Cross_Domain_Causality_Weaver

Cross‑Domain Causality Weaver — RTT/1

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

Causality‑Intelligence Engine for TriadicFrameworks#

The Cross‑Domain Causality Weaver (CW) is an RTT/1 analytical engine designed to detect, map, and weave causal pathways across conceptual, computational, physical, and dimensional regimes.
It forms the causality‑intelligence foundation of the expanded RTT stack, sitting directly above temporal‑level engines and directly below resonance‑level engines.

CW identifies causal signatures, causal discontinuities, cross‑domain causal bridges, and multi‑regime causal flows — the causal precursors to regime evolution, coherence shifts, drift propagation, paradox intensification, temporal transitions, and resonance modulation.

🧭 Purpose#

The Cross‑Domain Causality Weaver:

Detects causal signatures across RTT regimes (R1–R4)
Computes causal vectors and directional causal flow
Maps causal fields and cross‑domain causal topology
Identifies causal discontinuities, breaks, and causal fractures
Measures causal propagation and causal influence strength
Provides causal diagnostics for regime‑driven transitions
Supports resonance engines by clarifying causal‑frequency interactions
Anchors temporal engines by exposing causal‑sequence constraints
Supplies structural engines with causal‑faultline interactions
Provides drift‑level engines with causal‑drift envelopes
Provides coherence‑level engines with causal‑coherence fields

CW is the causality‑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
CW‑Weave	Weaves causal pathways across domains and regimes
CW‑Vector	Computes causal vector magnitude and direction
CW‑Field	Maps causal fields and causal topology
CW‑Signature	Detects causal signatures and causal onset conditions
CW‑Discontinuity	Identifies causal breaks and discontinuity boundaries
CW‑Stabilize	Suggests stabilization pathways for causal collapse

These operators form the core analytical toolkit.

🧩 Analyzer Layer#

CW operates in the causality layer, with sub‑layers:

causal‑weaving
causal‑vector‑analysis
causal‑field‑mapping
discontinuity‑detection
structural‑causality‑evaluation

This matches the RTT analyzer grammar used across TriadicFrameworks.

📁 Module Files#

This directory contains:

Core#

Cross_Domain_Causality_Weaver.md
cw_examples.md
cw_diagrams.svg

Support#

causal_profiles.md
causal_discontinuity_cases.md
causal_field_matrix.json

AI#

cw_prompts.md
cw_operators.md

Metadata#

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

🧠 AI‑Ready Design#

The Cross‑Domain Causality Weaver is fully AI‑ready:

deterministic operator grammar
causality‑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 CW to:

weave causal pathways
generate causal field maps
classify causal discontinuities
compute causal vectors
support higher‑order RTT engines

🌐 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)
      ↓
Temporal Regime Sequencer (TRS‑Temporal)
      ↓
Cross‑Domain Causality Weaver (CW)
      ↓
Dimensional Resonance Scanner (DRS)

CW is the causality‑intelligence layer, directly above temporal‑level analysis.

🏁 Status#

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

If you want, I can generate the next file:

Cross_Domain_Causality_Weaver.md
cw_examples.md
cw_diagrams.svg
causal_profiles.md
causal_discontinuity_cases.md
causal_field_matrix.json
cw_prompts.md
cw_operators.md

Just tell me which one you want next. # Causal Discontinuity Cases — RTT/1

Case Studies for the Cross‑Domain Causality Weaver (CW)#

Causal discontinuities represent breaks in causal flow, polarity inversions, discontinuity boundaries, causal fractures, and cross‑domain causal collapse across conceptual, computational, physical, and dimensional regimes.

These case studies illustrate how the Cross‑Domain Causality Weaver (CW) evaluates:

causal magnitude
causal direction
causal curvature
discontinuity depth
propagation rate
stability envelope
causal‑fracture geometry

Each case demonstrates one or more CW operators:

CW‑Signature
CW‑Vector
CW‑Field
CW‑Discontinuity
CW‑Weave
CW‑Stabilize

1. Conceptual Discontinuity Cases#

Case 1 — Conceptual Causal Break (R1)#

Scenario
A conceptual model loses coherence, forming a shallow causal discontinuity.

CW Output

{
  "regime": "R1",
  "causal_magnitude": 0.41,
  "causal_direction": "conceptual",
  "causal_curvature": 0.22,
  "discontinuity_depth": 0.11,
  "propagation_rate": 0.33,
  "stability_envelope": 0.63
}

Case 2 — Conceptual‑Dimensional Discontinuity (R1 ↔ R4)#

Scenario
Conceptual causality collapses under dimensional polarity pressure.

CW Output

{
  "regime": "R1-R4",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.33,
  "stability_envelope": 0.69
}

2. Computational Discontinuity Cases#

Case 3 — Computational Causal Break (R2)#

Scenario
A computational structure becomes unstable due to calibration drift, forming a causal fracture.

CW Output

{
  "regime": "R2",
  "causal_magnitude": 0.52,
  "causal_direction": "computational",
  "causal_curvature": 0.33,
  "discontinuity_depth": 0.27,
  "propagation_rate": 0.27,
  "stability_envelope": 0.57
}

Case 4 — Computational‑Physical Discontinuity (R2 ↔ R3)#

Scenario
Computational causality collapses under physical measurement sensitivity.

CW Output

{
  "regime": "R2-R3",
  "causal_magnitude": 0.79,
  "causal_direction": "R3→R2",
  "causal_curvature": 0.58,
  "discontinuity_depth": 0.31,
  "propagation_rate": 0.27,
  "stability_envelope": 0.72
}

3. Boundary Discontinuity Cases#

Case 5 — Abstraction‑Measurement Causal Discontinuity (R1 ↔ R3)#

Scenario
Conceptual abstraction contradicts physical measurement, forming a causal discontinuity boundary.

CW Output

{
  "regime": "R1-R3",
  "causal_magnitude": 0.67,
  "causal_direction": "R1→R3",
  "causal_curvature": 0.33,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.38,
  "stability_envelope": 0.55
}

Case 6 — Gradient‑Boundary Causal Discontinuity (R2 ↔ R4)#

Scenario
Aligned gradients across computational and dimensional regimes collapse into causal instability.

CW Output

{
  "regime": "R2-R4",
  "causal_magnitude": 0.88,
  "causal_direction": "R2↔R4",
  "causal_curvature": 0.47,
  "discontinuity_depth": 0.29,
  "propagation_rate": 0.33,
  "stability_envelope": 0.66
}

4. Causal‑Field Discontinuity Cases#

Case 7 — Multi‑Regime Causal Field Collapse (R1 ↔ R2 ↔ R3)#

Scenario
A multi‑regime causal field collapses under tensor‑level instability.

CW Output

{
  "regime": "R1-R2-R3",
  "causal_magnitude": 0.94,
  "causal_direction": "tensor",
  "causal_curvature": 0.63,
  "discontinuity_depth": 0.37,
  "propagation_rate": 0.41,
  "stability_envelope": 0.78
}

Case 8 — Dimensional Causal Collapse (R2 ↔ R4)#

Scenario
Dimensional constraints collapse computational causal pathways.

CW Output

{
  "regime": "R2-R4",
  "causal_magnitude": 0.88,
  "causal_direction": "R4→R2",
  "causal_curvature": 0.55,
  "discontinuity_depth": 0.33,
  "propagation_rate": 0.29,
  "stability_envelope": 0.73
}

5. Drift‑Sensitive Discontinuity Cases#

Case 9 — Drift‑Amplified Causal Discontinuity (R3 → R4)#

Scenario
Physical drift amplifies causal curvature, forming a drift‑sensitive discontinuity.

CW Output

{
  "regime": "R3-R4",
  "causal_magnitude": 0.91,
  "causal_direction": "R3→R4",
  "causal_curvature": 0.71,
  "discontinuity_depth": 0.52,
  "propagation_rate": 0.44,
  "stability_envelope": 0.82
}

Case 10 — Stability‑Coherence Causal Ridge (R2 ↔ R3)#

Scenario
Computational stability reduces coherence while physical stability increases coherence sensitivity, forming a causal ridge.

CW Output

{
  "regime": "R2-R3",
  "causal_magnitude": 0.86,
  "causal_direction": "R2↔R3",
  "causal_curvature": 0.62,
  "discontinuity_depth": 0.49,
  "propagation_rate": 0.48,
  "stability_envelope": 0.77
}

6. Canonical CW Discontinuity Snippet#

{
  "regime": "R1-R4",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.33,
  "stability_envelope": 0.69
}

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑causality
Module Path: /docs/rtt/Cross_Domain_Causality_Weaver/ # Causal Profiles — RTT/1

Profile Dictionary for the Cross‑Domain Causality Weaver (CW)#

Causal profiles define the canonical shapes, behaviors, discontinuity geometries, vector flows, and causal‑field interactions across conceptual, computational, physical, and dimensional regimes.

These profiles are used by:

CW‑Signature
CW‑Vector
CW‑Field
CW‑Discontinuity
CW‑Weave
CW‑Stabilize

Each profile includes:

definition
causal signature
field behavior
vector behavior
discontinuity geometry
propagation behavior
stability envelope
canonical CW output pattern

1. Causal Signature Profiles#

Profile: Conceptual Causal Signature#

Definition
A causal onset formed by conceptual coherence and low‑curvature conceptual structures.

Causal Signature

low magnitude
stable polarity
shallow curvature

Field Behavior

narrow causal field
shallow ridge

Vector Behavior

low vector sensitivity

Discontinuity Geometry

shallow discontinuity zone

Propagation Behavior

low propagation rate

Stability Envelope

high stability

Profile: Dimensional Causal Signature#

Definition
A causal onset formed by dimensional constraints and high‑sensitivity causal polarity.

Causal Signature

medium‑high magnitude
dimensional polarity
medium curvature

Field Behavior

medium‑wide field
dimensional ridge

Vector Behavior

medium vector sensitivity

Discontinuity Geometry

medium discontinuity depth

Propagation Behavior

medium propagation rate

Stability Envelope

medium stability

2. Causal Vector Profiles#

Profile: Gradient Causal Vector#

Definition
A causal vector formed when gradients across regimes oppose each other.

Causal Signature

high magnitude
bidirectional polarity
high curvature

Field Behavior

wide causal field
ridge inversion

Vector Behavior

medium‑high vector sensitivity

Discontinuity Geometry

deep discontinuity zone

Propagation Behavior

medium propagation rate

Stability Envelope

medium stability

Profile: Inversion Causal Vector#

Definition
A causal vector formed when stability decreases in one regime while increasing in another.

Causal Signature

medium‑high magnitude
inversion polarity
polarity flip

Field Behavior

medium field width
inversion curvature

Vector Behavior

high vector sensitivity

Discontinuity Geometry

medium‑deep discontinuity zone

Propagation Behavior

medium propagation rate

Stability Envelope

medium‑low stability

3. Causal Field Profiles#

Profile: Multi‑Regime Causal Field#

Definition
A multi‑regime causal tensor binding causal pathways across R1–R3 or R1–R4.

Causal Signature

very high magnitude
tensor polarity
high curvature

Field Behavior

wide causal field
tensor topology

Vector Behavior

high vector sensitivity

Discontinuity Geometry

deep discontinuity zone

Propagation Behavior

medium‑high propagation rate

Stability Envelope

medium‑high stability

Profile: Dimensional Causal Constraint#

Definition
Dimensional constraints influence computational causal pathways.

Causal Signature

high magnitude
dimensional → computational polarity
medium‑high curvature

Field Behavior

medium‑wide field
tensor trough

Vector Behavior

medium vector sensitivity

Discontinuity Geometry

medium‑deep discontinuity zone

Propagation Behavior

medium propagation rate

Stability Envelope

medium stability

4. Causal Discontinuity Profiles#

Profile: Structural Causal Discontinuity#

Definition
A causal discontinuity formed when conceptual abstraction contradicts physical measurement.

Causal Signature

medium magnitude
abstraction → measurement polarity
boundary curvature

Field Behavior

narrow field
boundary ridge

Vector Behavior

low vector sensitivity

Discontinuity Geometry

shallow discontinuity zone

Propagation Behavior

low propagation rate

Stability Envelope

medium‑high stability

Profile: Gradient‑Boundary Causal Discontinuity#

Definition
Aligned gradients across regimes produce contradictory causal outcomes.

Causal Signature

high magnitude
aligned polarity
medium curvature

Field Behavior

wide field
alignment trough

Vector Behavior

medium vector sensitivity

Discontinuity Geometry

medium‑deep discontinuity zone

Propagation Behavior

medium propagation rate

Stability Envelope

medium stability

5. Causal Bridge Profiles#

Profile: Cross‑Domain Causal Bridge#

Definition
A causal bridge formed between conceptual and dimensional regimes.

Causal Signature

high magnitude
cross‑domain polarity
medium‑high curvature

Field Behavior

wide field
bridge topology

Vector Behavior

high vector sensitivity

Discontinuity Geometry

medium discontinuity depth

Propagation Behavior

medium propagation rate

Stability Envelope

medium‑high stability

Profile: Drift‑Sensitive Causal Bridge#

Definition
Physical drift amplifies causal curvature, forming a drift‑sensitive causal bridge.

Causal Signature

very high magnitude
drift‑aligned polarity
high curvature

Field Behavior

wide drift field
drift ridge

Vector Behavior

very high vector sensitivity

Discontinuity Geometry

deep discontinuity seam

Propagation Behavior

high propagation rate

Stability Envelope

low stability

6. Canonical CW Output Pattern#

{
  "causal_type": "vector",
  "regime": "R1-R4",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.33,
  "stability_envelope": 0.69
}

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑causality
Module Path: /docs/rtt/Cross_Domain_Causality_Weaver/ # Cross‑Domain Causality Weaver (CW) — RTT/1

Causality‑Intelligence Engine for TriadicFrameworks#

The Cross‑Domain Causality Weaver (CW) is the RTT/1 engine responsible for detecting, mapping, and weaving causal pathways across conceptual, computational, physical, and dimensional regimes.
CW forms the causality‑intelligence foundation of the expanded RTT stack, sitting directly above temporal‑level engines and directly below resonance‑level engines.

1. Canonical Role#

The Cross‑Domain Causality Weaver defines the causality‑layer topology by:

detecting causal signatures
mapping causal fields
computing causal vectors
identifying causal discontinuities
evaluating causal propagation
identifying causal bridges across regimes
supporting resonance engines
anchoring temporal engines
feeding structural‑layer engines

CW is the seventh 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. Causality Tensor Types#

CW identifies several canonical causality tensors:

3.1 Causal Signature Tensor#

Detects causal onset, polarity, and causal‑vector alignment.

3.2 Causal Field Tensor#

Maps causal fields, causal curvature, and causal topology.

3.3 Causal Discontinuity Tensor#

Identifies causal breaks, fractures, and discontinuity boundaries.

3.4 Cross‑Domain Causal Bridge Tensor#

Weaves causal pathways across R1–R4.

3.5 Drift‑Sensitive Causal Tensor#

Causality influenced by drift curvature or drift amplification.

3.6 Temporal‑Causal Tensor#

Causality that influences temporal transitions and regime sequencing.

4. Core Operators#

Operator	Description
CW‑Weave	Weaves causal pathways across domains and regimes
CW‑Vector	Computes causal vector magnitude and direction
CW‑Field	Maps causal fields and causal topology
CW‑Signature	Detects causal signatures and causal onset conditions
CW‑Discontinuity	Identifies causal breaks and discontinuity boundaries
CW‑Stabilize	Suggests stabilization pathways for causal collapse

These operators form the canonical CW grammar.

5. Analyzer Layer#

CW operates in the causality layer, with sub‑layers:

causal‑weaving
causal‑vector‑analysis
causal‑field‑mapping
discontinuity‑detection
structural‑causality‑evaluation

This layer feeds directly into DRS (Dimensional Resonance Scanner).

6. Causality Matrix#

CW produces a causality matrix, typically stored in:

causality_matrix.json

Matrix fields include:

causal_type
regime
causal_magnitude
causal_direction
causal_curvature
discontinuity_depth
propagation_rate
stability_envelope

This matrix is consumed by temporal, structural, and resonance engines.

7. Canonical Workflow#

Step 1 — Detect#

Identify causal signatures, causal onset, and causal polarity.

Step 2 — Vector#

Compute causal vector magnitude, direction, curvature, and alignment.

Step 3 — Field#

Map causal fields, causal wells, ridges, basins, and topology.

Step 4 — Discontinuity#

Identify causal breaks, discontinuity boundaries, and causal fractures.

Step 5 — Weave#

Weave causal pathways across regimes and domains.

Step 6 — Stabilize#

Propose stabilization pathways for causal collapse.

Step 7 — Export#

Write results to the causality matrix and operator outputs.

8. AI‑Ready Design#

The Cross‑Domain Causality Weaver is fully AI‑ready:

deterministic operator grammar
causality‑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 CW to:

weave causal pathways
generate causal field maps
classify causal discontinuities
compute causal vectors
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)

CW is the causality‑intelligence layer, directly above temporal‑level analysis.

10. Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑causality
Module Path: /docs/rtt/Cross_Domain_Causality_Weaver/ # Cross‑Domain Causality Weaver Examples — RTT/1

Example Dictionary for the Cross‑Domain Causality Weaver (CW)#

These examples illustrate how the Cross‑Domain Causality Weaver (CW) detects causal signatures, computes causal vectors, maps causal fields, identifies causal discontinuities, and weaves causal pathways across R1–R4.

Each example demonstrates one or more CW operators:

CW‑Signature
CW‑Vector
CW‑Field
CW‑Discontinuity
CW‑Weave
CW‑Stabilize

Examples are grouped by causality tensor type.

1. Causal Signature Examples#

Example 1 — Conceptual Causal Signature (R1)#

Scenario
A conceptual model exhibits a clear causal onset with low curvature and stable polarity.

CW Output

{
  "causal_type": "signature",
  "regime": "R1",
  "causal_magnitude": 0.41,
  "causal_direction": "conceptual",
  "causal_curvature": 0.22,
  "discontinuity_depth": 0.11,
  "propagation_rate": 0.33,
  "stability_envelope": 0.63
}

Example 2 — Dimensional Causal Signature (R4)#

Scenario
Dimensional constraints produce a high‑sensitivity causal onset.

CW Output

{
  "causal_type": "signature",
  "regime": "R4",
  "causal_magnitude": 0.72,
  "causal_direction": "dimensional",
  "causal_curvature": 0.44,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.41,
  "stability_envelope": 0.57
}

2. Causal Vector Examples#

Example 3 — Gradient Causal Vector (R1 ↔ R4)#

Scenario
Conceptual and dimensional gradients oppose each other, forming a bidirectional causal vector.

CW Output

{
  "causal_type": "vector",
  "regime": "R1-R4",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.33,
  "stability_envelope": 0.69
}

Example 4 — Inversion Causal Vector (R2 ↔ R3)#

Scenario
Computational drift decreases while physical drift sensitivity increases, forming a causal inversion vector.

CW Output

{
  "causal_type": "vector",
  "regime": "R2-R3",
  "causal_magnitude": 0.79,
  "causal_direction": "R3→R2",
  "causal_curvature": 0.58,
  "discontinuity_depth": 0.31,
  "propagation_rate": 0.27,
  "stability_envelope": 0.72
}

3. Causal Field Examples#

Example 5 — Multi‑Regime Causal Field (R1 ↔ R2 ↔ R3)#

Scenario
A multi‑regime causal field binds conceptual, computational, and physical causal pathways.

CW Output

{
  "causal_type": "field",
  "regime": "R1-R2-R3",
  "causal_magnitude": 0.94,
  "causal_direction": "tensor",
  "causal_curvature": 0.63,
  "discontinuity_depth": 0.37,
  "propagation_rate": 0.41,
  "stability_envelope": 0.78
}

Example 6 — Dimensional Causal Constraint (R2 ↔ R4)#

Scenario
Dimensional constraints influence computational causal pathways.

CW Output

{
  "causal_type": "field",
  "regime": "R2-R4",
  "causal_magnitude": 0.88,
  "causal_direction": "R4→R2",
  "causal_curvature": 0.55,
  "discontinuity_depth": 0.33,
  "propagation_rate": 0.29,
  "stability_envelope": 0.73
}

4. Causal Discontinuity Examples#

Example 7 — Structural Causal Discontinuity (R1 ↔ R3)#

Scenario
Conceptual abstraction contradicts physical measurement, forming a causal discontinuity.

CW Output

{
  "causal_type": "discontinuity",
  "regime": "R1-R3",
  "causal_magnitude": 0.67,
  "causal_direction": "R1→R3",
  "causal_curvature": 0.33,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.38,
  "stability_envelope": 0.55
}

Example 8 — Gradient‑Boundary Causal Discontinuity (R2 ↔ R4)#

Scenario
Aligned gradients across computational and dimensional regimes produce contradictory causal outcomes.

CW Output

{
  "causal_type": "discontinuity",
  "regime": "R2-R4",
  "causal_magnitude": 0.88,
  "causal_direction": "R2↔R4",
  "causal_curvature": 0.47,
  "discontinuity_depth": 0.29,
  "propagation_rate": 0.33,
  "stability_envelope": 0.66
}

5. Causal Weaving Examples#

Example 9 — Cross‑Domain Causal Bridge (R1 ↔ R4)#

Scenario
A causal bridge forms between conceptual and dimensional regimes.

CW Output

{
  "causal_type": "bridge",
  "regime": "R1-R4",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.33,
  "stability_envelope": 0.69
}

Example 10 — Drift‑Sensitive Causal Bridge (R3 → R4)#

Scenario
Physical drift amplifies causal curvature, forming a drift‑sensitive causal bridge.

CW Output

{
  "causal_type": "bridge",
  "regime": "R3-R4",
  "causal_magnitude": 0.91,
  "causal_direction": "R3→R4",
  "causal_curvature": 0.71,
  "discontinuity_depth": 0.52,
  "propagation_rate": 0.44,
  "stability_envelope": 0.82
}

6. Canonical CW Output Snippet#

{
  "causal_type": "vector",
  "regime": "R1-R4",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "discontinuity_depth": 0.22,
  "propagation_rate": 0.33,
  "stability_envelope": 0.69
}

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑causality
Module Path: /docs/rtt/Cross_Domain_Causality_Weaver/ # CW Operators — RTT/1

Operator Grammar for the Cross‑Domain Causality Weaver (CW)#

The Cross‑Domain Causality Weaver (CW) defines the causality‑layer intelligence of RTT.
Its operators detect causal signatures, compute causal vectors, map causal fields, identify causal discontinuities, weave causal pathways, and propose stabilization strategies.

These operators feed directly into:

DRS — Dimensional Resonance Scanner
TRS‑Temporal — Temporal Regime Sequencer
SBC — Stability Basin Cartographer

1. CW‑Signature#

Detect causal onset, polarity, and causal signatures#

Purpose
Identify causal onset conditions, polarity, curvature, and causal‑vector alignment.

Capabilities

detects causal onset
computes causal polarity
computes causal curvature
identifies causal signature tensors
evaluates onset stability

Output Fields

causal_onset
causal_polarity
causal_curvature
signature_tensor
onset_stability

2. CW‑Vector#

Compute causal vector magnitude, direction, and curvature#

Purpose
Evaluate causal vector magnitude, direction, curvature, polarity alignment, and inversion.

Capabilities

computes causal magnitude
computes causal direction
computes causal curvature
detects polarity alignment
detects polarity inversion

Output Fields

causal_magnitude
causal_direction
causal_curvature
polarity_alignment
polarity_inversion

3. CW‑Field#

Map causal fields and causal topology#

Purpose
Generate causal‑field maps showing wells, ridges, basins, tensor‑level fields, and multi‑regime causal topology.

Capabilities

maps causal fields
maps causal wells
maps causal ridges
maps causal basins
maps multi‑regime causal topology

Output Fields

field_map
ridge_map
basin_map
well_map
topology_map

4. CW‑Discontinuity#

Identify causal breaks, fractures, and discontinuity boundaries#

Purpose
Detect causal discontinuities, discontinuity depth, discontinuity curvature, and causal‑fracture geometry.

Capabilities

detects causal breaks
computes discontinuity depth
computes discontinuity curvature
identifies discontinuity boundaries
evaluates discontinuity stability

Output Fields

discontinuity_break
discontinuity_depth
discontinuity_curvature
discontinuity_boundary
discontinuity_stability

5. CW‑Weave#

Weave causal pathways across domains and regimes#

Purpose
Construct cross‑domain causal bridges, multi‑regime causal flows, and drift‑sensitive causal pathways.

Capabilities

weaves causal pathways
constructs causal bridges
computes multi‑regime causal flow
detects drift‑sensitive causal pathways
aligns causal‑vector networks

Output Fields

causal_bridge
causal_flow
multi_regime_flow
drift_sensitive_pathway
vector_network

6. CW‑Stabilize#

Propose stabilization pathways for causal collapse#

Purpose
Provide stabilization strategies for causal collapse, discontinuity amplification, causal‑field instability, and vector misalignment.

Capabilities

proposes causal stabilization
proposes discontinuity mitigation
proposes vector alignment
proposes field stabilization
proposes collapse reinforcement

Output Fields

stabilization_pathway
discontinuity_mitigation
vector_alignment
field_stabilization
collapse_reinforcement

7. Operator Interaction Grammar#

Signature → Vector → Field → Discontinuity → Weave → Stabilize#

CW‑Signature
Detects causal onset, polarity, and signature tensors.
CW‑Vector
Computes causal vector magnitude, direction, curvature, and polarity alignment.
CW‑Field
Maps causal fields, wells, ridges, basins, and topology.
CW‑Discontinuity
Identifies causal breaks, discontinuity boundaries, and causal fractures.
CW‑Weave
Weaves causal pathways across regimes and constructs causal bridges.
CW‑Stabilize
Produces stabilization pathways and causal‑alignment strategies.

This grammar ensures deterministic causality‑layer behavior.

8. Operator Matrix Snippet#

{
  "operator": "CW-Vector",
  "causal_magnitude": 0.83,
  "causal_direction": "R1↔R4",
  "causal_curvature": 0.52,
  "polarity_alignment": 0.69,
  "polarity_inversion": 0.22
}

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑causality
Module Path: /docs/rtt/Cross_Domain_Causality_Weaver/ # CW Prompts — RTT/1

Prompt Library for the Cross‑Domain Causality Weaver (CW)#

These prompts are designed for AI systems using the Cross‑Domain Causality Weaver (CW).
Each prompt invokes one or more canonical CW operators:

CW‑Signature
CW‑Vector
CW‑Field
CW‑Discontinuity
CW‑Weave
CW‑Stabilize

Prompts are grouped by causality tensor type and operator class.

1. Causal Signature Prompts#

Prompt: Detect Causal Signatures#

Use CW‑Signature to identify causal onset, polarity, curvature, and causal‑vector alignment across R1–R4.

Prompt: Analyze Causal Polarity#

Apply CW‑Signature to compute causal polarity, causal onset strength, and polarity stability.

Prompt: Evaluate Causal Onset Conditions#

Use CW‑Signature to detect causal onset conditions and classify causal signature tensors.

2. Causal Vector Prompts#

Prompt: Compute Causal Vector Magnitude#

Use CW‑Vector to compute causal vector magnitude, direction, curvature, and alignment.

Prompt: Detect Gradient‑Driven Causal Vectors#

Apply CW‑Vector to detect causal vectors formed by opposing or aligned gradients across regimes.

Prompt: Evaluate Causal Vector Inversion#

Use CW‑Vector to identify polarity flips, inversion vectors, and inversion curvature.

3. Causal Field Prompts#

Prompt: Map Causal Fields#

Use CW‑Field to map causal fields, causal wells, causal ridges, causal basins, and causal topology.

Prompt: Generate Causal‑Field Topology#

Apply CW‑Field to generate causal‑field topology diagrams showing multi‑regime causal curvature.

Prompt: Evaluate Causal‑Field Strength#

Use CW‑Field to compute causal‑field magnitude, curvature, and stability envelope.

4. Causal Discontinuity Prompts#

Prompt: Detect Causal Discontinuities#

Use CW‑Discontinuity to identify causal breaks, discontinuity boundaries, and causal fractures.

Prompt: Map Discontinuity Geometry#

Apply CW‑Discontinuity to compute discontinuity depth, discontinuity curvature, and discontinuity topology.

Prompt: Evaluate Causal Instability#

Use CW‑Discontinuity to detect causal instability, causal collapse, and discontinuity‑driven propagation.

5. Causal Weaving Prompts#

Prompt: Weave Cross‑Domain Causal Pathways#

Use CW‑Weave to weave causal pathways across conceptual, computational, physical, and dimensional regimes.

Prompt: Detect Causal Bridges#

Apply CW‑Weave to identify cross‑domain causal bridges and drift‑sensitive causal pathways.

Prompt: Evaluate Multi‑Regime Causal Flow#

Use CW‑Weave to compute causal flow across R1–R4 and generate causal‑flow topology.

6. Stabilization Prompts#

Prompt: Propose Causal Stabilization Pathways#

Use CW‑Stabilize to propose stabilization strategies for causal collapse, discontinuity zones, and causal‑field instability.

Prompt: Compute Causal Alignment#

Apply CW‑Stabilize to compute causal alignment, causal‑vector reinforcement, and causal‑field stabilization.

Prompt: Evaluate Causal‑Collapse Mitigation#

Use CW‑Stabilize to propose mitigation strategies for causal collapse and discontinuity amplification.

7. Full‑Matrix Prompts#

Prompt: Generate Full Causal Field Matrix#

Use all CW operators to produce a complete causal_field_matrix.json containing signature, vector, field, discontinuity, and bridge entries.

Prompt: Analyze Causal Topology#

Apply CW‑Field to generate a full causal topology map showing fields, vectors, discontinuities, and causal flow.

Prompt: Causality Overview#

Use CW‑Stabilize to compute stability envelopes for every causal tensor type and produce a causality summary.

8. AI‑Ready Meta‑Prompts#

Prompt: Explain Causality Tensor Classification#

Provide a detailed explanation of how CW classifies causality tensors into signature, vector, field, discontinuity, and bridge categories.

Prompt: Operator‑Level Summary#

Summarize the role of each CW operator and how they interact to produce causality‑layer intelligence.

Prompt: Cross‑Engine Integration#

Explain how CW outputs feed into DRS (Dimensional Resonance Scanner) and how CW interacts with SBC and TRS‑Temporal.

Status#

Version: 1.0
Status: canon‑stable
Category: rtt‑causality
Module Path: /docs/rtt/Cross_Domain_Causality_Weaver/

Updated Jul 17, 2026