{"as_of":"2026-08-15T08:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b98773d1e83437b3afd82727d73af1b1e35a771dfa5c0acc9a97389520b85b02","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T06:01:04.355118Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-08T13:08:50.367997Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.14655","last_updated":"2022-04-01T22:54:06Z","snapshot_observed_at":"2026-08-15T07:42:08.761877Z","submitted_at":"2021-05-31T00:48:18Z","title":"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.14655","snapshot_observed_at":"2026-08-12T06:01:04.355118Z","title":"Unite: Unitary n-body tensor equivariant network with applications to quantum chemistry","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.19629","last_updated":"2024-11-29T11:25:51Z","snapshot_observed_at":"2026-08-14T10:14:05.043644Z","submitted_at":"2024-11-29T11:25:51Z","title":"OpenQDC: Open Quantum Data Commons","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T06:01:04.355118Z"},"links":{"cited_paper":"/paper/2105.14655","citing_paper":"/paper/2411.19629"},"observation_digest":"sha256:a78810e882a9623b214323739cc00bb2ca98bb013d499ec74b498c200099f192","observation_id":"63bae921-648a-44c3-95cc-0be3247909f8","resolution":{"observed_at":"2026-08-12T06:01:04.355118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.14655","last_updated":"2022-04-01T22:54:06Z","snapshot_observed_at":"2026-08-15T07:42:08.761877Z","submitted_at":"2021-05-31T00:48:18Z","title":"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry","version":4},"cited_work":{"arxiv_id":"2105.14655","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.14655","snapshot_observed_at":"2026-08-08T13:08:50.367997Z","title":"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry","venue":"cs.LG","work_id":"7b205ad8-55c9-4587-bcc4-386f052f4b9e","year":2021},"citing_paper":{"arxiv_id":"2502.07335","last_updated":"2025-03-18T09:13:58Z","snapshot_observed_at":"2026-08-12T16:53:53.276408Z","submitted_at":"2025-02-11T07:53:31Z","title":"The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials","version":2},"reference_index":176,"source":"pdf_text","source_observed_at":"2026-08-08T13:08:49.733233Z"},"links":{"cited_paper":"/paper/2105.14655","citing_paper":"/paper/2502.07335"},"observation_digest":"sha256:2f4e55f32194939984467b5fa40348e716d021c33fa85e622bade5e0c38ee21e","observation_id":"11abc639-c99c-4317-97a6-1dc3e1cc4969","resolution":{"observed_at":"2026-08-08T13:08:50.372871Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2105.14655/citation-record","integrity":"/paper/2105.14655/integrity","json":"/paper/2105.14655/citation-record.json","paper":"/paper/2105.14655"},"outbound":[],"paper":{"arxiv_id":"2105.14655","last_updated":"2022-04-01T22:54:06Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T07:42:08.761877Z","submitted_at":"2021-05-31T00:48:18Z","title":"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.14655."}