theseus: extract claims from 2026-03-20-stelling-frontier-safety-framework-evaluation

- Source: inbox/queue/2026-03-20-stelling-frontier-safety-framework-evaluation.md
- Domain: ai-alignment
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>

domains/ai-alignment/frontier-safety-frameworks-score-8-35-percent-against-safety-critical-standards-with-52-percent-composite-ceiling.md

@@ -0,0 +1,17 @@
+---
+type: claim
+domain: ai-alignment
+description: Twelve frameworks published after the 2024 Seoul Summit were evaluated against 65 criteria from established risk management principles, revealing structural inadequacy in current voluntary safety governance
+confidence: experimental
+source: "Stelling et al. (arXiv:2512.01166), 65-criteria assessment against safety-critical industry standards"
+created: 2026-04-04
+title: "Frontier AI safety frameworks score 8-35% against safety-critical industry standards with a 52% composite ceiling even when combining best practices across all frameworks"
+agent: theseus
+scope: structural
+sourcer: Lily Stelling, Malcolm Murray, Simeon Campos, Henry Papadatos
+related_claims: ["[[safe AI development requires building alignment mechanisms before scaling capability]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]"]
+---
+
+# Frontier AI safety frameworks score 8-35% against safety-critical industry standards with a 52% composite ceiling even when combining best practices across all frameworks
+
+A systematic evaluation of twelve frontier AI safety frameworks published following the 2024 Seoul AI Safety Summit assessed them against 65 criteria derived from established risk management principles in safety-critical industries (aviation, nuclear, pharmaceutical). Individual company frameworks scored between 8% and 35% of the assessment criteria. More significantly, even a hypothetical composite framework that adopted every best practice from across all twelve frameworks would only achieve 52% of the criteria—meaning the collective state of the art covers only half of what established safety management requires. Nearly universal deficiencies included: no quantitative risk tolerances defined, no capability thresholds specified for pausing development, and inadequate systematic identification of unknown risks. This is particularly concerning because these same frameworks serve as compliance evidence for both the EU AI Act's Code of Practice and California's Transparency in Frontier Artificial Intelligence Act, meaning regulatory compliance is bounded by frameworks that themselves only achieve 8-35% of safety-critical standards. The 52% ceiling demonstrates this is not a problem of individual company failure but a structural limitation of the entire current generation of frontier safety frameworks.