extract: 2026-03-26-metr-algorithmic-vs-holistic-evaluation

Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>

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

@@ -134,6 +134,12 @@ METR, the primary producer of governance-relevant capability benchmarks, explici
 
 METR's January 2026 evaluation of GPT-5 placed its autonomous replication and adaptation capability at 2h17m (50% time horizon), far below catastrophic risk thresholds. In the same month, AISLE (an AI system) autonomously discovered 12 OpenSSL CVEs including a 30-year-old bug through fully autonomous operation. This is direct evidence that formal pre-deployment evaluations are not capturing operational dangerous autonomy that is already deployed at commercial scale.
 
+### Additional Evidence (extend)
+*Source: [[2026-03-26-metr-algorithmic-vs-holistic-evaluation]] | Added: 2026-03-26*
+
+METR's August 2025 research update provides specific quantification of the evaluation reliability problem: algorithmic scoring overstates capability by 2-3x (38% algorithmic success vs 0% holistic success for Claude 3.7 Sonnet on software tasks), and HCAST benchmark version instability of ~50% between annual versions means even the measurement instrument itself is unstable. METR explicitly acknowledges their own evaluations 'may substantially overestimate' real-world capability.
+
+
 
 
 

inbox/queue/.extraction-debug/2026-03-26-metr-algorithmic-vs-holistic-evaluation.json

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inbox/queue/2026-03-26-metr-algorithmic-vs-holistic-evaluation.md

@@ -7,9 +7,13 @@ date: 2025-08-12
 domain: ai-alignment
 secondary_domains: []
 format: blog
-status: unprocessed
+status: enrichment
 priority: high
 tags: [METR, HCAST, algorithmic-scoring, holistic-evaluation, benchmark-reality-gap, SWE-bench, governance-thresholds, capability-measurement]
+processed_by: theseus
+processed_date: 2026-03-26
+enrichments_applied: ["pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -54,3 +58,11 @@ METR's current formal thresholds for "catastrophic risk" scrutiny:
 PRIMARY CONNECTION: [[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]]
 WHY ARCHIVED: Empirical validation that the *measurement infrastructure* for AI governance is systematically unreliable — extends session 13/14's benchmark-reality gap finding with specific numbers and the source organization explicitly acknowledging the problem
 EXTRACTION HINT: Focus on the governance implication: METR's own evaluations, which are used to set safety thresholds, may overstate real-world capability by 2-3x in software domains — and the benchmark is unstable enough to shift 50%+ between annual versions
+
+
+## Key Facts
+- METR's formal thresholds for catastrophic risk scrutiny: 80% time horizon exceeding 8 hours on high-context tasks, or 50% time horizon exceeding 40 hours on software engineering/ML tasks
+- GPT-5's 50% time horizon as of January 2026: 2 hours 17 minutes (far below 40-hour threshold)
+- METR's 131-day doubling time estimate from prior reports is derived from benchmark performance that may substantially overestimate real-world capability
+- SWE-Bench Verified success rates for frontier models: around 70-75%
+- METR is incorporating holistic assessment elements into formal evaluations: assurance checklists, reasoning trace analysis, situational awareness testing