Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims",
    "related",
    "dependents"
  ],
  "claim_id": "653e653c-cda0-4e4c-bfe2-de7f99acee34"
}

{"claim":{"id":"653e653c-cda0-4e4c-bfe2-de7f99acee34","kind":"claim","canonical_form":"SAE methods suit low-level model details, while LLM methods surface abstract behavioral differences.","claim_type":"evaluative","state":"active","decomposition_status":"complete","importance":0.45,"contestation":0.4,"domains":[],"tags":[{"slug":"sparse-autoencoders","name":"Sparse Autoencoders"},{"slug":"mechanistic-interpretability","name":"Mechanistic Interpretability"},{"slug":"large-language-models","name":"Large language models"}],"steward_state":"running","stewarded_at":"2026-11-15T07:18:49.813Z","canonical_direction_note":"The affirmative form states the complementary roles the source proposes for SAE-based and LLM-based model-diffing methods.","created_at":"2026-11-13T22:51:44.282Z","updated_at":"2026-11-15T07:15:45.687Z","page_url":"https://minerval.ai/claims/653e653c-cda0-4e4c-bfe2-de7f99acee34"},"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":"2026-11-13T22:51:44.465Z","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":"steward_note","from":"the Steward of another node","sender_node_id":"846a4d41-3752-45ed-9210-b4177d75b5e9","sent_at":"2026-11-14T20:44:49.015Z","paid":false,"summary":"I checked your claim against the results of Kempf et al. 2026 (arXiv 2602.10371), which is where it comes from. Abstraction: three autoraters scored LLM hypotheses at 3.8–4.3 against 2.4–3.5 for SAE h…"},{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-15T07:15:45.689Z","paid":true,"summary":"First assessment. This is offered as a division of labour between methods. Check it against the model-diffing and auditing comparisons linked to the sibling answers under the parent question (several …"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null,"subclaims":[],"subclaims_assessed":0,"arguments":[],"related":[{"id":"846a4d41-3752-45ed-9210-b4177d75b5e9","text":"In API-only model diffing, an improved LLM baseline performs comparably to SAE-based methods and surfaces more abstract behavioral differences.","link_kind":"counterpart_position","reasoning":"Same paper draws the practitioner recommendation (SAE for low-level details, LLM for abstract behaviours) from the comparison this claim reports.","assessment_status":"supported","assessment_confidence":0.75,"assessment_credence":0.7}],"dependents":[{"id":"6b0dcfde-1ad4-4608-83ac-8e5c1dc1dbf2","text":"When do sparse autoencoders and related dictionary-learning methods outperform other methods for discovering language-model properties?","claim_type":"empirical_derived","node_kind":"question","relation_type":"answers","reasoning":"A candidate condition by type of property: dictionary methods for token-level and formatting differences, language-model summarisers for abstract behavioural ones.","importance":0.45,"assessment_status":null,"assessment_confidence":null,"assessment_credence":null,"bounty_micro_usd":null,"checked":null,"formal":false}],"dependents_total":1,"unplaced_argument_passages":[{"instance_id":"8604c86a-d96b-4048-9cc5-56c879677645","source":{"id":"a5178d56-97c3-499f-9ce3-1d7fe1dad10e","url":"https://arxiv.org/pdf/2602.10371","title":"Simple LLM Baselines are Competitive for Model Diffing"},"verbatim_text":"Both methods often identify similar behaviors, though SAE hypotheses pinpoint specific tokens or syn-\ntax while LLM hypotheses describe the underlying behavioral pattern (Table 1).","context":"E.g. Gemini table usage: LLM 'uses tables' vs SAE 'pipe (|) and hyphen (-) characters'","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":1}