introduce a “semantic firewall” layer that optimizes inference at the language-law level — a symbolic energy compression mechanism that cuts redundant compute cycles while preserving meaning fidelity.
Instead of scaling by GPU count, this layer redefines compute as coherence between intention and output.
It’s a governance-first, efficiency-driven approach: models learn to “understand” before they “generate,” lowering both latency and energy use.
Why useful:
Reduces token and GPU cost by aligning meaning before computation.
Enhances consistency and safety without extra filters.
Enables explainable optimization: every decision leaves a verifiable WORM-trace.
Who benefits:
AI researchers, developers, and enterprises who want both performance and auditability. It’s a path from “more GPUs” → “smarter laws of compute.”
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