Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

==== Compared with just slapping zstd or gzip on tensors, or using basic half/8-bit quantization: ====
# GPU-aware patterns - It focuses on patterns that appear naturally in deep nets (ReLU zeros, pruning sparsity, quantization, similar batch structure) and implements them in CUDA-friendly ways.
# Semantic leverage via LoreTokens - Some patterns are not just “zeros here, values there” but known semantic structures. - A LoreToken can stand in for a complex, known tensor layout, meaning the system can compress entire structural patterns into short symbolic codes.
# Transparent hooking - No framework patching, no driver mods, no kernel modules. - Any CUDA-using code can, in principle, benefit without being rewritten.
# Safety-first, production-leaning design - It’s clearly laid out with: - Logging - Fallback paths - Stepwise activation levels - That makes it plausible to deploy in real systems, not just as a benchmark toy.