{"as_of":"2026-08-09T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d07eea0f62f345f2cd054e91131338c5425f1000d3852bf8b030685c3fb88678","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:26:34.606871Z","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":"2026-07-02T07:06:44.048562Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-08-07T14:26:34.606871Z","title":"Learning protein representations via complete 3d graph networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19014","last_updated":"2025-05-31T08:00:35Z","snapshot_observed_at":"2026-08-07T14:19:32.510384Z","submitted_at":"2025-05-25T07:36:50Z","title":"Tokenizing Electron Cloud in Protein-Ligand Interaction Learning","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T14:26:34.606871Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2505.19014"},"observation_digest":"sha256:34ed9757a847191c22f64a5b3977ec0ef5922a1297457badc93db2b7aeb8baa4","observation_id":"10e4be98-84eb-46c5-8276-a21b5b9fb4de","resolution":{"observed_at":"2026-08-07T14:26:34.606871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-08-04T20:20:51.777739Z","title":"Learning hierarchical protein representations via complete 3d graph networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08707","last_updated":"2025-09-10T15:56:19Z","snapshot_observed_at":"2026-08-09T03:11:20.895458Z","submitted_at":"2025-09-10T15:56:19Z","title":"Tokenizing Loops of Antibodies","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-04T20:20:51.777739Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2509.08707"},"observation_digest":"sha256:59d0d37631fa9d3787b7e88bc8ca962c0e727a84ed2994d97a6face3b94d8cb3","observation_id":"53edb2e4-78d9-4231-a8b4-cb043e8fbfcd","resolution":{"observed_at":"2026-08-04T20:20:51.777739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":"2207.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-07-02T07:06:44.048562Z","title":"arXiv preprint arXiv:2207.12600 , year=","venue":null,"work_id":"20cc34f4-3711-4da6-922e-9b75041fedf0","year":null},"citing_paper":{"arxiv_id":"2605.10985","last_updated":"2026-05-09T07:01:02Z","snapshot_observed_at":"2026-07-06T23:22:52.369277Z","submitted_at":"2026-05-09T07:01:02Z","title":"Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-13T07:20:50.755837Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2605.10985"},"observation_digest":"sha256:42049dca8ae809b3cec033629e12b9a1df03fa8ec322c5fb791f93d31c3b6545","observation_id":"88f20a23-2557-46db-b2a4-8f9312e048f1","resolution":{"observed_at":"2026-05-13T07:22:28.853829Z","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":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":"2207.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-07-02T07:06:44.048562Z","title":"arXiv preprint arXiv:2207.12600 , year=","venue":null,"work_id":"20cc34f4-3711-4da6-922e-9b75041fedf0","year":null},"citing_paper":{"arxiv_id":"2605.21485","last_updated":"2026-05-20T17:59:16Z","snapshot_observed_at":"2026-07-06T23:31:56.199912Z","submitted_at":"2026-05-20T17:59:16Z","title":"EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation","version":1},"reference_index":217,"source":"arxiv_source","source_observed_at":"2026-05-21T05:06:06.626303Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2605.21485"},"observation_digest":"sha256:76ddca6d1ea5e00222840a807efb3dae9953b64a24970e712f7acfbe61064264","observation_id":"fc11725d-5e7a-4c2d-a3cc-50492facc58c","resolution":{"observed_at":"2026-05-21T05:09:38.654394Z","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":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":"2207.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-07-02T07:06:44.048562Z","title":"arXiv preprint arXiv:2207.12600 , year=","venue":null,"work_id":"20cc34f4-3711-4da6-922e-9b75041fedf0","year":null},"citing_paper":{"arxiv_id":"2605.21600","last_updated":"2026-06-26T01:06:09Z","snapshot_observed_at":"2026-08-03T03:48:18.044746Z","submitted_at":"2026-05-20T18:04:32Z","title":"ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning","version":1},"reference_index":217,"source":"arxiv_source","source_observed_at":"2026-05-22T09:29:45.993792Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2605.21600"},"observation_digest":"sha256:87d74adeca250fcd18e042bc8977b027bf26e123ead9b8ae0cb96b09f22940d7","observation_id":"3a76412d-123a-429d-8383-1746e0a9a241","resolution":{"observed_at":"2026-05-22T09:31:22.623413Z","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":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":"2207.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-07-02T07:06:44.048562Z","title":"arXiv preprint arXiv:2207.12600 , year=","venue":null,"work_id":"20cc34f4-3711-4da6-922e-9b75041fedf0","year":null},"citing_paper":{"arxiv_id":"2605.21610","last_updated":"2026-05-20T18:16:52Z","snapshot_observed_at":"2026-07-31T20:59:05.760224Z","submitted_at":"2026-05-20T18:16:52Z","title":"AgForce Enables Antigen-conditioned Generative Antibody Design","version":1},"reference_index":217,"source":"arxiv_source","source_observed_at":"2026-05-22T09:21:17.324165Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2605.21610"},"observation_digest":"sha256:aec9ff59f4d236f4c11c3cd2b1d479d867d90369e58d06386292402de70eb7b8","observation_id":"4943f324-e12b-4907-afd6-f356a316c2c9","resolution":{"observed_at":"2026-05-22T09:21:20.456092Z","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":"2207.12600","last_updated":"2023-03-03T20:06:36Z","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks","version":2},"cited_work":{"arxiv_id":"2207.12600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.12600","snapshot_observed_at":"2026-07-02T07:06:44.048562Z","title":"arXiv preprint arXiv:2207.12600 , year=","venue":null,"work_id":"20cc34f4-3711-4da6-922e-9b75041fedf0","year":null},"citing_paper":{"arxiv_id":"2606.04154","last_updated":"2026-06-02T19:20:25Z","snapshot_observed_at":"2026-08-03T10:59:46.260266Z","submitted_at":"2026-06-02T19:20:25Z","title":"EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning","version":1},"reference_index":195,"source":"arxiv_source","source_observed_at":"2026-06-28T07:12:15.396602Z"},"links":{"cited_paper":"/paper/2207.12600","citing_paper":"/paper/2606.04154"},"observation_digest":"sha256:ddb9fd84b65d8070bc76f8d60b6cf7c17539afa4bc912db87e4025be5d30f319","observation_id":"de799a6a-cfad-46a1-80b4-e68ed2d1b4a4","resolution":{"observed_at":"2026-07-02T07:06:44.049922Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2207.12600/citation-record","integrity":"/paper/2207.12600/integrity","json":"/paper/2207.12600/citation-record.json","paper":"/paper/2207.12600"},"outbound":[],"paper":{"arxiv_id":"2207.12600","last_updated":"2023-03-03T20:06:36Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T13:35:14.805149Z","submitted_at":"2022-07-26T01:55:25Z","title":"Learning Hierarchical Protein Representations via Complete 3D Graph Networks"},"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 7 inbound Pith citation observations for arXiv:2207.12600."}