{"as_of":"2026-08-08T11:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4b9f47d67cc6ab18c9c372ca2b1230408339434638951ec8c7a41a07c29674f1","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:36:59.449667Z","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":"2026-08-05T10:37:00.904876Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.05852","last_updated":"2023-05-05T21:58:41Z","snapshot_observed_at":"2026-08-08T09:59:11.838678Z","submitted_at":"2022-06-12T22:39:41Z","title":"ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths","version":2},"cited_work":{"arxiv_id":"2206.05852","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.05852","snapshot_observed_at":"2026-08-05T10:37:00.904876Z","title":"ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths","venue":"cs.LG","work_id":"4e4b524e-62b0-437f-a2e6-8c8565689bf9","year":2022},"citing_paper":{"arxiv_id":"2509.03973","last_updated":"2025-09-04T07:58:52Z","snapshot_observed_at":"2026-08-05T10:36:57.332431Z","submitted_at":"2025-09-04T07:58:52Z","title":"SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:36:59.449667Z"},"links":{"cited_paper":"/paper/2206.05852","citing_paper":"/paper/2509.03973"},"observation_digest":"sha256:09402d2c44dd5064bae3e8460abcc15177d32db5bd2bb9915f470a84c03fb7d6","observation_id":"ce528d0e-4e49-4946-a949-c5790b2f88e1","resolution":{"observed_at":"2026-08-05T10:37:00.974362Z","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/2206.05852/citation-record","integrity":"/paper/2206.05852/integrity","json":"/paper/2206.05852/citation-record.json","paper":"/paper/2206.05852"},"outbound":[],"paper":{"arxiv_id":"2206.05852","last_updated":"2023-05-05T21:58:41Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T09:59:11.838678Z","submitted_at":"2022-06-12T22:39:41Z","title":"ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths"},"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 1 inbound Pith citation observation for arXiv:2206.05852."}