Mapping the Neural Ghost: How Deterministic Weights Expose Model Inefficiency

Traditional random initialization masks which specific weights actually contribute to learning, leading to significant structural waste. By implementing a hash-based deterministic approach, developers can now track every parameter's evolution without storage overhead, uncovering that over 60% of model weights may remain entirely 'dead' and prime for pruning.