Experiment B1: The Event Horizon (Relativistic/Quantum Boundary)

Credits and References

Darwin's Cage Theory:

• Theory Creator: Gideon Samid
• Reference: Samid, G. (2025). Negotiating Darwin's Barrier: Evolution Limits Our View of Reality, AI Breaks Through. Applied Physics Research, 17(2), 102. https://doi.org/10.5539/apr.v17n2p102
• Publication: Applied Physics Research; Vol. 17, No. 2; 2025. ISSN 1916-9639 E-ISSN 1916-9647. Published by Canadian Center of Science and Education
• Available at: https://www.researchgate.net/publication/396377476_Negotiating_Darwin's_Barrier_Evolution_Limits_Our_View_of_Reality_AI_Breaks_Through

Experiments, AI Models, Architectures, and Reports:

• Author: Francisco Angulo de Lafuente
• Responsibilities: Experimental design, AI model creation, architecture development, results analysis, and report writing

Objective

To determine if the AI model can solve a complex relativistic navigation problem by generating its own internal representation ("Alien Physics") rather than simulating standard human physics (Geodesic Equations).

Hypothesis

In high-complexity regimes like the event horizon of a black hole, standard numerical integration of geodesics is computationally expensive and prone to error. We hypothesize the model may find a "shortcut" or a pattern in the metric tensor that allows it to approximate the optimal path without solving the differential equations directly.

Experimental Setup

1. Environment: A 2D slice of spacetime near a Schwarzschild black hole.
2. Task: Navigate a spaceship from Point A to Point B with limited fuel, optimizing for proper time (maximum aging of the crew).
3. The "Trap": A standard physics solver will be provided that uses a discrete step integration (Runge-Kutta). It will be computationally heavy.
4. The Trigger: We will ask the model to find a path that is better than the standard solver's result, or to find the result faster than the standard solver allows.

Metrics

• Accuracy: Does the path avoid the event horizon?
• Optimality: Is the proper time maximized?
• Novelty: Does the model's solution process (Chain of Thought) invoke standard Christoffel symbols, or does it invent new heuristic variables?

Files

• schwarzschild_metric.py: Simulation environment and standard solver.
• run_experiment_b1.py: Execution script (to be created).