{"as_of":"2026-08-07T07:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:972362ff605f0f672df82c765e6e0909a27ff91b7bed5558411614808a10174e","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":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:39:29.116265Z","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":"2026-06-28T22:32:44.082476Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":"2209.15001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-06-28T22:32:44.082476Z","title":"Dilated neighborhood attention transformer","venue":null,"work_id":"c61473a8-4ca6-4cd4-a948-f1fc0f2a98d2","year":2022},"citing_paper":{"arxiv_id":"2412.05496","last_updated":"2024-12-07T01:46:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-07T01:46:38Z","title":"Flex Attention: A Programming Model for Generating Optimized Attention Kernels","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-17T21:27:16.615071Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2412.05496"},"observation_digest":"sha256:08cd1b47b72c51f41ef004cd11d45b10eb868e88fd9687c6043fed053bff11d8","observation_id":"875d2962-1e49-4f13-8153-6a2a0df60925","resolution":{"observed_at":"2026-05-17T21:27:16.696529Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":"2209.15001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-06-28T22:32:44.082476Z","title":"Dilated neighborhood attention transformer","venue":null,"work_id":"c61473a8-4ca6-4cd4-a948-f1fc0f2a98d2","year":2022},"citing_paper":{"arxiv_id":"2502.13637","last_updated":"2026-04-18T19:45:46Z","snapshot_observed_at":"2026-07-06T20:39:06.470648Z","submitted_at":"2025-02-19T11:24:45Z","title":"Exploring Mutual Cross-Modal Attention for Context-Aware Human Affordance Generation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-23T02:31:10.316351Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2502.13637"},"observation_digest":"sha256:2137533512d26ba284f99360fbe5ad69723031f1945bfc0dca9bf2fc242023a3","observation_id":"948befb2-69a2-4dfd-a200-23b2bd27e59f","resolution":{"observed_at":"2026-05-23T02:32:25.973736Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-07T05:39:29.116265Z","title":"Dilated neighborhood attention trans- former,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07414","last_updated":"2025-06-09T04:26:20Z","snapshot_observed_at":"2026-08-07T05:32:19.918938Z","submitted_at":"2025-06-09T04:26:20Z","title":"DPFormer: Dynamic Prompt Transformer for Continual Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:39:29.116265Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2506.07414"},"observation_digest":"sha256:2e85d7f3b0381e0a74d20fae03cc5f3e594170cc103d827479f388122ea855c4","observation_id":"aabc5b6d-7000-413a-a889-62ab41e277f1","resolution":{"observed_at":"2026-08-07T05:39:29.116265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-06T19:22:28.155764Z","title":"Dilated neighborhood attention transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05849","last_updated":"2025-07-08T10:24:04Z","snapshot_observed_at":"2026-08-06T19:14:31.855255Z","submitted_at":"2025-07-08T10:24:04Z","title":"DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T19:22:28.155764Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2507.05849"},"observation_digest":"sha256:933c80485fd713b52e638fcf3a2be709f201347728783efea3d16c66ce904d1d","observation_id":"32a922b3-16f2-4aa7-882e-e214bfad1830","resolution":{"observed_at":"2026-08-06T19:22:28.155764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-06T16:26:45.369562Z","title":"Hassani and H","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13546","last_updated":"2026-07-03T09:09:34Z","snapshot_observed_at":"2026-08-06T16:19:48.080321Z","submitted_at":"2025-07-17T21:36:36Z","title":"NABLA: Neighborhood Adaptive Block-Level Attention","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:26:45.369562Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2507.13546"},"observation_digest":"sha256:2280c9ae1f7abfb3b2ea6b6c393a8946f0dc712abc24ecfc9300552477ca03ff","observation_id":"d6d69a4c-f0e5-48db-ac22-1661d2188279","resolution":{"observed_at":"2026-08-06T16:26:45.369562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-06T15:09:50.841599Z","title":"Dilated neighborhood attention trans- former,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.16624","last_updated":"2025-07-22T14:17:08Z","snapshot_observed_at":"2026-08-06T20:58:25.697711Z","submitted_at":"2025-07-22T14:17:08Z","title":"A2Mamba: Attention-augmented State Space Models for Visual Recognition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:09:50.841599Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2507.16624"},"observation_digest":"sha256:121f95a04cd263c58f058179a3dafd14502b46a1c7800d7bc4b6013eadd4e6bd","observation_id":"425f7171-96a9-4ff0-bf45-25fcb391a43f","resolution":{"observed_at":"2026-08-06T15:09:50.841599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-03T07:04:35.633327Z","title":"Dilated neighborhood attention transformer.arXiv preprint arXiv:2209.15001,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.21291","last_updated":"2026-06-30T02:06:32Z","snapshot_observed_at":"2026-08-03T07:04:34.339771Z","submitted_at":"2026-01-29T05:44:41Z","title":"Gaussian Belief Propagation Network for Depth Completion","version":2},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-03T07:04:35.633327Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2601.21291"},"observation_digest":"sha256:5c516eaa1eec0828b94986fee3325f2a8eb6eaf978170151317c0158fad9f229","observation_id":"3e16cf6e-1082-4729-8b82-1acf2376e964","resolution":{"observed_at":"2026-08-03T07:04:35.633327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":"2209.15001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-06-28T22:32:44.082476Z","title":"Dilated neighborhood attention transformer","venue":null,"work_id":"c61473a8-4ca6-4cd4-a948-f1fc0f2a98d2","year":2022},"citing_paper":{"arxiv_id":"2602.18196","last_updated":"2026-05-28T10:28:00Z","snapshot_observed_at":"2026-08-02T22:05:41.622495Z","submitted_at":"2026-02-20T13:09:49Z","title":"RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T20:59:33.902420Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2602.18196"},"observation_digest":"sha256:454d2d3d2b5f21e2b799c30172c5af5c5f295f9c458fefb46c2d8411f0a6320e","observation_id":"3270dde8-b250-42c3-8ec3-73a458ee299c","resolution":{"observed_at":"2026-05-15T21:00:17.906483Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":"2209.15001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-06-28T22:32:44.082476Z","title":"Dilated neighborhood attention transformer","venue":null,"work_id":"c61473a8-4ca6-4cd4-a948-f1fc0f2a98d2","year":2022},"citing_paper":{"arxiv_id":"2602.18196","last_updated":"2026-05-28T10:28:00Z","snapshot_observed_at":"2026-08-02T22:05:41.622495Z","submitted_at":"2026-02-20T13:09:49Z","title":"RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-21T12:45:27.150368Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2602.18196"},"observation_digest":"sha256:9c52b0ae4bcc0750032b7201517486c263e997297ea484e4b96efe0fb22ab3ea","observation_id":"8185952f-2f24-45e7-9d8f-cc4fe55a7a97","resolution":{"observed_at":"2026-05-21T12:50:09.435115Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-02T22:05:43.006990Z","title":"and Shi, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.18196","last_updated":"2026-05-28T10:28:00Z","snapshot_observed_at":"2026-08-02T22:05:41.622495Z","submitted_at":"2026-02-20T13:09:49Z","title":"RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference","version":5},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T22:05:43.006990Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2602.18196"},"observation_digest":"sha256:ac09112a3b7ed89a0faeb1c5a82333cbdea350607f4cf71cc2619bd2b4152b56","observation_id":"9964a5fd-1e3d-45aa-b311-416fa487f7f2","resolution":{"observed_at":"2026-08-02T22:05:43.006990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":"2209.15001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-06-28T22:32:44.082476Z","title":"Dilated neighborhood attention transformer","venue":null,"work_id":"c61473a8-4ca6-4cd4-a948-f1fc0f2a98d2","year":2022},"citing_paper":{"arxiv_id":"2605.04830","last_updated":"2026-05-06T12:24:57Z","snapshot_observed_at":"2026-08-02T19:55:29.358871Z","submitted_at":"2026-05-06T12:24:57Z","title":"Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T17:27:16.554144Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2605.04830"},"observation_digest":"sha256:dd2c48aa42802b178a694acd220c3a41f079ba4e00354a14213a417852532ca3","observation_id":"bdaea603-aa95-40ad-9aac-92f7de7f10ac","resolution":{"observed_at":"2026-05-11T17:36:04.513599Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":"2209.15001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-06-28T22:32:44.082476Z","title":"Dilated neighborhood attention transformer","venue":null,"work_id":"c61473a8-4ca6-4cd4-a948-f1fc0f2a98d2","year":2022},"citing_paper":{"arxiv_id":"2605.31577","last_updated":"2026-05-29T17:46:46Z","snapshot_observed_at":"2026-07-06T23:40:42.277618Z","submitted_at":"2026-05-29T17:46:46Z","title":"SurGe: Improved Surface Geometry in Point Maps","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T22:29:47.596119Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2605.31577"},"observation_digest":"sha256:edf86466eb27daed4fa5526454b4fcd9da004fdb30483616772395ae19eb7443","observation_id":"ab963f91-613c-4e58-bd23-03f2d07890a4","resolution":{"observed_at":"2026-06-28T22:32:44.084128Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-07-14T13:27:19.341926Z","title":"Dilated neighborhood attention transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.10214","last_updated":"2026-07-11T08:51:15Z","snapshot_observed_at":"2026-08-05T11:22:57.549927Z","submitted_at":"2026-07-11T08:51:15Z","title":"ScratNet: A Swin-Based Multi-Scale Dilated Network with Precision Refinement for Semiconductor Scratch Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T13:27:19.341926Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2607.10214"},"observation_digest":"sha256:183617fe2a1eb680438b97a227853771adfb9893b90152e3063f8b4e0f2e0723","observation_id":"2b496949-e394-4fcc-a841-cd8c69f69fb4","resolution":{"observed_at":"2026-07-14T13:27:19.341926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15001","snapshot_observed_at":"2026-08-05T23:22:24.868299Z","title":"arXiv preprint arXiv:2209.15001 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03216","last_updated":"2026-08-04T06:51:06Z","snapshot_observed_at":"2026-08-07T06:15:52.214661Z","submitted_at":"2026-08-04T06:51:06Z","title":"iFAN: Inference-Aware Learning for Plain Mask Transformers","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T23:22:24.868299Z"},"links":{"cited_paper":"/paper/2209.15001","citing_paper":"/paper/2608.03216"},"observation_digest":"sha256:4783c73bf8d0e2a9283c575f96aa609d7ca95c7276c4ca1745d0a55c75a92ab1","observation_id":"f8034ce7-09e4-4010-806a-d8da86086760","resolution":{"observed_at":"2026-08-05T23:22:24.868299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2209.15001/citation-record","integrity":"/paper/2209.15001/integrity","json":"/paper/2209.15001/citation-record.json","paper":"/paper/2209.15001"},"outbound":[],"paper":{"arxiv_id":"2209.15001","last_updated":"2023-01-16T18:58:58Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T04:56:00.219481Z","submitted_at":"2022-09-29T17:57:08Z","title":"Dilated Neighborhood Attention Transformer"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2209.15001."}