Khalomot: Physics-Constrained Neuro-Symbolic Architecture for Geological Intelligence

Geological data presents a pathological case for standard deep learning: extreme sparsity, high dimensionality, and power-law "Black Swan" distributions combined with absent labeled ground truth. We introduce Khalomot, a neuro-symbolic platform that replaces stochastic hallucination with physics-constrained, explainable reasoning through unsupervised modules (Shemesh, Shahar) and a central causal inference engine (Keter) to make informed predictions (Kokhav). Validated on industrial mining operations, the architecture demonstrates how domain knowledge graphs and constraint satisfaction can ground AI predictions in geological reality.