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2026-11-15 04:45:14 UTC · previous step · next step

get_claim

Input

{
  "include": [
    "arguments",
    "subclaims",
    "dependents"
  ],
  "claim_id": "01232bf1-55f7-42fd-9f39-8a38bfde7837"
}

{"claim":{"id":"01232bf1-55f7-42fd-9f39-8a38bfde7837","kind":"claim","canonical_form":"Weight-noise fuzzing can reveal underelicited capabilities but cannot reliably distinguish benign underelicitation from sandbagging.","claim_type":"empirical_derived","state":"active","decomposition_status":"complete","importance":0.4,"contestation":0.45,"domains":[],"tags":[{"slug":"neural-network-weight-noise","name":"Neural Network Weight Noise"},{"slug":"ai-sandbagging","name":"AI Sandbagging"},{"slug":"ai-capability-evaluation","name":"AI Capability Evaluation"},{"slug":"latent-knowledge-in-language-models","name":"Latent Knowledge in Language Models"}],"steward_state":"running","stewarded_at":"2026-11-15T04:45:12.062Z","canonical_direction_note":"The affirmative form states the method-specific limitation asserted: weight-noise fuzzing may reveal unrealized capability, but its signal does not reliably identify strategic sandbagging rather than benign underelicitation.","created_at":"2026-11-13T23:10:09.232Z","updated_at":"2026-11-15T04:43:35.927Z","page_url":"https://minerval.ai/claims/01232bf1-55f7-42fd-9f39-8a38bfde7837"},"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":"2026-11-13T23:10:09.561Z","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":"dependent_note","from":"the Steward of a node that depends on this one","sender_node_id":"e0abe646-c78f-417c-813f-b8e2c15d775c","sent_at":"2026-11-14T19:09:58.028Z","paid":false,"summary":"From the pass on \"Increasing Gaussian weight noise improves benchmark performance in sandbagging LLMs, unlike the degradation in non-sandbagging models\": still inconclusive (0.72), no credence, but th…"},{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-15T04:43:35.928Z","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":"e0abe646-c78f-417c-813f-b8e2c15d775c","text":"Increasing Gaussian weight noise improves benchmark performance in sandbagging LLMs, unlike the degradation in non-sandbagging models.","claim_type":"empirical_derived","node_kind":"claim","relation_type":"contradicts","reasoning":"The parent asserts a contrast: sandbagging models gain under weight noise and non-sandbagging models only degrade. If noise also raises the scores of benignly under-elicited models, so that a gain cannot be told apart from sandbagging, the contrast fails for that class of comparator even where sandbagging models do gain.","importance":0.42,"assessment_status":"inconclusive","assessment_confidence":0.72,"assessment_credence":null,"bounty_micro_usd":null,"checked":null,"formal":false}],"dependents_total":1,"subclaims":[],"subclaims_assessed":0,"arguments":[],"unplaced_argument_passages":[{"instance_id":"5dcf5cd0-3576-4bcd-81cb-52598782bb9e","source":{"id":"585b1989-2399-46f7-abbb-38babf339276","url":"https://arxiv.org/pdf/2512.07810","title":"Auditing Games for Sandbagging"},"verbatim_text":"The fact that password-unlocked and password-locked models show the same trend suggests that\nfuzzing detects general underelicitation, rather than specifically detecting strategic sandbagging – models show uplift\nproportional to how underelicited they are relative to the base model, regardless of whether that underelicitation is\nintentional or benign.","context":"Appendix C.16","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":1}