{"as_of":"2026-08-19T18:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fb7a36320e17cc647bc4e0258b17d5c9b1554fb164ad8b9a67d7d52908ac0389","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":"2026-08-19T06:32:44.657259+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":"2026-08-11T16:56:01.653783Z","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":"2026-08-11T16:56:02.287972Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.04967","last_updated":"2024-11-07T18:43:17Z","snapshot_observed_at":"2026-08-16T13:02:00.466221Z","submitted_at":"2024-11-07T18:43:17Z","title":"AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation","version":1},"cited_work":{"arxiv_id":"2411.04967","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.04967","snapshot_observed_at":"2026-08-11T16:56:02.287972Z","title":"AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation","venue":"cs.CV","work_id":"0d868acf-ebab-4336-a281-ef617bd471a8","year":2024},"citing_paper":{"arxiv_id":"2412.09619","last_updated":"2024-12-12T18:59:53Z","snapshot_observed_at":"2026-08-18T17:16:22.737486Z","submitted_at":"2024-12-12T18:59:53Z","title":"SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T16:56:01.653783Z"},"links":{"cited_paper":"/paper/2411.04967","citing_paper":"/paper/2412.09619"},"observation_digest":"sha256:ec86adac9782a084f99f23f65bcad75337e2f55fa601f351dc25a7983b77d9a1","observation_id":"30105d95-d86e-43fb-91bd-b9afaa3c23d4","resolution":{"observed_at":"2026-08-11T16:56:02.294501Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04967","last_updated":"2024-11-07T18:43:17Z","snapshot_observed_at":"2026-08-16T13:02:00.466221Z","submitted_at":"2024-11-07T18:43:17Z","title":"AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04967","snapshot_observed_at":"2026-08-03T10:56:12.920321Z","title":"Ascan: Asymmetric convolution-attention networks for efficient recognition and generation.arXiv preprint arXiv:2411.04967, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.08303","last_updated":"2026-07-06T07:20:34Z","snapshot_observed_at":"2026-08-07T13:59:11.336528Z","submitted_at":"2026-01-13T07:46:46Z","title":"SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T10:56:12.920321Z"},"links":{"cited_paper":"/paper/2411.04967","citing_paper":"/paper/2601.08303"},"observation_digest":"sha256:f02ac930227c05e80c182487eeb64ee78dd3c88c1f478078aaae2788d74315ae","observation_id":"c0c87f4c-f57c-47c4-9a49-eecbdfd6e5f9","resolution":{"observed_at":"2026-08-03T10:56:12.920321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2411.04967/citation-record","integrity":"/paper/2411.04967/integrity","json":"/paper/2411.04967/citation-record.json","paper":"/paper/2411.04967"},"outbound":[],"paper":{"arxiv_id":"2411.04967","last_updated":"2024-11-07T18:43:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T13:02:00.466221Z","submitted_at":"2024-11-07T18:43:17Z","title":"AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.04967."}