{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QZ4PDBES4S7GUX6GSKCWV77KHY","short_pith_number":"pith:QZ4PDBES","schema_version":"1.0","canonical_sha256":"8678f18492e4be6a5fc692856affea3e3652e6cb2cab4cb7606b4f37db47527f","source":{"kind":"arxiv","id":"2409.18624","version":4},"attestation_state":"computed","paper":{"title":"Unsupervised Cognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Alfredo Ibias, Eduard Alarcon, Enric Guinovart, Guillem Ramirez-Miranda, Hector Antona","submitted_at":"2024-09-27T10:50:49Z","abstract_excerpt":"Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a primitive-based, unsupervised learning approach for decision-making inspired by a novel cognition framework. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We compared our approach with both current state-of-the-art unsupervised learning classification, with current state-of-the-ar"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.18624","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-09-27T10:50:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9ce6321c3f1f1ebcff5f62986d2001a7f7b815f8c396ba0bfa5838020a84bc9c","abstract_canon_sha256":"e481b8810ff90a2ce54887f0d4899ecbc6734f614ea6ac4881b34ce1a012c4f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:04:31.221764Z","signature_b64":"zfHX8rGZWXdayap3zqEsymH4w0kIRNfGOTh/NgV1/gXbUepChOQoGyrhA4lOeR+NZUxZgURa0EYNo8ablInBAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8678f18492e4be6a5fc692856affea3e3652e6cb2cab4cb7606b4f37db47527f","last_reissued_at":"2026-06-02T03:04:31.221262Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:04:31.221262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Cognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Alfredo Ibias, Eduard Alarcon, Enric Guinovart, Guillem Ramirez-Miranda, Hector Antona","submitted_at":"2024-09-27T10:50:49Z","abstract_excerpt":"Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a primitive-based, unsupervised learning approach for decision-making inspired by a novel cognition framework. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We compared our approach with both current state-of-the-art unsupervised learning classification, with current state-of-the-ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18624","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.18624/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.18624","created_at":"2026-06-02T03:04:31.221325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.18624v4","created_at":"2026-06-02T03:04:31.221325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18624","created_at":"2026-06-02T03:04:31.221325+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZ4PDBES4S7G","created_at":"2026-06-02T03:04:31.221325+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZ4PDBES4S7GUX6G","created_at":"2026-06-02T03:04:31.221325+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZ4PDBES","created_at":"2026-06-02T03:04:31.221325+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03045","citing_title":"Optimisation Is Not What You Need","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY","json":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY.json","graph_json":"https://pith.science/api/pith-number/QZ4PDBES4S7GUX6GSKCWV77KHY/graph.json","events_json":"https://pith.science/api/pith-number/QZ4PDBES4S7GUX6GSKCWV77KHY/events.json","paper":"https://pith.science/paper/QZ4PDBES"},"agent_actions":{"view_html":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY","download_json":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY.json","view_paper":"https://pith.science/paper/QZ4PDBES","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.18624&json=true","fetch_graph":"https://pith.science/api/pith-number/QZ4PDBES4S7GUX6GSKCWV77KHY/graph.json","fetch_events":"https://pith.science/api/pith-number/QZ4PDBES4S7GUX6GSKCWV77KHY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY/action/storage_attestation","attest_author":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY/action/author_attestation","sign_citation":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY/action/citation_signature","submit_replication":"https://pith.science/pith/QZ4PDBES4S7GUX6GSKCWV77KHY/action/replication_record"}},"created_at":"2026-06-02T03:04:31.221325+00:00","updated_at":"2026-06-02T03:04:31.221325+00:00"}