{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LZM74RXIAWB3YUVWS6HF2FDNVN","short_pith_number":"pith:LZM74RXI","schema_version":"1.0","canonical_sha256":"5e59fe46e80583bc52b6978e5d146dab5b19ee359142800944850c3213ae3a62","source":{"kind":"arxiv","id":"2107.13377","version":3},"attestation_state":"computed","paper":{"title":"Learning to solve complex tasks by growing knowledge culturally across generations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Brin Harper, Jason Madeano, Joshua B. Tenenbaum, Michael Henry Tessler, Noah D. Goodman, Pedro A. Tsividis","submitted_at":"2021-07-28T14:09:40Z","abstract_excerpt":"Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn rests on language: language is the richest record of what previous generations believed, valued, and practiced, and how these evolved over time. The power and mechanisms of language as a means of cultural learning, however, are not well understood, and as a result, current AI systems do not leverage language as a means for cultural knowledge transmission. Here, we take a first step towards reverse-engineering cultural l"},"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":"2107.13377","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-28T14:09:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"93f5a34341758551f4c57c200e19a4c6b9ce647febc399f9bf015cf833ecd2a3","abstract_canon_sha256":"436617170a91dca5c14d7f72bdc67f0fd33cb50a4da6f7db1fb349bc64379dfa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:41:25.998921Z","signature_b64":"feFTY+Shaiz7556/cff19s6IAiBrCORokop8dKYfJeA9cTIrCCMPMtjl+VUZhnzhaBAxe2uxydKOCJTZsdJsAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e59fe46e80583bc52b6978e5d146dab5b19ee359142800944850c3213ae3a62","last_reissued_at":"2026-07-05T03:41:25.998504Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:41:25.998504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to solve complex tasks by growing knowledge culturally across generations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Brin Harper, Jason Madeano, Joshua B. Tenenbaum, Michael Henry Tessler, Noah D. Goodman, Pedro A. Tsividis","submitted_at":"2021-07-28T14:09:40Z","abstract_excerpt":"Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn rests on language: language is the richest record of what previous generations believed, valued, and practiced, and how these evolved over time. The power and mechanisms of language as a means of cultural learning, however, are not well understood, and as a result, current AI systems do not leverage language as a means for cultural knowledge transmission. Here, we take a first step towards reverse-engineering cultural l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.13377","kind":"arxiv","version":3},"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/2107.13377/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":"2107.13377","created_at":"2026-07-05T03:41:25.998562+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.13377v3","created_at":"2026-07-05T03:41:25.998562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.13377","created_at":"2026-07-05T03:41:25.998562+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZM74RXIAWB3","created_at":"2026-07-05T03:41:25.998562+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZM74RXIAWB3YUVW","created_at":"2026-07-05T03:41:25.998562+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZM74RXI","created_at":"2026-07-05T03:41:25.998562+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.19010","citing_title":"A theory of appropriateness with applications to generative artificial intelligence","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN","json":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN.json","graph_json":"https://pith.science/api/pith-number/LZM74RXIAWB3YUVWS6HF2FDNVN/graph.json","events_json":"https://pith.science/api/pith-number/LZM74RXIAWB3YUVWS6HF2FDNVN/events.json","paper":"https://pith.science/paper/LZM74RXI"},"agent_actions":{"view_html":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN","download_json":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN.json","view_paper":"https://pith.science/paper/LZM74RXI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.13377&json=true","fetch_graph":"https://pith.science/api/pith-number/LZM74RXIAWB3YUVWS6HF2FDNVN/graph.json","fetch_events":"https://pith.science/api/pith-number/LZM74RXIAWB3YUVWS6HF2FDNVN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN/action/storage_attestation","attest_author":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN/action/author_attestation","sign_citation":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN/action/citation_signature","submit_replication":"https://pith.science/pith/LZM74RXIAWB3YUVWS6HF2FDNVN/action/replication_record"}},"created_at":"2026-07-05T03:41:25.998562+00:00","updated_at":"2026-07-05T03:41:25.998562+00:00"}