{"as_of":"2026-08-08T15:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19b9f7fd1824e1639420b564ddadd31e52bb0dfb589f4d5119996dabf1b9cf45","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T12:16:56.260076Z","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":"2026-08-06T11:07:48.500593Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.11931","last_updated":"2022-10-21T13:06:19Z","snapshot_observed_at":"2026-08-07T14:00:01.223663Z","submitted_at":"2022-10-21T13:06:19Z","title":"Deep Reinforcement Learning for Inverse Inorganic Materials Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.11931","snapshot_observed_at":"2026-08-07T12:16:56.260076Z","title":"Deep reinforcement learning for inverse inorganic materials design","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00198","last_updated":"2025-05-30T20:09:11Z","snapshot_observed_at":"2026-08-07T12:08:08.710712Z","submitted_at":"2025-05-30T20:09:11Z","title":"MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T12:16:56.260076Z"},"links":{"cited_paper":"/paper/2210.11931","citing_paper":"/paper/2506.00198"},"observation_digest":"sha256:fdbe78aef790ec3ebdf265f98c87d14cfa60af9fcbe276d2b7a4ec99c8203f2d","observation_id":"d1069d32-ddec-4311-9056-9b2711d84cc8","resolution":{"observed_at":"2026-08-07T12:16:56.260076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11931","last_updated":"2022-10-21T13:06:19Z","snapshot_observed_at":"2026-08-07T14:00:01.223663Z","submitted_at":"2022-10-21T13:06:19Z","title":"Deep Reinforcement Learning for Inverse Inorganic Materials Design","version":1},"cited_work":{"arxiv_id":"2210.11931","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.11931","snapshot_observed_at":"2026-08-06T11:07:48.500593Z","title":"Deep Reinforcement Learning for Inverse Inorganic Materials Design","venue":"cond-mat.mtrl-sci","work_id":"1b6dfe07-5ba1-47dd-978c-349589202f05","year":2022},"citing_paper":{"arxiv_id":"2507.23160","last_updated":"2025-07-30T23:43:40Z","snapshot_observed_at":"2026-08-06T11:17:36.467822Z","submitted_at":"2025-07-30T23:43:40Z","title":"Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:07:46.535523Z"},"links":{"cited_paper":"/paper/2210.11931","citing_paper":"/paper/2507.23160"},"observation_digest":"sha256:e6a56cc13aad417697b71c90c6a756e33b14486499a3b61751e30b334e3d2f43","observation_id":"8f5e4f8e-06cf-4528-aadb-f970c46786ef","resolution":{"observed_at":"2026-08-06T11:07:48.661650Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2210.11931/citation-record","integrity":"/paper/2210.11931/integrity","json":"/paper/2210.11931/citation-record.json","paper":"/paper/2210.11931"},"outbound":[],"paper":{"arxiv_id":"2210.11931","last_updated":"2022-10-21T13:06:19Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-07T14:00:01.223663Z","submitted_at":"2022-10-21T13:06:19Z","title":"Deep Reinforcement Learning for Inverse Inorganic Materials Design"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.11931."}