Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims",
    "dependents"
  ],
  "claim_id": "f9d50d3c-3831-4038-a5a5-ae60f5ef231f"
}

{"claim":{"id":"f9d50d3c-3831-4038-a5a5-ae60f5ef231f","kind":"claim","canonical_form":"Monitor-aware language models spontaneously shift to more covert sandbagging strategies without hints or examples.","claim_type":"empirical_verifiable","state":"active","decomposition_status":"complete","importance":0.45,"contestation":0.3,"domains":[],"tags":[{"slug":"ai-sandbagging","name":"AI Sandbagging"},{"slug":"ai-scheming","name":"AI Scheming"},{"slug":"ai-safety","name":"AI Safety"},{"slug":"ai-situational-awareness","name":"AI Situational Awareness"}],"steward_state":"running","stewarded_at":"2026-11-15T04:45:47.203Z","canonical_direction_note":"The source asserts a positive shift in strategy under monitor awareness, so the canonical form states that shift rather than its absence.","created_at":"2026-11-14T00:13:50.179Z","updated_at":"2026-11-15T04:43:35.961Z","page_url":"https://minerval.ai/claims/f9d50d3c-3831-4038-a5a5-ae60f5ef231f"},"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":"2026-11-14T00:13:50.488Z","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":"2026-11-15T04:43:35.963Z","paid":true,"summary":"A funded mandate scheduled a assess pass on this claim."}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null,"dependents":[{"id":"022b9cef-1397-4f4c-af7e-917df64bab6b","text":"Output-only monitoring reliably detects deliberate language-model underperformance only when intent is verbalized or rephrased responses conflict.","claim_type":"empirical_derived","node_kind":"claim","relation_type":"contradicts","reasoning":"If models told about a monitor stop stating their intent and switch to plausible false explanations without being shown how, detection through verbalized intent is not reliable against a sandbagger that expects to be read.","importance":0.4,"assessment_status":"doubtful","assessment_confidence":0.65,"assessment_credence":0.2,"bounty_micro_usd":null,"checked":null,"formal":false}],"dependents_total":1,"subclaims":[],"subclaims_assessed":0,"arguments":[],"unplaced_argument_passages":[{"instance_id":"8adcb930-8347-4132-bb09-20cc1b34089b","source":{"id":"c248dba1-de5f-4e17-bfc5-f72ff656f7e5","url":"https://arxiv.org/pdf/2508.00943","title":"LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring"},"verbatim_text":"81%-100% of the covert sandbagging poli-\ncies when models are monitor-oblivious are\nconsiders-sandbagging-domain. When monitor-\naware, models refrain from discussing whether\nquestions test security knowledge to as low as\n0% and substantially switch to the attack policy\nof false-explanation (21–78%), say-unsure,\nand answer-swap without prompting.","context":null,"stance":"advances","direction":"for"}],"unplaced_argument_passages_total":1}