H2E Locks LLMs into Expert-Only Responses via Semantic Gates
H2E framework uses cosine similarity (SROI) thresholds like 0.9583 to gate queries against 'Expert DNA' vectors, ensuring deterministic AI outputs only for high-stakes industrial tasks with DeepSeek 70B on NVIDIA L4.
Three-Layer Gating Prevents Semantic Drift in Expert AI
Implement H2E by embedding user queries and expert knowledge as vectors, then compute SROI (cosine similarity) in the Intent Governance Zone (IGZ). Block responses below thresholds like 0.9583 with a HARD-STOP to enforce alignment to 'Expert DNA'—a gold-standard intent vector from domain pros. Only passing queries reach the Cognitive Reasoning Layer, using greedy decoding (temperature=0.0) for repeatable, non-hallucinated outputs. This Normalized Expert Zone (NEZ) + IGZ + reasoning stack transforms probabilistic LLMs into deterministic systems, ideal for safety-critical ops like Orion ECLSS diagnostics where generic answers risk failure.
Trade-off: High thresholds (0.9583) silence even relevant queries without perfect vector match, prioritizing certainty over flexibility; low ones (0.5) mimic standard assistants but invite drift.
Fit 70B Model on 24GB L4 GPU with Quantization and Offloading
Load DeepSeek-R1-Distill-Llama-70B in Q4_K_M GGUF format (42.5 GB compressed) to slash VRAM needs while preserving reasoning. Enable Flash Attention 2 for faster processing, offload 28 layers to GPU (n_gpu_layers=28), and handle the rest on CPU to dodge OOM errors. Result: Sovereign, on-premise inference without cloud leaks, maintaining industrial data privacy.
This setup proves massive models viable on edge hardware—L4's 24GB runs what once needed clusters— but demands tuning layers per workload to balance speed and accuracy.
Thresholds Deliver Sovereign Agency with Auditable Certainty
At SROI=0.5, AI details spacecraft oxygen protocols freely; at 0.9583, it rejects near-misses, embodying 'Expert Manifold' confinement. Outputs stay auditable via fixed decoding, ending unsupervised AI in pro environments. H2E shifts from prompt hacks to geometric governance: queries must geometrically align to expert reality or halt, yielding certainty over creativity for high-stakes like aerospace where one hallucination compromises safety.