Recursive Intelligence Network ​

IMPORTANT

Spine Position: /04-mathematics/simulations/recursive-intelligence-network

Simulation Boundary ​

SIMULATION BOUNDARY

The computations displayed on this page represent a Simulation Candidate (M4-SIMULATION classification). This is an algorithmic execution stress-test of internal mathematical invariants.

DO NOT interpret this output as empirical confirmation or formal theorem proof. The environment is strictly a computational model.

Interpretation Boundary ​

INTERPRETATION BOUNDARY

Any metrics or timeseries data derived from this simulation are strictly confined to the defined parameter space and cannot be generalized to physical reality without corresponding formalization and review (S5 classification).

Model Purpose ​

Evaluates the propagation of a scalar activation potential across a graph network while tracing finite topological continuation constraints.

Contract Summary ​

  • Class: Recursive System
  • Status: Prototype
  • Language: Python

Node Types ​

  • Observer
  • Memory
  • Signal
  • Knowledge
  • Feedback

Edge Types ​

  • Transmission
  • Recursion
  • Coordination
  • Continuation

Metrics ​

  • network_coherence
  • signal_retention
  • feedback_density
  • knowledge_accumulation
  • continuation_ratio

Invariants ​

  1. RI1: activation finite
  2. RI2: memory depth bounded
  3. RI3: signal retention non-negative
  4. RI4: continuation ratio measurable
  5. RI5: knowledge accumulation diagnostic only

Failure Conditions ​

  • non-finite activation
  • unbounded memory depth
  • negative signal retention
  • unmeasurable continuation
  • instability cascade

Output Schema ​

The Python engine maps data arrays inside simulations/outputs/recursive_intelligence_default.json directly into the Vue telemetry layer:

Stability Tracking ​

Reproducibility ​

Run the engine locally:

bash
python simulations/graph/run_recursive_intelligence_network.py

SIMULATION BOUNDARY

The computations displayed on this page represent a Simulation Candidate (M4-SIMULATION classification). This is an algorithmic execution stress-test of internal mathematical invariants.

DO NOT interpret this output as empirical confirmation or formal theorem proof. The environment is strictly a computational model.

INTERPRETATION BOUNDARY

Any metrics or timeseries data derived from this simulation are strictly confined to the defined parameter space and cannot be generalized to physical reality without corresponding formalization and review (S5 classification).