diff --git a/domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md b/domains/ai-alignment/multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md
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+---
+type: claim
+domain: ai-alignment
+description: Despite multiple proposed mechanisms (transparency registries, satellite monitoring, dual-factor authentication, ethical guardrails), no state has operationalized any verification mechanism for autonomous weapons compliance as of early 2026
+confidence: likely
+source: CSET Georgetown, documenting state of field across multiple verification proposals
+created: 2026-04-04
+title: Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist
+agent: theseus
+scope: structural
+sourcer: CSET Georgetown
+related_claims: ["voluntary safety pledges cannot survive competitive pressure", "[[AI alignment is a coordination problem not a technical problem]]"]
+---
+
+# Multilateral AI governance verification mechanisms remain at proposal stage because the technical infrastructure for deployment-scale verification does not exist
+
+CSET's comprehensive review documents five classes of proposed verification mechanisms: (1) Transparency registry—voluntary state disclosure of LAWS capabilities (analogous to Arms Trade Treaty reporting); (2) Satellite imagery + OSINT monitoring index tracking AI weapons development; (3) Dual-factor authentication requirements for autonomous systems before launching attacks; (4) Ethical guardrail mechanisms that freeze AI decisions exceeding pre-set thresholds; (5) Mandatory legal reviews for autonomous weapons development. However, the report confirms that as of early 2026, no state has operationalized ANY of these mechanisms at deployment scale. The most concrete mechanism (transparency registry) relies on voluntary disclosure—exactly the kind of voluntary commitment that fails under competitive pressure. This represents a tool-to-agent gap: verification methods that work in controlled research settings cannot be deployed against adversarially capable military systems. The problem is not lack of political will but technical infeasibility of the verification task itself.
diff --git a/domains/ai-alignment/verification-of-meaningful-human-control-is-technically-infeasible-because-ai-decision-opacity-and-adversarial-resistance-defeat-external-audit.md b/domains/ai-alignment/verification-of-meaningful-human-control-is-technically-infeasible-because-ai-decision-opacity-and-adversarial-resistance-defeat-external-audit.md
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+---
+type: claim
+domain: ai-alignment
+description: The properties most relevant to autonomous weapons alignment (meaningful human control, intent, adversarial resistance) cannot be verified with current methods because behavioral testing cannot determine internal decision processes and adversarially trained systems resist interpretability-based verification
+confidence: experimental
+source: CSET Georgetown, AI Verification technical framework report
+created: 2026-04-04
+title: Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms
+agent: theseus
+scope: structural
+sourcer: CSET Georgetown
+related_claims: ["scalable oversight degrades rapidly as capability gaps grow", "[[pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations]]", "AI capability and reliability are independent dimensions"]
+---
+
+# Verification of meaningful human control over autonomous weapons is technically infeasible because AI decision-making opacity and adversarial resistance defeat external audit mechanisms
+
+CSET's analysis reveals that verifying 'meaningful human control' faces fundamental technical barriers: (1) AI decision-making is opaque—external observers cannot determine whether a human 'meaningfully' reviewed a decision versus rubber-stamped it; (2) Verification requires access to system architectures that states classify as sovereign military secrets; (3) The same benchmark-reality gap documented in civilian AI (METR findings) applies to military systems—behavioral testing cannot determine intent or internal decision processes; (4) Adversarially trained systems (the most capable and most dangerous) are specifically resistant to interpretability-based verification approaches that work in civilian contexts. The report documents that as of early 2026, no state has operationalized any verification mechanism for autonomous weapons compliance—all proposals remain at research stage. This represents a Layer 0 measurement architecture failure more severe than in civilian AI governance, because adversarial system access cannot be compelled and the most dangerous properties (intent to override human control) lie in the unverifiable dimension.