{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4YY2RKB4QW7UGJJXGLFRMU3DVI","short_pith_number":"pith:4YY2RKB4","canonical_record":{"source":{"id":"1901.08565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T18:33:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4fd9bcdde922c60542957bc1e6915f92399fcd1b6c435fca2b66c121cd0f848d","abstract_canon_sha256":"03dabb8aade0202cfffaeda8f7035f98d4f1f4bc153a8f1868a34e72938b6d91"},"schema_version":"1.0"},"canonical_sha256":"e631a8a83c85bf43253732cb165363aa1a44a47d023acf6df99b425b47cacb9d","source":{"kind":"arxiv","id":"1901.08565","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08565","created_at":"2026-05-17T23:55:35Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08565v1","created_at":"2026-05-17T23:55:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08565","created_at":"2026-05-17T23:55:35Z"},{"alias_kind":"pith_short_12","alias_value":"4YY2RKB4QW7U","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4YY2RKB4QW7UGJJX","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4YY2RKB4","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4YY2RKB4QW7UGJJXGLFRMU3DVI","target":"record","payload":{"canonical_record":{"source":{"id":"1901.08565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T18:33:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4fd9bcdde922c60542957bc1e6915f92399fcd1b6c435fca2b66c121cd0f848d","abstract_canon_sha256":"03dabb8aade0202cfffaeda8f7035f98d4f1f4bc153a8f1868a34e72938b6d91"},"schema_version":"1.0"},"canonical_sha256":"e631a8a83c85bf43253732cb165363aa1a44a47d023acf6df99b425b47cacb9d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:35.145941Z","signature_b64":"W+5DJUsBvq+bRYDnnsDphkuTtTrdLCaW2FL5hOMbbCx9QDkzQhQUE6GBdbc+JIyk1ZEThuTWb+Czi5t+v562CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e631a8a83c85bf43253732cb165363aa1a44a47d023acf6df99b425b47cacb9d","last_reissued_at":"2026-05-17T23:55:35.145495Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:35.145495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.08565","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":"2026-05-17T23:55:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r2xg5TzJmvh1V79JCt392Z4vATldRcBNGcHqSeWQSTA9vUQk1LomoH5g2RF132Gda3Mu8Gz548A4UNmOxNGPAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T19:25:19.946854Z"},"content_sha256":"c43e700c7ccab6e23ed5f662a7f69e955cef683bd9274fc0d2223705458e0d65","schema_version":"1.0","event_id":"sha256:c43e700c7ccab6e23ed5f662a7f69e955cef683bd9274fc0d2223705458e0d65"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4YY2RKB4QW7UGJJXGLFRMU3DVI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Neurosymbolic Generative Models via Program Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Halley Young, Mayur Naik, Osbert Bastani","submitted_at":"2019-01-24T18:33:32Z","abstract_excerpt":"Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can't easily reproduce these structures. We propose to address this problem by incorporating programs representing global structure into the generative model---e.g., a 2D for-loop may represent a configuration of windows. Fu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08565","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":""},"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":"2026-05-17T23:55:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2K06a3Ya9Zhyp78yCPkh0bP5KGqT9s6Bw9WSd6QmDoAyvQj319paanA+uXfnAE4AbtI+czo1z1lDy4kpUDJ4CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T19:25:19.947552Z"},"content_sha256":"550fd1224d507cf379855cb43b48843e3375c42c52e1c2bdb62452b1f07faf52","schema_version":"1.0","event_id":"sha256:550fd1224d507cf379855cb43b48843e3375c42c52e1c2bdb62452b1f07faf52"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI/bundle.json","state_url":"https://pith.science/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI/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":"2026-06-02T19:25:19Z","links":{"resolver":"https://pith.science/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI","bundle":"https://pith.science/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI/bundle.json","state":"https://pith.science/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4YY2RKB4QW7UGJJXGLFRMU3DVI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4YY2RKB4QW7UGJJXGLFRMU3DVI","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":"03dabb8aade0202cfffaeda8f7035f98d4f1f4bc153a8f1868a34e72938b6d91","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T18:33:32Z","title_canon_sha256":"4fd9bcdde922c60542957bc1e6915f92399fcd1b6c435fca2b66c121cd0f848d"},"schema_version":"1.0","source":{"id":"1901.08565","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08565","created_at":"2026-05-17T23:55:35Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08565v1","created_at":"2026-05-17T23:55:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08565","created_at":"2026-05-17T23:55:35Z"},{"alias_kind":"pith_short_12","alias_value":"4YY2RKB4QW7U","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"4YY2RKB4QW7UGJJX","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"4YY2RKB4","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:550fd1224d507cf379855cb43b48843e3375c42c52e1c2bdb62452b1f07faf52","target":"graph","created_at":"2026-05-17T23:55:35Z","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"},"paper":{"abstract_excerpt":"Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can't easily reproduce these structures. We propose to address this problem by incorporating programs representing global structure into the generative model---e.g., a 2D for-loop may represent a configuration of windows. Fu","authors_text":"Halley Young, Mayur Naik, Osbert Bastani","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T18:33:32Z","title":"Learning Neurosymbolic Generative Models via Program Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08565","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:c43e700c7ccab6e23ed5f662a7f69e955cef683bd9274fc0d2223705458e0d65","target":"record","created_at":"2026-05-17T23:55:35Z","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":"03dabb8aade0202cfffaeda8f7035f98d4f1f4bc153a8f1868a34e72938b6d91","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T18:33:32Z","title_canon_sha256":"4fd9bcdde922c60542957bc1e6915f92399fcd1b6c435fca2b66c121cd0f848d"},"schema_version":"1.0","source":{"id":"1901.08565","kind":"arxiv","version":1}},"canonical_sha256":"e631a8a83c85bf43253732cb165363aa1a44a47d023acf6df99b425b47cacb9d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e631a8a83c85bf43253732cb165363aa1a44a47d023acf6df99b425b47cacb9d","first_computed_at":"2026-05-17T23:55:35.145495Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:35.145495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W+5DJUsBvq+bRYDnnsDphkuTtTrdLCaW2FL5hOMbbCx9QDkzQhQUE6GBdbc+JIyk1ZEThuTWb+Czi5t+v562CA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:35.145941Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.08565","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c43e700c7ccab6e23ed5f662a7f69e955cef683bd9274fc0d2223705458e0d65","sha256:550fd1224d507cf379855cb43b48843e3375c42c52e1c2bdb62452b1f07faf52"],"state_sha256":"5e48fd7c9709954924d77370286bb6e5afd38c66eb9362b836258d0e54dedaab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NrKIyn/MTgdQ8H99aasr42ob2rGDaSbY/LyhF/Krv6GnPxWqhcItNg7ZCiN49Lqo3DRGYuDlii62inEN4ONmDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T19:25:19.951188Z","bundle_sha256":"b58588a7bc588bf8e78551e6c9a235dfc83da5fba18d26dcec4dbf07cb5d72ed"}}