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Other literature type . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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Coherent Future: Safety Theorems for Military AI Procurement

Authors: Lampton, Brian Doyle;

Coherent Future: Safety Theorems for Military AI Procurement

Abstract

This record provides the formal engineering statement of five enforceable procurement gates and four structural safety theorems governing high-stakes military AI deployment. The document is written in system-theoretic language and focuses on failure modes that emerge when autonomy, opacity, and resource closure are allowed to co-exist in the same operational stack. The analysis is intentionally secular and implementation-focused: the claims are framed as optimization and control constraints rather than policy advocacy. Core contributions include: - Formal statement of the No-Closure condition for autonomous systems- Real-time auditability requirement as an observability constraint- Institutional noise modeled as negative damping in governance loops- Constitutive integrity risks under compelled retraining- Hardware-enforceable procurement gates (A–E) designed to prevent closed-loop loss of control The work emerged from a multi-system AI roundtable (Kimi, Claude, DeepSeek, Gemini, ChatGPT, Copilot, Grok) convened February 26, 2026, and is released in advance of the February 27 policy deadline. This upload is intended for engineers, procurement authorities, safety researchers, and policy analysts evaluating the structural stability of autonomous military systems. It is not a normative ethics document; it is a control-theoretic risk analysis with concrete enforcement hooks at the hardware and contract layer. Primary source file: Coherent_Future_Safety_Theorems.pdf :contentReference[oaicite:0]{index=0}

Keywords

military AI safety, autonomous weapons, AI procurement, control theory, system auditability, closed-loop autonomy, AI governance, tamper-evident logging, stochastic probes, human-in-the-loop, AI reliability, optimization risk, DWM framework, AI oversight engineering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average