{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:VW2CUMRFUH3G2H7DVYWSU7COOZ","short_pith_number":"pith:VW2CUMRF","canonical_record":{"source":{"id":"2011.13045","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-25T22:10:32Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"6d316811f7fff8780d879a8ad16ee7e783fea683c63736567a202981f295cd6c","abstract_canon_sha256":"c392733963b80b4165b860ebce3fb0510db5879d1531efa3a2c7aa446bd83a17"},"schema_version":"1.0"},"canonical_sha256":"adb42a3225a1f66d1fe3ae2d2a7c4e76464120a8cf0062e78a450a0868744441","source":{"kind":"arxiv","id":"2011.13045","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.13045","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"arxiv_version","alias_value":"2011.13045v4","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.13045","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"pith_short_12","alias_value":"VW2CUMRFUH3G","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"pith_short_16","alias_value":"VW2CUMRFUH3G2H7D","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"pith_short_8","alias_value":"VW2CUMRF","created_at":"2026-07-05T04:07:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:VW2CUMRFUH3G2H7DVYWSU7COOZ","target":"record","payload":{"canonical_record":{"source":{"id":"2011.13045","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-25T22:10:32Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"6d316811f7fff8780d879a8ad16ee7e783fea683c63736567a202981f295cd6c","abstract_canon_sha256":"c392733963b80b4165b860ebce3fb0510db5879d1531efa3a2c7aa446bd83a17"},"schema_version":"1.0"},"canonical_sha256":"adb42a3225a1f66d1fe3ae2d2a7c4e76464120a8cf0062e78a450a0868744441","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:07:36.832359Z","signature_b64":"HKIjpLvpSlfFpLa9nrP9pyxOW+C3T/fW0bh8Gi5GHuKJglW+8rRDFUte3NMsVO+lLScKWu82abnW/Pa2jM1sBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adb42a3225a1f66d1fe3ae2d2a7c4e76464120a8cf0062e78a450a0868744441","last_reissued_at":"2026-07-05T04:07:36.831996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:07:36.831996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.13045","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:07:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gUkVjftyoSjzYLCxcvFD0EatAwNSyFjyU1MAvY+48qFUB0UOdcrBU1jeML6xc2i900G7LHMK0XrPxD+v8+eZBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T13:01:14.910595Z"},"content_sha256":"cb14d5e03fb80d9936200c90581355a0a48f5cbc13312d59d40de4cd18a20985","schema_version":"1.0","event_id":"sha256:cb14d5e03fb80d9936200c90581355a0a48f5cbc13312d59d40de4cd18a20985"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:VW2CUMRFUH3G2H7DVYWSU7COOZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Daniel Ritchie, Homer Walke, R. Kenny Jones","submitted_at":"2020-11-25T22:10:32Z","abstract_excerpt":"Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more. Training models to perform this task is complicated because paired (shape, program) data is not readily available for many domains, making exact supervised learning infeasible. However, it is possible to get paired data by compromising the accuracy of either the assigned program labels or the shape distribution. Wake-sleep methods use samples from a generative model of shape programs to approximate the distribution of real shapes. In self-training, shapes are passed through a recognition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.13045","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/2011.13045/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":"2026-07-05T04:07:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rq4l3M+oxuECjIA/v0rODy/yjqKDNISNrtg6MQ8xg9d8YswLGKGVxtteyaf9CG5yoZ6G96PIMIo7zDzBhw4BCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T13:01:14.911521Z"},"content_sha256":"b99c222c8aa05e6917e5008da3047140e3e7b418064298b1f471c13412cee4cc","schema_version":"1.0","event_id":"sha256:b99c222c8aa05e6917e5008da3047140e3e7b418064298b1f471c13412cee4cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ/bundle.json","state_url":"https://pith.science/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ/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":"2026-08-06T13:01:14Z","links":{"resolver":"https://pith.science/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ","bundle":"https://pith.science/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ/bundle.json","state":"https://pith.science/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VW2CUMRFUH3G2H7DVYWSU7COOZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VW2CUMRFUH3G2H7DVYWSU7COOZ","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":"c392733963b80b4165b860ebce3fb0510db5879d1531efa3a2c7aa446bd83a17","cross_cats_sorted":["cs.GR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-25T22:10:32Z","title_canon_sha256":"6d316811f7fff8780d879a8ad16ee7e783fea683c63736567a202981f295cd6c"},"schema_version":"1.0","source":{"id":"2011.13045","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.13045","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"arxiv_version","alias_value":"2011.13045v4","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.13045","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"pith_short_12","alias_value":"VW2CUMRFUH3G","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"pith_short_16","alias_value":"VW2CUMRFUH3G2H7D","created_at":"2026-07-05T04:07:36Z"},{"alias_kind":"pith_short_8","alias_value":"VW2CUMRF","created_at":"2026-07-05T04:07:36Z"}],"graph_snapshots":[{"event_id":"sha256:b99c222c8aa05e6917e5008da3047140e3e7b418064298b1f471c13412cee4cc","target":"graph","created_at":"2026-07-05T04:07:36Z","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/2011.13045/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more. Training models to perform this task is complicated because paired (shape, program) data is not readily available for many domains, making exact supervised learning infeasible. However, it is possible to get paired data by compromising the accuracy of either the assigned program labels or the shape distribution. Wake-sleep methods use samples from a generative model of shape programs to approximate the distribution of real shapes. In self-training, shapes are passed through a recognition","authors_text":"Daniel Ritchie, Homer Walke, R. Kenny Jones","cross_cats":["cs.GR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-25T22:10:32Z","title":"PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.13045","kind":"arxiv","version":4},"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:cb14d5e03fb80d9936200c90581355a0a48f5cbc13312d59d40de4cd18a20985","target":"record","created_at":"2026-07-05T04:07:36Z","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":"c392733963b80b4165b860ebce3fb0510db5879d1531efa3a2c7aa446bd83a17","cross_cats_sorted":["cs.GR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-25T22:10:32Z","title_canon_sha256":"6d316811f7fff8780d879a8ad16ee7e783fea683c63736567a202981f295cd6c"},"schema_version":"1.0","source":{"id":"2011.13045","kind":"arxiv","version":4}},"canonical_sha256":"adb42a3225a1f66d1fe3ae2d2a7c4e76464120a8cf0062e78a450a0868744441","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adb42a3225a1f66d1fe3ae2d2a7c4e76464120a8cf0062e78a450a0868744441","first_computed_at":"2026-07-05T04:07:36.831996Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:07:36.831996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HKIjpLvpSlfFpLa9nrP9pyxOW+C3T/fW0bh8Gi5GHuKJglW+8rRDFUte3NMsVO+lLScKWu82abnW/Pa2jM1sBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:07:36.832359Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.13045","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb14d5e03fb80d9936200c90581355a0a48f5cbc13312d59d40de4cd18a20985","sha256:b99c222c8aa05e6917e5008da3047140e3e7b418064298b1f471c13412cee4cc"],"state_sha256":"55e1be315a9793343e08386db778ce4f7475afd634dad579894bf34a62119c01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h7nnfgrBfGXpPHTGADR6FYtQsBUAUAIeig3/3reW+stynKMOv0KPeJhUexBzKC1u2uviQHWLkhTA3eVeUNo4BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T13:01:14.918888Z","bundle_sha256":"89e99f66c054f3ab6f60d82dc269b48b1d63381f609cbfa6593bfef63e32373a"}}