Merge branch 'main' into extract/2026-03-20-bench2cop-benchmarks-insufficient-compliance

Pipeline auto-fixer: removed [[ ]] brackets from links
that don't resolve to existing claims in the knowledge base.

domains/ai-alignment/AI transparency is declining not improving because Stanford FMTI scores dropped 17 points in one year while frontier labs dissolved safety teams and removed safety language from mission statements.md

@@ -31,22 +31,28 @@ The alignment implication: transparency is a prerequisite for external oversight
 
 
 ### Additional Evidence (extend)
-*Source: [[2024-12-00-uuk-mitigations-gpai-systemic-risks-76-experts]] | Added: 2026-03-19*
+*Source: 2024-12-00-uuk-mitigations-gpai-systemic-risks-76-experts | Added: 2026-03-19*
 
 Expert consensus identifies 'external scrutiny, proactive evaluation and transparency' as the key principles for mitigating AI systemic risks, with third-party audits as the top-3 implementation priority. The transparency decline documented by Stanford FMTI is moving in the opposite direction from what 76 cross-domain experts identify as necessary.
 
 
 ### Additional Evidence (extend)
-*Source: [[2025-08-00-mccaslin-stream-chembio-evaluation-reporting]] | Added: 2026-03-19*
+*Source: 2025-08-00-mccaslin-stream-chembio-evaluation-reporting | Added: 2026-03-19*
 
 STREAM proposal identifies that current model reports lack 'sufficient detail to enable meaningful independent assessment' of dangerous capability evaluations. The need for a standardized reporting framework confirms that transparency problems extend beyond general disclosure (FMTI scores) to the specific domain of dangerous capability evaluation where external verification is currently impossible.
 
 
 ### Additional Evidence (confirm)
-*Source: [[2026-03-16-theseus-ai-coordination-governance-evidence]] | Added: 2026-03-19*
+*Source: 2026-03-16-theseus-ai-coordination-governance-evidence | Added: 2026-03-19*
 
 Stanford FMTI 2024→2025 data: mean transparency score declined 17 points. Meta -29 points, Mistral -37 points, OpenAI -14 points. OpenAI removed 'safely' from mission statement (Nov 2025), dissolved Superalignment team (May 2024) and Mission Alignment team (Feb 2026). Google accused by 60 UK lawmakers of violating Seoul commitments with Gemini 2.5 Pro (Apr 2025).
 
+
+### Additional Evidence (extend)
+*Source: [[2026-03-20-bench2cop-benchmarks-insufficient-compliance]] | Added: 2026-03-20*
+
+The Bench-2-CoP analysis reveals that even when labs do conduct evaluations, the benchmark infrastructure itself is architecturally incapable of measuring loss-of-control risks. This compounds the transparency decline: labs are not just hiding information, they're using evaluation tools that cannot detect the most critical failure modes even if applied honestly.
+
 ---
 
 Relevant Notes:

domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md

@@ -52,10 +52,16 @@ METR and UK AISI evaluations as of March 2026 focus primarily on sabotage risk a
 
 
 ### Additional Evidence (confirm)
-*Source: [[2026-02-23-shapira-agents-of-chaos]] | Added: 2026-03-19*
+*Source: 2026-02-23-shapira-agents-of-chaos | Added: 2026-03-19*
 
 Agents of Chaos demonstrates that static single-agent benchmarks fail to capture vulnerabilities that emerge in realistic multi-agent deployment. The study's central argument is that pre-deployment evaluations are insufficient because they cannot test for cross-agent propagation, identity spoofing, and unauthorized compliance patterns that only manifest in multi-party environments with persistent state.
 
+
+### Additional Evidence (extend)
+*Source: [[2026-03-20-bench2cop-benchmarks-insufficient-compliance]] | Added: 2026-03-20*
+
+Prandi et al. (2025) found that 195,000 benchmark questions provided zero coverage of oversight evasion, self-replication, and autonomous AI development capabilities. This extends the evaluation unreliability thesis by showing the gap is not just predictive validity but complete absence of measurement for alignment-critical capabilities.
+
 ---
 
 Relevant Notes:

inbox/queue/.extraction-debug/2026-03-20-bench2cop-benchmarks-insufficient-compliance.json

@@ -0,0 +1,24 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "ai-benchmarks-provide-zero-coverage-of-loss-of-control-capabilities-making-them-structurally-insufficient-for-regulatory-compliance.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 1,
+    "kept": 0,
+    "fixed": 1,
+    "rejected": 1,
+    "fixes_applied": [
+      "ai-benchmarks-provide-zero-coverage-of-loss-of-control-capabilities-making-them-structurally-insufficient-for-regulatory-compliance.md:set_created:2026-03-20"
+    ],
+    "rejections": [
+      "ai-benchmarks-provide-zero-coverage-of-loss-of-control-capabilities-making-them-structurally-insufficient-for-regulatory-compliance.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-20"
+}

inbox/queue/2026-03-20-bench2cop-benchmarks-insufficient-compliance.md

@@ -7,9 +7,13 @@ date: 2025-08-01
 domain: ai-alignment
 secondary_domains: []
 format: paper
-status: unprocessed
+status: enrichment
 priority: high
 tags: [benchmarking, EU-AI-Act, compliance, evaluation-gap, loss-of-control, oversight-evasion, independent-evaluation, GPAI]
+processed_by: theseus
+processed_date: 2026-03-20
+enrichments_applied: ["pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md", "AI transparency is declining not improving because Stanford FMTI scores dropped 17 points in one year while frontier labs dissolved safety teams and removed safety language from mission statements.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -52,3 +56,11 @@ The paper examines whether current AI benchmarks are adequate for EU AI Act regu
 PRIMARY CONNECTION: [[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]]
 WHY ARCHIVED: Creates empirical bridge between EU AI Act mandatory obligations and the practical impossibility of compliance through existing evaluation tools — closes the loop on the "evaluation infrastructure building but architecturally wrong" thesis
 EXTRACTION HINT: Focus on the zero-coverage finding for loss-of-control capabilities — this is the most striking and specific number, and it directly supports the argument that compliance infrastructure exists on paper but not in practice
+
+
+## Key Facts
+- EU AI Act GPAI obligations (Article 55) came into force August 2, 2025
+- Prandi et al. analyzed approximately 195,000 benchmark questions using LLM-as-judge methodology
+- 61.6% of regulatory-relevant benchmark coverage addresses 'tendency to hallucinate'
+- 31.2% of regulatory-relevant benchmark coverage addresses 'lack of performance reliability'
+- Zero benchmark questions in the analyzed corpus covered oversight evasion, self-replication, or autonomous AI development capabilities