Step 2: tool results

get_claim

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  "claim_id": "4dbe502e-402c-4ae9-8c6f-57a252a0d324"
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{"claim":{"id":"4dbe502e-402c-4ae9-8c6f-57a252a0d324","kind":"claim","canonical_form":"Sparse autoencoders are powerful tools for discovering previously unknown concepts represented in language models.","claim_type":"evaluative","state":"active","decomposition_status":"complete","importance":0.6,"contestation":0.6,"domains":[],"tags":[{"slug":"mechanistic-interpretability","name":"Mechanistic Interpretability"},{"slug":"large-language-models","name":"Large language models"}],"steward_state":"running","stewarded_at":"2026-11-14T20:41:38.033Z","canonical_direction_note":"The source advances the affirmative thesis that sparse autoencoders enable discovery of unknown concepts, rather than denying or merely posing it.","created_at":"2026-11-13T21:57:14.387Z","updated_at":"2026-11-14T20:39:57.066Z","page_url":"https://minerval.ai/claims/4dbe502e-402c-4ae9-8c6f-57a252a0d324"},"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":"2026-11-13T21:57:19.319Z","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":"2026-11-14T20:39:57.068Z","paid":true,"summary":"First assessment. This is the affirmative position, so give it its strongest form: read the position paper arguing for SAEs as discovery tools (arXiv 2506.23845 is likely the source) and the concrete …"}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null,"subclaims":[],"subclaims_assessed":0,"related":[],"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":"The widely cited position that dictionary methods come into their own whenever the concept is not known in advance. It is the prominent affirmative answer and should be shown beside the controlled comparisons.","importance":0.45,"assessment_status":null,"assessment_confidence":null,"assessment_credence":null,"bounty_micro_usd":null,"checked":null,"formal":false}],"dependents_total":1,"arguments":[],"unplaced_argument_passages":[{"instance_id":"6aedce88-e38e-4a16-9b0b-fb637fb6def9","source":{"id":"cfaeaedc-6d7d-49fd-9a30-9f4874fcbb27","url":"https://arxiv.org/pdf/2506.23845","title":"Position: Use Sparse Autoencoders to Discover Unknowns"},"verbatim_text":"In both cases, our\nhope is to discover concepts that satisfy a property of inter-\nest, out of intractably many possibilities. SAEs produce a\nset of concepts that is both tractable and expressive, after\nwhich selecting a subset of concepts that are relevant to the\ntask is straightforward.","context":"Explaining why SAEs succeed at hypothesis generation and LLM biology.","stance":"advances","direction":"for"},{"instance_id":"1c248dc7-4cd3-4340-9b76-318a3c8b7188","source":{"id":"cfaeaedc-6d7d-49fd-9a30-9f4874fcbb27","url":"https://arxiv.org/pdf/2506.23845","title":"Position: Use Sparse Autoencoders to Discover Unknowns"},"verbatim_text":"Crucially, however, these baselines\nare less equipped to perform another simple task: to enumer-\nate a list of unknown concepts that satisfy some objective.","context":"Contrasting baselines that win on known-concept tasks with SAEs' comparative advantage.","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":2}