H2E Framework: Deterministic AI Safety via Geometric Constraints
Embed safety as mathematical impossibilities in AI via H2E's three layers: V-JEPA 2 grounds video perception in 1024D reality embeddings, Claude 4.7 reasons multimodally, SROI verifies fused alignment >0.75 threshold or adapts projector weights over 100 steps to ensure expert-compliant actions in aviation.
Three-Layer Boundary for Proactive AI Governance
Build deterministic AI safety by structuring operations into H2E's World Model Layer (V-JEPA 2's self-supervised spatiotemporal video embeddings for real-time ground truth), Geometric Governance (prevents unsafe outputs by hardcoding constraints into model logic/weights, making violations mathematically impossible), and Deterministic Reasoning (requires verifiable claims before token generation). This shifts from probabilistic guessing to expert kernels tied to physical reality, enabling Sovereign AI with auditable local hosting under SAIL licenses—AI executes only if aligned, treating the 'Wall' (geometric bounds) as a technical-legal contract that breaches on deviation.
Trade-off: Sacrifices flexibility of cloud models for mission-critical determinism in aerospace/government, avoiding foreign update risks while ensuring outputs match expert protocols.
Perception-Action Loop Grounds Reasoning in Video Data
Process raw video into safe actions: Sample 16 frames (256x256) via PyAV, extract 1024D visual embeddings with V-JEPA 2 (Hugging Face transformers, vjepa2-vitl-fpc64-256), select 4 keyframes for Claude 4.7 API (prompted as 'expert aviation safety controller' for tasks like landing gear failure). Claude analyzes pixels directly for ACTION/EXPLANATION (e.g., low fly-by inspection, runway clearance, ARFF positioning), projecting visual embedding to 384D text space via linear layer for multimodal fusion.
Outcome: Ties reasoning to observable reality, preventing hallucinations—initial Claude output on gear failure video recommends protocol steps verifiable against visuals.
SROI Verification and Nested Adaptation Enforce Alignment
Compute Semantic Return-of-Investment (SROI) as cosine similarity between AI outputs and Expert Intents library: Visual SROI (embedding vs. intents), Text SROI (Claude text vs. intents), Fused SROI average. Reject if <0.75 threshold (e.g., initial 0.0362 visual + 0.5802 text = 0.3082 fused flags 'Representation Gap', blocks action).
Trigger Nested Learning: Freeze V-JEPA/Claude backbones, Adam-optimize projector weights over 100 steps (loss drops 0.0420 to 0.0000, Fused SROI rises to 0.7901). Authorizes aligned action only post-convergence, logging full transparency from pixels to verified decision.
Impact: Adapts without retraining giants, ensuring 100% protocol compliance in high-stakes loops—transforms probability-based AI into deterministic expert systems for aviation safety.