{"as_of":"2026-08-10T23:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:381d89d7036fd9e2b073ed769f02ae38f229efd56bde1205acb06018ae035004","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:57:40.257682Z","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":"2026-05-25T07:20:28.573534Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.01717","last_updated":"2021-09-30T22:09:28Z","snapshot_observed_at":"2026-08-10T23:17:13.476626Z","submitted_at":"2021-09-30T22:09:28Z","title":"Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.01717","snapshot_observed_at":"2026-08-06T21:57:40.257682Z","title":"Molecule3d: A benchmark for predicting 3d geometries from molecular graphs","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23008","last_updated":"2025-07-08T22:06:07Z","snapshot_observed_at":"2026-08-09T13:36:30.106441Z","submitted_at":"2025-06-28T20:45:27Z","title":"A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T21:57:40.257682Z"},"links":{"cited_paper":"/paper/2110.01717","citing_paper":"/paper/2506.23008"},"observation_digest":"sha256:7d0111ca9760721a50afa05a41cbe9994c993fcc1a759f1cd35c9f32894d5ac7","observation_id":"88142563-9b1c-49d6-955e-3f02f5f6491e","resolution":{"observed_at":"2026-08-06T21:57:40.257682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.01717","last_updated":"2021-09-30T22:09:28Z","snapshot_observed_at":"2026-08-10T23:17:13.476626Z","submitted_at":"2021-09-30T22:09:28Z","title":"Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs","version":1},"cited_work":{"arxiv_id":"2110.01717","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.01717","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Molecule3d: A benchmark for predicting 3d geometries from molecular graphs","venue":null,"work_id":"9fe7acea-dc89-431a-b122-5523e235c2ac","year":2021},"citing_paper":{"arxiv_id":"2512.22597","last_updated":"2026-05-22T12:56:03Z","snapshot_observed_at":"2026-08-03T23:39:50.738932Z","submitted_at":"2025-12-27T14:00:22Z","title":"Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-25T07:19:33.319408Z"},"links":{"cited_paper":"/paper/2110.01717","citing_paper":"/paper/2512.22597"},"observation_digest":"sha256:f2076e016ac7c172bd0f3cb53cac2799cce33eb566f3a76991b7f91a56e0e446","observation_id":"680743b2-0fef-43c5-af33-891fa705b4c8","resolution":{"observed_at":"2026-05-25T07:20:28.576127Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.01717","last_updated":"2021-09-30T22:09:28Z","snapshot_observed_at":"2026-08-10T23:17:13.476626Z","submitted_at":"2021-09-30T22:09:28Z","title":"Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.01717","snapshot_observed_at":"2026-08-01T13:34:17.949375Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.19083","last_updated":"2026-07-29T09:42:55Z","snapshot_observed_at":"2026-08-04T06:25:35.235202Z","submitted_at":"2026-07-21T13:18:59Z","title":"GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T13:34:17.949375Z"},"links":{"cited_paper":"/paper/2110.01717","citing_paper":"/paper/2607.19083"},"observation_digest":"sha256:29007484592ef1399cccda901844382938f5681ac09e018a6865d3745ec6aaa8","observation_id":"fc43c7f1-89e3-4263-ac03-110be2d38c95","resolution":{"observed_at":"2026-08-01T13:34:17.949375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2110.01717/citation-record","integrity":"/paper/2110.01717/integrity","json":"/paper/2110.01717/citation-record.json","paper":"/paper/2110.01717"},"outbound":[],"paper":{"arxiv_id":"2110.01717","last_updated":"2021-09-30T22:09:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T23:17:13.476626Z","submitted_at":"2021-09-30T22:09:28Z","title":"Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2110.01717."}