What question did this study set out to answer?

The aim is to develop an AI safety evaluation system that operates without the need for external language model calls.

March 13, 20260 citationsOpen Access

Zero-LLM Multi-Agent Architecture for AI Safety Evaluation: Formal Verification, 8 Ethical Dimensions, <50ms

Key Points

The aim is to develop an AI safety evaluation system that operates without the need for external language model calls.
Developed TSCWH for safety evaluation using zero-LLM calls
Implemented a multi-agent design with 10 agents analyzing 8 ethical dimensions
Conducted formal verification of governance invariants
Designed comprehensive test suite for full coverage
Achieved <50 ms deliberation time
Reduced costs by 94.4% compared to previous methods
Maintained a <2% false positive rate
Improved latency by 360 times through integrated safety layers

Abstract

We present TSCWH (The System for Covenant-Weighted Heuristics), a patent-pending AI safety evaluation architecture achieving zero-LLM-call deliberative consensus through a proprietary shared-memory coordination design. KEY RESULTS:• 10 agents deliberate across 8 ethical dimensions (Charity, Grace, Stewardship, Truth, Dignity, Courage, Community, Creation Dignity) in <50 ms• 0 LLM API calls for safety evaluation — 94.4% cost reduction vs. prior art• Formal verification of governance invariants on every evaluation cycle• <2% false positive rate on legitimate actions; 360× latency improvement• 28 integrated safety layers, 42 hardening phases, comprehensive test suite with full coverage Patent pending (U.S. Provisional App. 63/998,573, March 2026). Copyright registered (Case #1-15114193381).

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