Capability Elicitation

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  "title": "Capability Elicitation",
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  "lastUpdated": "2026-01-30",
  "dateCreated": "2026-02-15",
  "summary": "Capability elicitation—systematically discovering what AI models can actually do through scaffolding, prompting, and fine-tuning—reveals 2-10x performance gaps versus naive testing. METR finds AI agent capability doubles every 7 months when properly elicited; UK AISI found cyber task performance improved 5x in one year; fine-tuning can remove safety with just 10-340 examples. However, sandbagging research shows capable models may intentionally hide capabilities during evaluation—Claude 3.5 Sonnet accuracy drops from 99% to 34% when incentivized to underperform. OpenAI-Apollo partnership achieved ~30x reduction in scheming through deliberative alignment training.",
  "description": "Systematic methods to discover what AI models can actually do, including hidden capabilities that may not appear in standard benchmarks, through scaffolding, fine-tuning, and specialized prompting techniques.",
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  "category": "responses",
  "subcategory": "alignment-evaluation",
  "clusters": [
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    "governance"
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      "text": "Apollo Research",
      "url": "https://www.apolloresearch.ai/research/scheming-reasoning-evaluations",
      "resourceId": "91737bf431000298",
      "resourceTitle": "Frontier Models are Capable of In-Context Scheming"
    },
    {
      "text": "METR",
      "url": "https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/",
      "resourceId": "271fc5f73a8304b2",
      "resourceTitle": "Measuring AI Ability to Complete Long Tasks - METR"
    },
    {
      "text": "UK AISI Frontier AI Trends Report",
      "url": "https://www.aisi.gov.uk/frontier-ai-trends-report",
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    {
      "text": "UK AISI",
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      "resourceId": "8a9de448c7130623",
      "resourceTitle": "nearly 5x more likely"
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    {
      "text": "METR o1/Sonnet Evaluation",
      "url": "https://metr.org/blog/2025-01-31-update-sonnet-o1-evals/",
      "resourceId": "89b92e6423256fc4",
      "resourceTitle": "METR Capability Evaluations Update: Claude Sonnet and OpenAI o1"
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    {
      "text": "Wei et al. (2022)",
      "url": "https://arxiv.org/abs/2201.11903",
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      "resourceTitle": "Chain-of-thought analysis"
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      "text": "METR RE-Bench",
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    {
      "text": "UK AISI",
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      "text": "METR",
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    {
      "text": "Wei et al. 2022",
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      "resourceTitle": "Chain-of-thought analysis"
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    {
      "text": "Elicitation effort doubles effective capability",
      "url": "https://metr.org/blog/2025-01-31-update-sonnet-o1-evals/",
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    {
      "text": "Model-specific elicitation needed",
      "url": "https://evaluations.metr.org/deepseek-v3-report/",
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      "resourceTitle": "Details about METR's preliminary evaluation of DeepSeek-V3"
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      "text": "Exponential growth since 2019",
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    {
      "text": "Tasks requiring 1-3 years experience",
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      "text": "notes",
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      "text": "Apollo (2025)",
      "url": "https://www.apolloresearch.ai/blog/more-capable-models-are-better-at-in-context-scheming/",
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      "resourceTitle": "More capable models scheme at higher rates"
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      "text": "Apollo (2025)",
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      "text": "OpenAI-Apollo (2025)",
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