A Minimal Discrete Geometric Filter for Accelerated Classification Applications to Prime Structures and Protein Folding

We present a minimal discrete geometric filtering framework inspired by a 4 × 4 snap-to-grid constraint, originally motivated by the Geometric Invariant Grid Logic (GIGL) hy-pothesis. The method performs deterministic early-stage classification using fixed-depth local constraints, sharply reducing the candidate space for computationally intensive prob-lems. We demonstrate the framework on prime number classification via a multi-level geometric sieve that guarantees zero false negatives (no prime loss), while achieving substantial search-space reduction with constant-time complexity. A theoretical Monte Carlo analysis at the 106 scale is provided. We further discuss extensions to protein folding pre-classification, where geometric filtering may drastically reduce conformational search complexity prior to probabilistic inference.