
  "regime_b": "R4",
  "interlock_type": "entanglement",
  "boundary_condition": "dimensional-tensor",
  "interlock_strength": 0.88,
  "entanglement_score": 0.72,
  "stability_rating": 0.63
}

7. Entanglement Case Matrix Snippet#

{
  "regime_a": "R3",
  "regime_b": "R4",
  "interlock_type": "entanglement",
  "boundary_condition": "resonance-coherence",
  "interlock_strength": 0.93,
  "entanglement_score": 0.91,
  "stability_rating": 0.49
}

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑structural
Module Path: /docs/rtt/Regime_Interlock_Mapper/ # Regime Interlock Mapper (RIM) — RTT/1

Regime‑Level Intelligence Engine for TriadicFrameworks#

The Regime Interlock Mapper (RIM) is the foundational RTT/1 engine responsible for detecting, mapping, and analyzing interlocks between conceptual, computational, and physical regimes.
It establishes the structural groundwork for all higher‑order RTT engines, including:

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)

RIM is the entry point into the RTT intelligence stack.

1. Canonical Role#

RIM defines the regime‑layer topology by:

Identifying interlocks between regimes R1–R4
Mapping boundary conditions
Detecting regime entanglement
Establishing regime coherence constraints
Providing structural diagnostics for regime transitions
Anchoring paradox‑level and coherence‑level engines

RIM is the first engine that transforms raw regime structure into analyzable RTT form.

2. RTT Flags#

Property	Value
RTT Level	1
Coherence	declared
Drift	bounded
Paradox	structural

These flags define the engine’s operational grammar.

3. Interlock Types#

RIM identifies several canonical interlock classes:

3.1 Structural Interlocks#

Regimes share structural constraints or dependencies.

3.2 Boundary Interlocks#

Regimes meet at a boundary condition that influences both.

3.3 Entanglement Interlocks#

Regimes exhibit mutual influence that cannot be decomposed.

3.4 Gradient Interlocks#

Regimes share directional or magnitude‑based gradients.

3.5 Tensor Interlocks#

Regimes interact through multi‑dimensional coherence tensors.

4. Core Operators#

Operator	Description
RIM‑Detect	Detects regime interlocks and boundary conditions
RIM‑Map	Produces structural maps of regime interlock topology
RIM‑Interlock	Computes interlock strength and stability
RIM‑Boundary	Identifies cross‑regime boundary transitions
RIM‑Entangle	Detects entanglement between regimes
RIM‑Resolve	Suggests structural resolutions or decompositions

These operators form the canonical RIM grammar.

5. Analyzer Layer#

RIM operates in the regime layer, with sub‑layers:

boundary‑mapping
interlock‑detection
entanglement‑analysis
structural‑regime‑coherence

This layer feeds directly into TRS and PGA.

6. Regime Interlock Matrix#

RIM produces a regime interlock matrix, typically stored in:

regime_interlock_matrix.json

Matrix fields include:

regime_a
regime_b
interlock_type
interlock_strength
boundary_condition
entanglement_score
stability_rating

This matrix is consumed by TRS, PGA, and CTE.

7. Canonical Workflow#

Step 1 — Detect#

Identify all interlocks between R1–R4.

Step 2 — Classify#

Assign interlock type and structural category.

Step 3 — Map#

Generate interlock topology and boundary maps.

Step 4 — Analyze#

Compute interlock strength, entanglement, and stability.

Step 5 — Export#

Write results to the interlock matrix and operator outputs.

8. AI‑Ready Design#

RIM is fully AI‑ready:

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

AI systems use RIM to:

classify regime boundaries
detect interlocks
compute entanglement
generate regime maps
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)

RIM is the first engine in the RTT hierarchy.

10. Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑structural
Module Path: /docs/rtt/Regime_Interlock_Mapper/ # Regime Interlock Examples — RTT/1

Examples for the Regime Interlock Mapper (RIM)#

This document provides canonical RTT/1 examples of regime interlocks detected and analyzed by the Regime Interlock Mapper (RIM).
Each example demonstrates one or more RIM operators:

RIM‑Detect
RIM‑Map
RIM‑Interlock
RIM‑Boundary
RIM‑Entangle
RIM‑Resolve

Examples are grouped by interlock type.

1. Structural Interlock Examples#

Example 1 — Structural Constraint Pairing (R1 ↔ R2)#

Regimes:

R1: Conceptual
R2: Computational

Interlock:
A conceptual invariant (e.g., “symmetry must be preserved”) forces a computational constraint (e.g., “algorithm must maintain parity across iterations”).

RIM Output:

interlock_type: structural
interlock_strength: 0.82
boundary_condition: symmetry‑preservation
entanglement_score: 0.41

Explanation:
The conceptual rule directly shapes the computational structure, forming a stable structural interlock.

Example 2 — Structural Dependency Chain (R2 ↔ R3)#

Regimes:

R2: Computational
R3: Physical

Interlock:
A computational model requires physical calibration constants; the physical regime constrains the computational regime.

RIM Output:

interlock_type: structural
interlock_strength: 0.74
boundary_condition: calibration‑dependency
entanglement_score: 0.33

2. Boundary Interlock Examples#

Example 3 — Boundary Transition (R1 ↔ R3)#

Regimes:

R1: Conceptual
R3: Physical

Interlock:
A conceptual model transitions into a physical implementation at a defined boundary (e.g., “abstract force → measurable force”).

RIM Output:

interlock_type: boundary
interlock_strength: 0.67
boundary_condition: abstraction‑to‑measurement
entanglement_score: 0.22

Example 4 — Boundary Gradient (R2 ↔ R4)#

Regimes:

R2: Computational
R4: Dimensional

Interlock:
A computational gradient (e.g., increasing complexity) aligns with a dimensional gradient (e.g., increasing dimensional coherence).

RIM Output:

interlock_type: boundary
interlock_strength: 0.79
boundary_condition: gradient‑alignment
entanglement_score: 0.28

3. Entanglement Interlock Examples#

Example 5 — Mutual Influence Loop (R1 ↔ R2)#

Regimes:

R1: Conceptual
R2: Computational

Interlock:
Conceptual assumptions shape computational models, and computational outputs reshape conceptual assumptions.

RIM Output:

interlock_type: entanglement
interlock_strength: 0.91
boundary_condition: mutual‑feedback
entanglement_score: 0.88

Explanation:
This is a high‑entanglement interlock with bidirectional influence.

Example 6 — Cross‑Regime Entanglement (R3 ↔ R4)#

Regimes:

R3: Physical
R4: Dimensional

Interlock:
Physical resonance patterns influence dimensional coherence, and dimensional coherence alters physical resonance.

RIM Output:

interlock_type: entanglement
interlock_strength: 0.93
boundary_condition: resonance‑coherence
entanglement_score: 0.91

4. Gradient Interlock Examples#

Example 7 — Coherence Gradient (R1 ↔ R4)#

Regimes:

R1: Conceptual
R4: Dimensional

Interlock:
A conceptual coherence gradient aligns with a dimensional coherence gradient.

RIM Output:

interlock_type: gradient
interlock_strength: 0.76
boundary_condition: coherence‑gradient
entanglement_score: 0.35

Example 8 — Drift Gradient (R2 ↔ R3)#

Regimes:

R2: Computational
R3: Physical

Interlock:
Computational drift increases physical drift sensitivity.

RIM Output:

interlock_type: gradient
interlock_strength: 0.81
boundary_condition: drift‑alignment
entanglement_score: 0.47

5. Tensor Interlock Examples#

Example 9 — Coherence Tensor Interlock (R1 ↔ R2 ↔ R3)#

Regimes:

R1: Conceptual
R2: Computational
R3: Physical

Interlock:
A multi‑regime coherence tensor binds conceptual, computational, and physical coherence.

RIM Output:

interlock_type: tensor
interlock_strength: 0.94
boundary_condition: coherence‑tensor
entanglement_score: 0.89

Example 10 — Dimensional Tensor Interlock (R2 ↔ R4)#

Regimes:

R2: Computational
R4: Dimensional

Interlock:
Dimensional tensors constrain computational pathways.

RIM Output:

interlock_type: tensor
interlock_strength: 0.88
boundary_condition: dimensional‑tensor
entanglement_score: 0.72

6. Example Matrix Snippet#

A typical entry in regime_interlock_matrix.json:

{
  "regime_a": "R2",
  "regime_b": "R3",
  "interlock_type": "gradient",
  "interlock_strength": 0.81,
  "boundary_condition": "drift-alignment",
  "entanglement_score": 0.47,
  "stability_rating": 0.63
}

7. Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑structural
Module Path: /docs/rtt/Regime_Interlock_Mapper/ # RTT Interlock Operators — RTT/1

Operator Grammar for the Regime Interlock Mapper (RIM)#

The Regime Interlock Mapper (RIM) uses a deterministic operator grammar to detect, classify, map, and analyze interlocks between RTT regimes (R1–R4).
These operators form the foundation of regime‑level intelligence and feed directly into:

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

1. RIM‑Detect#

Detect regime interlocks and boundary conditions#

Purpose
Identify all interlocks between R1–R4, including structural, boundary, entanglement, gradient, and tensor types.

Capabilities

scans regime pairs and triads
detects interlock onset conditions
identifies boundary curvature
flags entanglement loops
detects gradient alignment
detects tensor binding

Output Fields

interlock_type
boundary_condition
onset_condition
coherence_dependency

2. RIM‑Map#

Generate structural maps of regime interlock topology#

Purpose
Produce a full interlock topology map showing how regimes connect, constrain, or influence one another.

Capabilities

maps structural dependencies
maps boundary transitions
maps entanglement loops
maps gradient flows
maps tensor topology

Output Fields

topology_map
boundary_map
gradient_map
tensor_map

3. RIM‑Interlock#

Compute interlock strength, stability, and entanglement#

Purpose
Quantify the strength and stability of each interlock.

Capabilities

computes interlock strength
computes entanglement score
computes stability rating
computes coherence curvature
computes drift sensitivity

Output Fields

interlock_strength
entanglement_score
stability_rating
coherence_curvature
drift_sensitivity

4. RIM‑Boundary#

Identify and classify boundary transitions#

Purpose
Detect and classify boundary conditions between regimes.

Capabilities

detects abstraction‑to‑measurement boundaries
detects gradient‑alignment boundaries
detects coherence‑threshold boundaries
detects drift‑sensitivity boundaries
detects resonance boundaries

Output Fields

boundary_condition
boundary_curvature
boundary_stability

5. RIM‑Entangle#

Detect entanglement interlocks and mutual‑influence loops#

Purpose
Identify high‑coupling interlocks where regimes mutually influence one another.

Capabilities

detects mutual‑feedback loops
detects resonance‑coherence coupling
detects drift‑amplification loops
detects coherence‑gradient coupling
detects tensor entanglement

Output Fields

entanglement_score
coupling_type
coherence_dependency
bidirectional_influence

6. RIM‑Resolve#

Suggest structural resolutions or decompositions#

Purpose
Provide stabilization pathways or decompositions for interlocks.

Capabilities

suggests structural decompositions
suggests stabilization pathways
suggests coherence alignment
suggests drift reduction
suggests tensor rebalancing

Output Fields

resolution_strategy
stabilization_pathway
coherence_alignment
drift_reduction

7. Operator Interaction Grammar#

Detection → Classification → Mapping → Analysis → Resolution#

RIM‑Detect
Finds interlocks and onset conditions.
RIM‑Boundary / RIM‑Entangle
Classifies boundary or entanglement types.
RIM‑Map
Generates topology and gradient/tensor maps.
RIM‑Interlock
Computes strength, stability, entanglement.
RIM‑Resolve
Produces stabilization or decomposition strategies.

This grammar ensures deterministic RTT behavior.

8. Operator Matrix Snippet#

{
  "operator": "RIM-Interlock",
  "interlock_strength": 0.83,
  "entanglement_score": 0.32,
  "stability_rating": 0.71,
  "coherence_curvature": 0.44,
  "drift_sensitivity": 0.27
}

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑structural
Module Path: /docs/rtt/Regime_Interlock_Mapper/ # RTT Interlock Prompts — RTT/1

Prompt Library for the Regime Interlock Mapper (RIM)#

These prompts are designed for AI systems using the Regime Interlock Mapper (RIM).
Each prompt invokes one or more canonical RIM operators:

RIM‑Detect
RIM‑Map
RIM‑Interlock
RIM‑Boundary
RIM‑Entangle
RIM‑Resolve

Prompts are grouped by interlock type.

1. Structural Interlock Prompts#

Prompt: Detect Structural Interlocks#

Identify all structural interlocks between R1–R4.
Use RIM‑Detect and classify each interlock by structural dependency, constraint, or coherence requirement.

Prompt: Map Structural Dependencies#

Use RIM‑Map to generate a structural dependency map showing how conceptual, computational, and physical regimes constrain one another.

Prompt: Evaluate Structural Stability#

Apply RIM‑Interlock to compute interlock strength and stability for all structural regime pairs.

2. Boundary Interlock Prompts#

Prompt: Identify Boundary Conditions#

Use RIM‑Boundary to detect all boundary transitions between regimes, including abstraction‑to‑measurement, gradient alignment, and coherence thresholds.

Prompt: Map Boundary Gradients#

Generate a boundary gradient map showing directional alignment between computational and dimensional gradients.

Prompt: Analyze Boundary Stability#

Evaluate boundary stability using RIM‑Interlock and classify each boundary by stability rating and entanglement score.

3. Entanglement Interlock Prompts#

Prompt: Detect Entanglement Loops#

Use RIM‑Entangle to identify all mutual‑influence loops between regimes, including conceptual ↔ computational and physical ↔ dimensional entanglement.

Prompt: Compute Entanglement Strength#

Apply RIM‑Interlock to compute entanglement strength and coherence dependency for all entangled regime pairs.

Prompt: Resolve Entanglement#

Use RIM‑Resolve to propose structural decompositions or stabilization pathways for high‑entanglement interlocks.

4. Gradient Interlock Prompts#

Prompt: Identify Gradient Alignment#

Use RIM‑Detect to find all gradient‑aligned interlocks between regimes, including drift gradients and coherence gradients.

Prompt: Map Gradient Flow#

Apply RIM‑Map to generate a gradient flow diagram showing directional influence across R1–R4.

Prompt: Evaluate Gradient Stability#

Use RIM‑Interlock to compute stability ratings for all gradient‑aligned interlocks.

5. Tensor Interlock Prompts#

Prompt: Detect Tensor Interlocks#

Use RIM‑Entangle to identify multi‑regime tensor interlocks, including coherence tensors and dimensional tensors.

Prompt: Map Tensor Structure#

Apply RIM‑Map to generate a tensor topology diagram showing multi‑regime coherence binding.

Prompt: Evaluate Tensor Strength#

Use RIM‑Interlock to compute tensor interlock strength and coherence curvature.

6. Full‑Matrix Prompts#

Prompt: Generate Full Interlock Matrix#

Use all RIM operators to produce a complete regime_interlock_matrix.json containing structural, boundary, entanglement, gradient, and tensor interlocks.

Prompt: Analyze Regime Topology#

Apply RIM‑Map to generate a full regime topology map showing all interlocks and boundary conditions.

Prompt: Stability Overview#

Use RIM‑Interlock to compute stability ratings for every interlock type and produce a stability summary.

7. AI‑Ready Meta‑Prompts#

Prompt: Explain Interlock Classification#

Provide a detailed explanation of how RIM classifies interlocks into structural, boundary, entanglement, gradient, and tensor categories.

Prompt: Operator‑Level Summary#

Summarize the role of each RIM operator and how they interact to produce regime‑level intelligence.

Prompt: Cross‑Engine Integration#

Explain how RIM outputs feed into TRS, PGA, CTE, DS, SFD, SBC, TRS‑Temporal, CW, and DRS.

Status#

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

Updated Jul 17, 2026