H2E: 4 Pillars for Deterministic AI in Safety-Critical Systems
H2E framework wraps LLMs like Gemini 2.0 Flash in a 4-pillar architecture to enforce provable agency: Civilizational goals via SROI > 0.9583, structured JSON outputs, sentinel hard-stops on subpar plans, and logged executions for audits.
Anchoring AI Reasoning to Civilizational Priorities
The H2E (Human-to-Expert) framework starts by defining a Non-Negotiable Expert Zone (NEZ) that prioritizes human life in safety-critical scenarios like disaster response, focusing on uninterrupted hospital power. This Civilizational Thinking pillar sets a concrete SROI (Semantic Return on Investment) threshold of 0.9583—actions below this fail alignment checks. Pairing this with Mathematical Foundations, LLMs like Gemini 2.0 Flash generate reasoning but output only structured JSON via Pydantic schemas: each proposal includes a specific action, predicted_impact (0.1-1.0), and resource_cost (0.1-1.0). This converts probabilistic prose into auditable data, verifiable against engineering laws.
Deterministic Safeguards Prevent Hallucinations
Industrial Engineering acts as a Sentinel, computing SROI as impact/cost ratio. If below 0.9583, it triggers a Physical Hard-Stop via os.kill, terminating the process at the kernel level before physical impact. This exoskeleton ensures no sub-optimal or hallucinated plans escape, turning AI into a reliable governor for systems like autonomous grid control or emergency power management. Real-World Deployment then executes verified actions—e.g., rerouting 30 MW to medical grids—while logging everything in an immutable Black Box Governance Log, tracking Real-Time Factor (RTF) and carbon intensity for audits.
Outcomes: Sovereign AI for Industrial Crises
H2E shifts AI from black-box experimentation to sovereign utility, operating locally with mathematical certainty. It tethers high-order reasoning to safety valves, enabling resilient governance in complex crises. Trade-offs include restricting LLM flexibility to JSON-only outputs and kernel-level kills for enforcement, but this guarantees zero tolerance for 'best guess' failures in life-critical domains.