OP here. I've been researching geometric inefficiencies in standard computational physics—specifically the 'Entropy Tax' of using Cube/Cartesian units to model spherical phenomena (quantum dots, orbits, packing).
I developed a logic framework called Sphere-Base-One (SB1) which normalizes the fundamental unit to a Sphere (D=1,V=1). This effectively absorbs π and other transcendental constants into the unit definition.
The Results: I ran stress tests on both Gemini and GPT using this logic. Both models successfully adopted the protocol and demonstrated that it converts complex floating-point equations (like Energy Levels in quantum dots) into clean Integer Sequences (1,4,9...).
I've released the Python kernel (v2.0) here as a proof-of-concept for engineers working on Ray Tracing or Quantum Simulation.
The repo includes the core class and the 'Hahn Bridge' for converting standard SI units into SB1 units. Would love feedback on the packing_efficiency implementation.
OP here. I've been researching geometric inefficiencies in standard computational physics—specifically the 'Entropy Tax' of using Cube/Cartesian units to model spherical phenomena (quantum dots, orbits, packing).
I developed a logic framework called Sphere-Base-One (SB1) which normalizes the fundamental unit to a Sphere (D=1,V=1). This effectively absorbs π and other transcendental constants into the unit definition.
The Results: I ran stress tests on both Gemini and GPT using this logic. Both models successfully adopted the protocol and demonstrated that it converts complex floating-point equations (like Energy Levels in quantum dots) into clean Integer Sequences (1,4,9...).
I've released the Python kernel (v2.0) here as a proof-of-concept for engineers working on Ray Tracing or Quantum Simulation.
The repo includes the core class and the 'Hahn Bridge' for converting standard SI units into SB1 units. Would love feedback on the packing_efficiency implementation.