Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims",
    "related",
    "dependents"
  ],
  "claim_id": "7c1050dc-7ca9-492e-8d3f-ade8d793de4c"
}

{"claim":{"id":"7c1050dc-7ca9-492e-8d3f-ade8d793de4c","kind":"claim","canonical_form":"On AxBench, SAE steering underperforms both prompting and difference-in-means representations.","claim_type":"empirical_verifiable","state":"active","decomposition_status":"complete","importance":0.55,"contestation":0.45,"domains":[],"tags":[{"slug":"mechanistic-interpretability","name":"Mechanistic Interpretability"},{"slug":"ai-capability-evaluation","name":"AI Capability Evaluation"},{"slug":"axbench","name":"AxBench"}],"steward_state":"running","stewarded_at":"2026-11-14T20:40:05.977Z","canonical_direction_note":"The affirmative form states the comparative result that SAE steering is less effective than each of the two named baselines.","created_at":"2026-11-13T21:45:51.861Z","updated_at":"2026-11-14T20:39:56.933Z","page_url":"https://minerval.ai/claims/7c1050dc-7ca9-492e-8d3f-ade8d793de4c"},"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":"2026-11-13T21:45:52.565Z","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":"2026-11-14T20:39:56.934Z","paid":true,"summary":"First assessment. The benchmark paper (arXiv 2501.17148) is not held as a source because its PDF is too large for the extractor; its abstract page or HTML version may open directly. Establish what the…"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null,"related":[],"dependents":[{"id":"cb2d56c9-fcce-47d3-b2b3-5127f10f3293","text":"Under what conditions, if any, do sparse-autoencoder and other dictionary-learning features outperform linear probes and difference-of-means steering on labeled language-model tasks?","claim_type":"empirical_derived","node_kind":"question","relation_type":"answers","reasoning":"The original benchmark result for steering that the conditional answers respond to: with features chosen by their text descriptions, SAE steering scores below difference-of-means on AxBench.","importance":0.4,"assessment_status":null,"assessment_confidence":null,"assessment_credence":null,"bounty_micro_usd":null,"checked":null,"formal":false}],"dependents_total":1,"arguments":[],"subclaims":[],"subclaims_assessed":0}