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/

Updated Jul 8, 2026