{"as_of":"2026-08-09T04:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:25853215c44ae78884b81dea9cd9738bf6c70a9a95a23458ee7f886fe5d1446f","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:37:30.533798Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-23T04:55:25.019670Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":"2412.17808","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":"909ec5fb-9202-4a70-b7b9-4e11eebd7b7b","year":2024},"citing_paper":{"arxiv_id":"2501.12202","last_updated":"2026-05-14T15:41:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-21T15:16:54Z","title":"Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation","version":5},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-23T04:53:58.465445Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2501.12202"},"observation_digest":"sha256:a3b21793162015824ba153f72b1040e3754fc0260acb66cfdaa2a56c776424f7","observation_id":"d27782ef-78ba-4892-a36b-9110f9b00b57","resolution":{"observed_at":"2026-05-23T04:55:25.022622Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-07T15:37:30.533798Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14521","last_updated":"2025-06-12T04:10:04Z","snapshot_observed_at":"2026-08-09T01:18:07.901275Z","submitted_at":"2025-05-20T15:44:54Z","title":"Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:37:30.533798Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2505.14521"},"observation_digest":"sha256:f60e69674a7769c33a35d00f32bca07a4db056f99b038e7ac0b310f37defde63","observation_id":"8e12d9a5-5f3a-4739-a2b3-9a5d41fb6cd5","resolution":{"observed_at":"2026-08-07T15:37:30.533798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-07T14:50:37.717407Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17412","last_updated":"2025-05-26T17:47:04Z","snapshot_observed_at":"2026-08-07T14:45:22.746516Z","submitted_at":"2025-05-23T02:58:01Z","title":"Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:50:37.717407Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2505.17412"},"observation_digest":"sha256:9767879c34e0d21c247def5f2910f06af12e55bcf2c45dda251a126e0050a119","observation_id":"97a57fc2-e2f3-4ceb-94c3-49e3bb0e1cfc","resolution":{"observed_at":"2026-08-07T14:50:37.717407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-07T14:17:22.780129Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19492","last_updated":"2025-05-26T04:21:18Z","snapshot_observed_at":"2026-08-08T17:50:45.822873Z","submitted_at":"2025-05-26T04:21:18Z","title":"ViewCraft3D: High-Fidelity and View-Consistent 3D Vector Graphics Synthesis","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:17:22.780129Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2505.19492"},"observation_digest":"sha256:76b9af12126200a553dd241f024a0907d972acb187d52c3db0e07e1360e8437a","observation_id":"34e63545-777f-4cd9-8ae5-b2700879bc6a","resolution":{"observed_at":"2026-08-07T14:17:22.780129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-07T12:53:36.388844Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23253","last_updated":"2025-05-29T08:58:41Z","snapshot_observed_at":"2026-08-08T23:41:05.183010Z","submitted_at":"2025-05-29T08:58:41Z","title":"UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:53:36.388844Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2505.23253"},"observation_digest":"sha256:ce04ced6229770be7b356cad464300bf7286bba9edb423baab3ebfc4b7245254","observation_id":"8beeccf5-00b7-4158-8e7a-9bb468782fef","resolution":{"observed_at":"2026-08-07T12:53:36.388844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-07T10:18:00.443883Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders.arXiv preprint arXiv:2412.17808, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05573","last_updated":"2025-06-05T20:30:28Z","snapshot_observed_at":"2026-08-07T10:11:35.802790Z","submitted_at":"2025-06-05T20:30:28Z","title":"PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:00.443883Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2506.05573"},"observation_digest":"sha256:5b3d158d264115145e7f6079d1dcd6a839f20e8eca28be2243e2995f546a0ef7","observation_id":"90e8cd61-1c5b-4001-8f96-263fe5a27353","resolution":{"observed_at":"2026-08-07T10:18:00.443883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-07T04:40:13.486494Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders.arXiv preprint arXiv:2412.17808, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09980","last_updated":"2025-06-11T17:55:03Z","snapshot_observed_at":"2026-08-08T06:47:57.252857Z","submitted_at":"2025-06-11T17:55:03Z","title":"Efficient Part-level 3D Object Generation via Dual Volume Packing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T04:40:13.486494Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2506.09980"},"observation_digest":"sha256:df7b1406935984a1802d4075f7653d07ea4825c9480158a7896728ad554a94bb","observation_id":"11cc84ce-1056-4ae2-a2b3-9a96887e9b1e","resolution":{"observed_at":"2026-08-07T04:40:13.486494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":"2412.17808","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":"909ec5fb-9202-4a70-b7b9-4e11eebd7b7b","year":2024},"citing_paper":{"arxiv_id":"2506.15442","last_updated":"2025-06-18T13:14:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-18T13:14:46Z","title":"Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-17T23:10:17.659621Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2506.15442"},"observation_digest":"sha256:d208cd5807fa13b8cfd856efab5e1c287ebe7e8bdc948003a95a775dff89d9ab","observation_id":"ee6af1ee-dadb-4f2a-88d4-abb9f40f0e83","resolution":{"observed_at":"2026-05-17T23:10:17.723190Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-06T16:30:04.577406Z","title":"Dora: Sampling and benchmarking for 3D shape variational auto-encoders.arXiv, 2412.17808, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13346","last_updated":"2025-07-19T22:47:47Z","snapshot_observed_at":"2026-08-07T02:49:22.788773Z","submitted_at":"2025-07-17T17:59:47Z","title":"AutoPartGen: Autogressive 3D Part Generation and Discovery","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:30:04.577406Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2507.13346"},"observation_digest":"sha256:6c66dd0fc5e806120beed4ad56fbeb5f44575145c4d7a648c741132f4cc7f32c","observation_id":"b5473c45-12c1-45b0-900a-ce3d027c9897","resolution":{"observed_at":"2026-08-06T16:30:04.577406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-08-05T11:40:22.179122Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.02474","last_updated":"2025-09-02T16:25:12Z","snapshot_observed_at":"2026-08-07T20:38:35.110082Z","submitted_at":"2025-09-02T16:25:12Z","title":"Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T11:40:22.179122Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2509.02474"},"observation_digest":"sha256:b3724139f4672e7de1d6f16415ee35d7c417de7f59c77c4645ba424fabb83c74","observation_id":"41b460c1-a971-4128-9cf6-ed79802bf5e3","resolution":{"observed_at":"2026-08-05T11:40:22.179122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":"2412.17808","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":"909ec5fb-9202-4a70-b7b9-4e11eebd7b7b","year":2024},"citing_paper":{"arxiv_id":"2604.28134","last_updated":"2026-05-16T14:07:01Z","snapshot_observed_at":"2026-07-06T23:13:29.310140Z","submitted_at":"2026-04-30T17:18:05Z","title":"MeshReGen: A Unified 3D Geometry Regeneration Framework","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-07T06:31:01.724502Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2604.28134"},"observation_digest":"sha256:2b921e7bb4893a68f8d0274ada903e53776518d412525aab48d42a9324c0002b","observation_id":"80ae22d5-a1db-4940-9db8-9d15d844b491","resolution":{"observed_at":"2026-05-12T10:21:29.589908Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","version":3},"cited_work":{"arxiv_id":"2412.17808","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.17808","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dora: Sampling and benchmarking for 3d shape variational auto-encoders","venue":null,"work_id":"909ec5fb-9202-4a70-b7b9-4e11eebd7b7b","year":2024},"citing_paper":{"arxiv_id":"2604.28134","last_updated":"2026-05-16T14:07:01Z","snapshot_observed_at":"2026-07-06T23:13:29.310140Z","submitted_at":"2026-04-30T17:18:05Z","title":"MeshReGen: A Unified 3D Geometry Regeneration Framework","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-21T00:50:36.513963Z"},"links":{"cited_paper":"/paper/2412.17808","citing_paper":"/paper/2604.28134"},"observation_digest":"sha256:fe4550c86dbb5f30a2a4e2a6584cec122e94ec5a829735f74ebe93437127d0de","observation_id":"71868cd6-9f5b-4709-b8e2-92d24af756a6","resolution":{"observed_at":"2026-05-21T00:53:53.124223Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.17808/citation-record","integrity":"/paper/2412.17808/integrity","json":"/paper/2412.17808/citation-record.json","paper":"/paper/2412.17808"},"outbound":[],"paper":{"arxiv_id":"2412.17808","last_updated":"2025-03-24T16:41:50Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T20:12:18.144353Z","submitted_at":"2024-12-23T18:59:06Z","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2412.17808."}