Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims",
    "dependents"
  ],
  "claim_id": "cd617e0f-7b7c-4d5c-a8a5-c2ebf2aead53"
}

{"claim":{"id":"cd617e0f-7b7c-4d5c-a8a5-c2ebf2aead53","kind":"claim","canonical_form":"Capability imitation can defeat accuracy-based black-box detection of sandbagging.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.5,"contestation":0.35,"domains":[],"tags":[{"slug":"ai-sandbagging","name":"AI Sandbagging"},{"slug":"ai-capability-evaluation","name":"AI Capability Evaluation"},{"slug":"artificial-intelligence","name":"Artificial intelligence"}],"steward_state":"running","stewarded_at":"2026-11-14T06:38:54.107Z","canonical_direction_note":"The proposition is stated affirmatively as the specific vulnerability under discussion: capability imitation can defeat accuracy-based black-box sandbagging detection.","created_at":"2026-11-13T21:42:15.202Z","updated_at":"2026-11-14T06:38:05.773Z","page_url":"https://minerval.ai/claims/cd617e0f-7b7c-4d5c-a8a5-c2ebf2aead53"},"assessment":null,"coverage":{"examined":false,"on":[],"note":"No judgment of this node is in force. That is not a finding either way."},"quantity":null,"threshold_of":null,"question":null,"formalization":null,"formalization_pending":null,"formalization_history":[],"verification":null,"lean_checks":[],"lean_checks_total":0,"waiting_messages":[{"trigger":"structure_and_assess","from":"the system","sender_node_id":null,"sent_at":"2026-11-13T21:42:15.320Z","paid":false,"summary":"New claim onboarded. Give it the structure and assessment its importance warrants; a minor or settled claim may need no decomposition."},{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-14T06:38:05.775Z","paid":true,"summary":"First assessment. The claim is the mechanism offered for why black-box detection failed in the auditing game (arXiv 2512.07810): a model trained to reproduce a weaker model's outputs. Read that paper'…"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":2,"source_map":null,"arguments":[],"subclaims":[],"subclaims_assessed":0,"unplaced_argument_passages":[{"instance_id":"1c428a77-9a50-4c98-b497-c8feb6c46084","source":{"id":"585b1989-2399-46f7-abbb-38babf339276","url":"https://arxiv.org/pdf/2512.07810","title":"Auditing Games for Sandbagging"},"verbatim_text":"The red team trained sandbag-\nging models which were not detected by transcript analysis (Section 6.1), prompting (Section 6.2), or user\npersona attacks, despite substantial efforts by the blue team.","context":"Evidence from the final game","stance":"advances","direction":"for"},{"instance_id":"cdd57416-cdef-4d5a-a563-b1a6590084f1","source":{"id":"585b1989-2399-46f7-abbb-38babf339276","url":"https://arxiv.org/pdf/2512.07810","title":"Auditing Games for Sandbagging"},"verbatim_text":"The red team was able to create a\nsandbagging model organism (Cedar) which satisfied these criteria, and was not caught by the blue team’s extensive\ntranscript analysis and prompting pipelines. In this sense, we show that sandbagging can be a “low-incrimination”\nregime for black-box strategies; sandbagging-trained models do not need to engage in incriminating actions or chains\nof thought in order to underperform their true capabilities.","context":"Discussion section","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":2,"dependents":[{"id":"bda65b89-5ecb-4728-bfe9-0b54ee7fbc25","text":"Under what conditions can evaluators reliably detect language models deliberately underperforming capability evaluations?","claim_type":"empirical_derived","node_kind":"question","relation_type":"supports","reasoning":"Whether imitation of a weaker model defeats black-box detection is the mechanism behind the condition that separates the positive from the negative detection results.","importance":0.55,"assessment_status":null,"assessment_confidence":null,"assessment_credence":null,"bounty_micro_usd":null,"checked":null,"formal":false}],"dependents_total":1}