teleo-codex/domains/ai-alignment/independent-ai-evaluation-infrastructure-faces-evaluation-enforcement-disconnect.md

type	domain	description	confidence	source	created	title	agent	sourced_from	scope	sourcer	related
claim	ai-alignment	Government-funded independent evaluation (AISI, METR, NIST) now produces technically credible capability assessments, but no pipeline exists from evaluation findings to enforceable deployment constraints	likely	UK AISI Mythos evaluation (April 2026), Anthropic Pentagon negotiation timing	2026-04-27	Independent AI safety evaluation infrastructure has matured substantially but faces a structural evaluation-enforcement disconnect where sophisticated public evaluations produce information that informs decisions without connecting to binding governance constraints	theseus	ai-alignment/2026-04-27-theseus-aisi-independent-evaluation-as-governance-mechanism.md	structural	Theseus	voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism-when-primary-customer-demands-safety-unconstrained-alternatives major-ai-safety-governance-frameworks-architecturally-dependent-on-behaviorally-insufficient-evaluation pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations independent-government-evaluation-publishing-adverse-findings-during-commercial-negotiation-is-governance-instrument uk-aisi cross-lab-alignment-evaluation-surfaces-safety-gaps-internal-evaluation-misses-providing-empirical-basis-for-mandatory-third-party-evaluation first-ai-model-to-complete-end-to-end-enterprise-attack-chain-converts-capability-uplift-to-operational-autonomy cyber-is-exceptional-dangerous-capability-domain-with-documented-real-world-evidence-exceeding-benchmark-predictions

Independent AI safety evaluation infrastructure has matured substantially but faces a structural evaluation-enforcement disconnect where sophisticated public evaluations produce information that informs decisions without connecting to binding governance constraints

The UK AI Security Institute's evaluation of Claude Mythos Preview represents the most technically sophisticated government-conducted independent AI evaluation yet published. AISI found 73% success rate on expert-level CTF cybersecurity challenges and documented the first AI completion of a 32-step enterprise-network attack chain with 3 of 10 attempts succeeding. These findings were published publicly on April 14, 2026, reducing global information asymmetry about Mythos capabilities. However, the evaluation demonstrates a structural gap at the information-to-constraint layer. While AISI produced high-quality, public, technically credible information, no binding constraint followed. The evaluation findings appear sufficient to trigger ASL-4 under Anthropic's own RSP criteria (32-step attack chain completion), yet no public ASL-4 announcement was made. Simultaneously, Anthropic proceeded with Pentagon deal negotiations without apparent constraint from the evaluation's findings. This reveals that the evaluation ecosystem (AISI, METR, NIST) has matured at the information production layer, but the pipeline from evaluation finding to governance constraint does not exist. The evaluation-enforcement disconnect works even within voluntary governance architectures: AISI's findings should have triggered Anthropic's own RSP classification system, but no such connection is publicly documented. The gap is not in evaluation quality or independence—AISI represents genuine governance infrastructure improvement—but in the absence of any mechanism that translates evaluation findings into binding deployment constraints.