{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2PIH4F5NRQIAGIH52MMP6Z264O","short_pith_number":"pith:2PIH4F5N","canonical_record":{"source":{"id":"2305.19374","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-30T19:30:50Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"436b61492883b83d422a1c20e6ee2bc31fcd128beefddb46cd05437ee402675b","abstract_canon_sha256":"d5090f265ec31dbdf47e643e03eea9b4dbba54a1424520f313246ac1ea103297"},"schema_version":"1.0"},"canonical_sha256":"d3d07e17ad8c100320fdd318ff675ee382956b37c62f6ab609d15df55bb4c316","source":{"kind":"arxiv","id":"2305.19374","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.19374","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"arxiv_version","alias_value":"2305.19374v1","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.19374","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"pith_short_12","alias_value":"2PIH4F5NRQIA","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"pith_short_16","alias_value":"2PIH4F5NRQIAGIH5","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"pith_short_8","alias_value":"2PIH4F5N","created_at":"2026-07-05T06:15:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2PIH4F5NRQIAGIH52MMP6Z264O","target":"record","payload":{"canonical_record":{"source":{"id":"2305.19374","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-30T19:30:50Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"436b61492883b83d422a1c20e6ee2bc31fcd128beefddb46cd05437ee402675b","abstract_canon_sha256":"d5090f265ec31dbdf47e643e03eea9b4dbba54a1424520f313246ac1ea103297"},"schema_version":"1.0"},"canonical_sha256":"d3d07e17ad8c100320fdd318ff675ee382956b37c62f6ab609d15df55bb4c316","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:15:49.442371Z","signature_b64":"PS92UZeYBQ+ZVjlIlt1KroDxOYjrAVQhImICpR4x/+OVVkQSDwbnp/P1HBN2lzKgWfz3bKmk9duLrZoQFk/yDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3d07e17ad8c100320fdd318ff675ee382956b37c62f6ab609d15df55bb4c316","last_reissued_at":"2026-07-05T06:15:49.441864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:15:49.441864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.19374","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:15:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zg4s3LUjPch2WAlMvywnG6hhsC8/XjB46JI3MeRo/gSfDzZoqHTjgTZH2fy/3687CR59uw2+Pu5Yhq+UuuM+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:21:47.141836Z"},"content_sha256":"775906e9f191bfd5b07aaae1a0d02fc40dca318855d691156faee1090512984c","schema_version":"1.0","event_id":"sha256:775906e9f191bfd5b07aaae1a0d02fc40dca318855d691156faee1090512984c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2PIH4F5NRQIAGIH52MMP6Z264O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Compositional diversity in visual concept learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Brenden M. Lake, Reuben Feinman, YanLi Zhou","submitted_at":"2023-05-30T19:30:50Z","abstract_excerpt":"Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do. Here, we study these distinctively human abilities across a range of different types of visual composition, examining how people classify and generate ``alien figures'' with rich relational structure. We also develop a Bayesian program induction model which searches for the best programs for gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.19374","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/2305.19374/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:15:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rqtprfEggniFfxq9kmgxi9ajzigx8Vx0kJZTvrJSyJztOILcm+eRcIZGa4nXGgSUFaYOxdt8u4UtPK3gmvvjAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:21:47.142534Z"},"content_sha256":"89f9a8bfdfcb829aabdf8f5d54633c394458c366fa9442bc05e08935881d808e","schema_version":"1.0","event_id":"sha256:89f9a8bfdfcb829aabdf8f5d54633c394458c366fa9442bc05e08935881d808e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2PIH4F5NRQIAGIH52MMP6Z264O/bundle.json","state_url":"https://pith.science/pith/2PIH4F5NRQIAGIH52MMP6Z264O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2PIH4F5NRQIAGIH52MMP6Z264O/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-09T05:21:47Z","links":{"resolver":"https://pith.science/pith/2PIH4F5NRQIAGIH52MMP6Z264O","bundle":"https://pith.science/pith/2PIH4F5NRQIAGIH52MMP6Z264O/bundle.json","state":"https://pith.science/pith/2PIH4F5NRQIAGIH52MMP6Z264O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2PIH4F5NRQIAGIH52MMP6Z264O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2PIH4F5NRQIAGIH52MMP6Z264O","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d5090f265ec31dbdf47e643e03eea9b4dbba54a1424520f313246ac1ea103297","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-30T19:30:50Z","title_canon_sha256":"436b61492883b83d422a1c20e6ee2bc31fcd128beefddb46cd05437ee402675b"},"schema_version":"1.0","source":{"id":"2305.19374","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.19374","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"arxiv_version","alias_value":"2305.19374v1","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.19374","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"pith_short_12","alias_value":"2PIH4F5NRQIA","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"pith_short_16","alias_value":"2PIH4F5NRQIAGIH5","created_at":"2026-07-05T06:15:49Z"},{"alias_kind":"pith_short_8","alias_value":"2PIH4F5N","created_at":"2026-07-05T06:15:49Z"}],"graph_snapshots":[{"event_id":"sha256:89f9a8bfdfcb829aabdf8f5d54633c394458c366fa9442bc05e08935881d808e","target":"graph","created_at":"2026-07-05T06:15:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.19374/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do. Here, we study these distinctively human abilities across a range of different types of visual composition, examining how people classify and generate ``alien figures'' with rich relational structure. We also develop a Bayesian program induction model which searches for the best programs for gene","authors_text":"Brenden M. Lake, Reuben Feinman, YanLi Zhou","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-30T19:30:50Z","title":"Compositional diversity in visual concept learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.19374","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:775906e9f191bfd5b07aaae1a0d02fc40dca318855d691156faee1090512984c","target":"record","created_at":"2026-07-05T06:15:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d5090f265ec31dbdf47e643e03eea9b4dbba54a1424520f313246ac1ea103297","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-30T19:30:50Z","title_canon_sha256":"436b61492883b83d422a1c20e6ee2bc31fcd128beefddb46cd05437ee402675b"},"schema_version":"1.0","source":{"id":"2305.19374","kind":"arxiv","version":1}},"canonical_sha256":"d3d07e17ad8c100320fdd318ff675ee382956b37c62f6ab609d15df55bb4c316","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3d07e17ad8c100320fdd318ff675ee382956b37c62f6ab609d15df55bb4c316","first_computed_at":"2026-07-05T06:15:49.441864Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:15:49.441864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PS92UZeYBQ+ZVjlIlt1KroDxOYjrAVQhImICpR4x/+OVVkQSDwbnp/P1HBN2lzKgWfz3bKmk9duLrZoQFk/yDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:15:49.442371Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.19374","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:775906e9f191bfd5b07aaae1a0d02fc40dca318855d691156faee1090512984c","sha256:89f9a8bfdfcb829aabdf8f5d54633c394458c366fa9442bc05e08935881d808e"],"state_sha256":"3bba3b2e3e54bee5f3df09e7e6065d386c5d1bddeb1fc6c462cb6e38bdca14db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Enn4xCyaVmn58KV34aT99mCzK6I0WNcTATiDzt9+Xz/Scm7VtE108qs+xUN56eF6vud9XQOgXIcoDGaY3LdBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:21:47.147465Z","bundle_sha256":"7da7a6f44c02923a1b7290ad42b810341563712371d7f1c78a8f403d39ab55d8"}}