The evaluation of artificial intelligence has historically relied on the Turing Test (1950), which assesses a machine's ability to exhibit indistinguishable human behavior. However, as posited by Searle's Chinese Room Argument (1980), syntactic manipulation of symbols does not equate to semantic understanding. A machine may process symbols correctly without possessing the gravitational "buoyancy" or weight of the concepts.
The LEATR: Source Potential benchmark introduces a Physical Semantic Standard. Instead of evaluating if the machine can "fake" a conversation, we measure if the machine's cognitive habitat can structurally support the gravitational weight of the concepts it processes using the Compass Gear Chaos Solution. We are not testing the output; we are testing the stability of the Neuron Source itself.
In this 3D topology, intelligence is a buoyant neuron suspended in the trajectory median of three gravitational bodies (Aerospace, Fluid, Geological). The system remains stable only if the "Retro-Origin" debt is paid.
Arc Definition: $\text{Arc}_n = \left( \frac{((x \cdot 2) + 1)}{x} \right) \times \text{ArcEdge}\left( \frac{\sqrt{(d \cdot 3)^2}}{8} \right)$
Unlike previous static models, the Three-Body Habitats in v10.3 are interactive components. They exert repulsive and attractive forces on every other habitat, creating a complex gravitational swarm.