{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DQN3ROOFER36IM5YJMIVV5LPK7","short_pith_number":"pith:DQN3ROOF","schema_version":"1.0","canonical_sha256":"1c1bb8b9c52477e433b84b115af56f57c420abbb62ab851964e11c6668ed9a74","source":{"kind":"arxiv","id":"2312.05361","version":1},"attestation_state":"computed","paper":{"title":"Emergence and Function of Abstract Representations in Self-Supervised Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Joshua Ching, Quentin RV. Ferry, Takashi Kawai","submitted_at":"2023-12-08T20:47:15Z","abstract_excerpt":"Human intelligence relies in part on our brains' ability to create abstract mental models that succinctly capture the hidden blueprint of our reality. Such abstract world models notably allow us to rapidly navigate novel situations by generalizing prior knowledge, a trait deep learning systems have historically struggled to replicate. However, the recent shift from supervised to self-supervised objectives, combined with expressive transformer-based architectures, have yielded powerful foundation models that appear to learn versatile representations that can support a wide range of downstream t"},"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":"2312.05361","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-12-08T20:47:15Z","cross_cats_sorted":[],"title_canon_sha256":"ae7de2e51b5d6f75e0668956ccb93d3101d6cc9451ae20f8953cd6e52f86b2be","abstract_canon_sha256":"cae42f65a9f0f3659317153b1b789aa24a1d7d9ee18e23da5373e649ea239788"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:03.442926Z","signature_b64":"E85Qr48xp7loglBXOt0pnSlHil8tjnznrqzCitqxmgGzP+kCL1NU0BVyHogG+5RqVpEclBToVHRCszXeiWf3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c1bb8b9c52477e433b84b115af56f57c420abbb62ab851964e11c6668ed9a74","last_reissued_at":"2026-07-05T07:22:03.442498Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:03.442498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Emergence and Function of Abstract Representations in Self-Supervised Transformers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Joshua Ching, Quentin RV. Ferry, Takashi Kawai","submitted_at":"2023-12-08T20:47:15Z","abstract_excerpt":"Human intelligence relies in part on our brains' ability to create abstract mental models that succinctly capture the hidden blueprint of our reality. Such abstract world models notably allow us to rapidly navigate novel situations by generalizing prior knowledge, a trait deep learning systems have historically struggled to replicate. However, the recent shift from supervised to self-supervised objectives, combined with expressive transformer-based architectures, have yielded powerful foundation models that appear to learn versatile representations that can support a wide range of downstream t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.05361","kind":"arxiv","version":1},"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/2312.05361/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":"2312.05361","created_at":"2026-07-05T07:22:03.442541+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.05361v1","created_at":"2026-07-05T07:22:03.442541+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.05361","created_at":"2026-07-05T07:22:03.442541+00:00"},{"alias_kind":"pith_short_12","alias_value":"DQN3ROOFER36","created_at":"2026-07-05T07:22:03.442541+00:00"},{"alias_kind":"pith_short_16","alias_value":"DQN3ROOFER36IM5Y","created_at":"2026-07-05T07:22:03.442541+00:00"},{"alias_kind":"pith_short_8","alias_value":"DQN3ROOF","created_at":"2026-07-05T07:22:03.442541+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.26745","citing_title":"Deep sequence models tend to memorize geometrically; it is unclear why","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7","json":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7.json","graph_json":"https://pith.science/api/pith-number/DQN3ROOFER36IM5YJMIVV5LPK7/graph.json","events_json":"https://pith.science/api/pith-number/DQN3ROOFER36IM5YJMIVV5LPK7/events.json","paper":"https://pith.science/paper/DQN3ROOF"},"agent_actions":{"view_html":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7","download_json":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7.json","view_paper":"https://pith.science/paper/DQN3ROOF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.05361&json=true","fetch_graph":"https://pith.science/api/pith-number/DQN3ROOFER36IM5YJMIVV5LPK7/graph.json","fetch_events":"https://pith.science/api/pith-number/DQN3ROOFER36IM5YJMIVV5LPK7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7/action/storage_attestation","attest_author":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7/action/author_attestation","sign_citation":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7/action/citation_signature","submit_replication":"https://pith.science/pith/DQN3ROOFER36IM5YJMIVV5LPK7/action/replication_record"}},"created_at":"2026-07-05T07:22:03.442541+00:00","updated_at":"2026-07-05T07:22:03.442541+00:00"}