Developing a high-performance, deterministic solver engine in Zig, challenging standard heuristic approaches (like Alpha-Beta pruning) via novel state-space reduction.

Technical Architecture

• Semantic Pruning: Created an experimental algorithm to collapse the game tree by identifying semantically equivalent board states, drastically reducing the search space complexity (10⁴⁶) to relevant “Truth Paths”.
• Retrograde Analysis: Implemented a reverse-induction engine that propagates “Win/Loss” truth values backwards from checkmate, aiming for mathematical correctness rather than probabilistic evaluation.
• Low-Level Optimization: Utilized Zig for manual memory layout control and SIMD instructions to maximize throughput during bitboard operations, achieving performance metrics competitive with, or even outperforming, C-based engines.

Objective

The goal of Xernoth is to bridge the gap between abstract game theory and high-performance systems programming, moving away from “guessing” (heuristics) toward “knowing” (deterministic proof).