{"as_of":"2026-08-18T05:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f9401f2b56a22ed6c619c8c314c57c83424b5cab9f69245578c3d13ba2a9bf78","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":"2026-08-17T06:30:58.91139+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":"2026-08-11T00:24:53.986049Z","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":"2026-05-11T21:36:18.122518Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.00680","last_updated":"2022-08-16T07:58:25Z","snapshot_observed_at":"2026-08-17T08:52:24.067348Z","submitted_at":"2021-11-01T03:47:07Z","title":"GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing","version":2},"cited_work":{"arxiv_id":"2111.00680","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.00680","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e724368c-abf5-4708-a04f-86dc652a4a1a","year":2022},"citing_paper":{"arxiv_id":"2605.05639","last_updated":"2026-08-12T14:28:27Z","snapshot_observed_at":"2026-08-15T23:12:05.817598Z","submitted_at":"2026-05-07T03:47:18Z","title":"TokenStack: A Heterogeneous HBM-PIM Architecture and Runtime for Efficient LLM Inference","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-08T04:43:41.912918Z"},"links":{"cited_paper":"/paper/2111.00680","citing_paper":"/paper/2605.05639"},"observation_digest":"sha256:b25002a82892249847a146c7e42faafd18d6d62ba0c217094b244bee3ca762f3","observation_id":"d48f346b-6031-4a2e-bdd5-0e546a0001f8","resolution":{"observed_at":"2026-05-11T21:36:18.125827Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00680","last_updated":"2022-08-16T07:58:25Z","snapshot_observed_at":"2026-08-17T08:52:24.067348Z","submitted_at":"2021-11-01T03:47:07Z","title":"GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00680","snapshot_observed_at":"2026-08-11T00:24:53.986049Z","title":"GNNear: Accelerating full-batch training of graph neural networks with near-memory processing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.07733","last_updated":"2026-08-07T19:55:45Z","snapshot_observed_at":"2026-08-17T08:04:24.402148Z","submitted_at":"2026-08-07T19:55:45Z","title":"LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T00:24:53.986049Z"},"links":{"cited_paper":"/paper/2111.00680","citing_paper":"/paper/2608.07733"},"observation_digest":"sha256:b4ed3d119d1796bcfcf524a1d9087d83133feed67d3e5bcd5ccb5eff6d5224c3","observation_id":"d882eab4-e000-483b-be94-26f7a8d69ca1","resolution":{"observed_at":"2026-08-11T00:24:53.986049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2111.00680/citation-record","integrity":"/paper/2111.00680/integrity","json":"/paper/2111.00680/citation-record.json","paper":"/paper/2111.00680"},"outbound":[],"paper":{"arxiv_id":"2111.00680","last_updated":"2022-08-16T07:58:25Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T08:52:24.067348Z","submitted_at":"2021-11-01T03:47:07Z","title":"GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.00680."}