{"as_of":"2026-08-18T22:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd5f1cc0b166c2809bfd61dabbb1397a2652fd842325236b774f0282e207921e","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T13:40:39.718350Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.12902/citation-record","integrity":"/paper/2412.12902/integrity","json":"/paper/2412.12902/citation-record.json","paper":"/paper/2412.12902"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.484541Z","title":"Docformer: End-to-end transformer for document understanding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.484541Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:0d7809b70d04adf5b4cd7412072daec93c146d829d33b4ef7af04e87cb30e857","observation_id":"66ec3495-180b-4d7b-a51c-83e6a5ce15bc","resolution":{"observed_at":"2026-08-11T13:40:39.484541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.429539Z","title":"Visual and textual deep feature fusion for document image classification","venue":null,"work_id":"b9a981e0-a8b5-4cb7-8773-af0cde01c8e9","year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.490094Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:6737e2ad9b800c379b24d0b27dc31262ffca1e6debf66264fbddea0ca578d38b","observation_id":"8fd7490b-cab5-4011-a31a-31d79b7f9cbf","resolution":{"observed_at":"2026-08-11T13:40:40.434122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.416579Z","title":"Eaml: Ensemble self-attention-based mu- tual learning network for document image classification,","venue":null,"work_id":"4433d104-68bc-44d0-ad7d-fae2ef4e186b","year":null},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.494541Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:89ab0f31ef58c2f9280260f65a147ff107d70ba94b2d944dcff37b2d84bde1e5","observation_id":"9bea97bc-1f37-4130-a7d9-2aaf2f2a2339","resolution":{"observed_at":"2026-08-11T13:40:40.420940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08254","last_updated":"2022-09-03T14:11:33Z","snapshot_observed_at":"2026-08-17T05:59:48.347864Z","submitted_at":"2021-06-15T16:02:37Z","title":"BEiT: BERT Pre-Training of Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08254","snapshot_observed_at":"2026-08-11T13:40:39.499670Z","title":"Beit: Bert pre-training of image transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.499670Z"},"links":{"cited_paper":"/paper/2106.08254","citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:cb3024b69548c60548949d34f2242bb8e1e7a3a194dd2bb0f04132bf521cdb9d","observation_id":"0333c493-8e97-4c62-9425-584319afcd0e","resolution":{"observed_at":"2026-08-11T13:40:39.499670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.402674Z","title":"Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, and Jovana Mitrovi´c","venue":null,"work_id":"793d5fff-5eff-4bc8-ad85-1f1ef2f59d94","year":2024},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.505431Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:7ac695553d69a009394625e3d794a8595ce87cf0e9762f677090b3980aee2ab8","observation_id":"e9c3414a-0dbb-4003-9c61-65d09ea10892","resolution":{"observed_at":"2026-08-11T13:40:40.407110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.383316Z","title":"Cascade r-cnn: High quality object detection and instance segmentation","venue":null,"work_id":"bbb97472-beac-4f8c-8365-af3f31affc08","year":2019},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.510451Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:a4fa0c3408c55907465e6214d5e0bdd826d0d716df91f94a2475e46a8bc57415","observation_id":"b65dd56a-4296-4227-854b-d3697cd3fa76","resolution":{"observed_at":"2026-08-11T13:40:40.391229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.515084Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.515084Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:5dd42215f8200e944cf32c5351bc7fc8ad4c70feebd8e4151ebc0016701f1049","observation_id":"682cc618-ae98-41f9-bca2-cc528aa83daf","resolution":{"observed_at":"2026-08-11T13:40:39.515084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.354146Z","title":"Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts","venue":null,"work_id":"6be9e140-4c1d-40d3-b405-f52a302ef68a","year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.520754Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:2e3b671d516df8a8ee117a6ba673a2421974c8afacc77ca68d49b2ae04a6bed3","observation_id":"8400b773-7954-4b58-8269-14422081365a","resolution":{"observed_at":"2026-08-11T13:40:40.361518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.525119Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.525119Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:76e23423d655278d61a02854a85708b79b4a842f111e4ea431953c9e18aaeb54","observation_id":"49f50b91-07a4-4063-9c8f-30f4706b3949","resolution":{"observed_at":"2026-08-11T13:40:39.525119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.322226Z","title":"Big self-supervised mod- els are strong semi-supervised learners","venue":null,"work_id":"fe672644-32bc-4b81-a3f4-84f6c58c7eaa","year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.529747Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:177e418badea7492a8aa5e7289dcead73a77f8ec296d434f39320ee7fee812a9","observation_id":"89747ae7-cac3-433a-8592-12cfd1b1d7e9","resolution":{"observed_at":"2026-08-11T13:40:40.327008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.534111Z","title":"Uniter: Universal image-text representation learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.534111Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:065208c7a4e8baa7bbde02776fa2b5f5639134e4f9bca413c63ff8ae8147c68a","observation_id":"f32e35b1-f074-45e7-a5b6-6eb998f3ce3a","resolution":{"observed_at":"2026-08-11T13:40:39.534111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.300367Z","title":"Vision grid transformer for document layout analysis","venue":null,"work_id":"dfac02c0-087c-4125-bd0d-ec35227493b6","year":2023},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.538610Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:94a89adbf212ae46dd21dc78b303a486d2efe5be4a10fbaa845f1efb6fc1dbe4","observation_id":"67578e09-632f-4fae-a273-f183d55d4f1f","resolution":{"observed_at":"2026-08-11T13:40:40.304685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.287080Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"c82bc8a2-8c2e-4634-9794-25e5678ab4f8","year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.542811Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:8cd8f59107aba36fe57add1d72496cb8d55c7b1721d50e15bd14ef9c86c07eb6","observation_id":"70680e1c-a978-45ea-84b2-2887f04cb98f","resolution":{"observed_at":"2026-08-11T13:40:40.291726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.546905Z","title":"Bootstrap your own latent-a new approach to self-supervised learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.546905Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:d5c4ec351b8e9ae29353c1d6f9e981c9b438631fb945bb743d21aec7d0e9fdb4","observation_id":"b16bcab3-423f-4d11-bfad-252ed5f3bbe1","resolution":{"observed_at":"2026-08-11T13:40:39.546905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.257012Z","title":"Evaluation of deep convolutional nets for document image classification and retrieval","venue":null,"work_id":"e36436a5-d50b-4d32-ac44-ae1806291c70","year":2015},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.551384Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:c7ce9b1208d29b4d8eb2d35bddca278c295afd630e8529bdd041d81142bd7e28","observation_id":"e633ae88-9220-4280-8de8-f86f6947bfe1","resolution":{"observed_at":"2026-08-11T13:40:40.263374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.556434Z","title":"Momentum contrast for unsupervised visual rep- resentation learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.556434Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:6fe0d45742cf1f0c90a6887bcbb3bd3efbe5af7ec3e3f9fdf6aeba5555c32d6f","observation_id":"8d25c0fe-45d6-41a7-afdd-f20c1c32357d","resolution":{"observed_at":"2026-08-11T13:40:39.556434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.231724Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":"dbe5f3c8-5e26-4e2b-a7f8-c9bd8483bea0","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.560702Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:50ad1aeaf3fb90c3aed0ad81f86b90b699d8e9ecd540f056b869eaad85491d18","observation_id":"a02407b9-2880-48ab-b9de-61c0d382e819","resolution":{"observed_at":"2026-08-11T13:40:40.236336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.218003Z","title":"Bros: A pre-trained lan- guage model focusing on text and layout for better key infor- mation extraction from documents","venue":null,"work_id":"b38c4940-9f11-41fc-b134-cd1daaef44f8","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.564700Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:34c6a996d50d19177fb262a1266a966930dae3d4cee899abfc2ce41748f79f4e","observation_id":"3383098b-8d62-40c2-869e-743b5fe97593","resolution":{"observed_at":"2026-08-11T13:40:40.222480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.569129Z","title":"Layoutlmv3: Pre-training for document ai with unified text and image masking","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.569129Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:5577271d6967210df20f78760fb7d8ffb7ce256e7bce5121ea4cd947bc152f10","observation_id":"60f325e4-3b2f-490f-a045-690fcb2b1e8b","resolution":{"observed_at":"2026-08-11T13:40:39.569129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.194016Z","title":"Icdar2019 compe- tition on scanned receipt ocr and information extraction","venue":null,"work_id":"1de0c20f-ccd3-4e24-baed-0db477cd6650","year":2019},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.573650Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:bce93fa1fbc4740f89f4143e9058d84240a6bc805c9c407dc42317c34d221205","observation_id":"323b5738-e0c2-494a-bf73-211acd795769","resolution":{"observed_at":"2026-08-11T13:40:40.200010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.180058Z","title":null,"venue":null,"work_id":"8fc9ae98-0959-41de-886a-56db11058db1","year":2023},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.579360Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:048c5d3ccce693a83b3b3f18b67b2800f129c988237ac1d6c6a8062e83203f2b","observation_id":"4b133606-b0da-42ab-a007-0f1a1399d918","resolution":{"observed_at":"2026-08-11T13:40:40.184396Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.165090Z","title":"Funsd: A dataset for form understanding in noisy scanned documents, 2019","venue":null,"work_id":"ab05d2b8-0dd0-47ec-9750-220533110dde","year":2019},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.584087Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:44b2bd0ff144a2031a462ec5c5812d50442f75ed1bb17f41f1d2b6e38238d4f4","observation_id":"444b683c-04af-42ed-a478-d1265eb77b62","resolution":{"observed_at":"2026-08-11T13:40:40.170443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.588594Z","title":"Scaling up visual and vision-language representa- tion learning with noisy text supervision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.588594Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:5ed2572ee8a79ed9ac6c725dd1af9b6aa99448b195826cdb0b008ea6e2f21f22","observation_id":"4d4173ea-df47-4d37-9d8f-d67018a45527","resolution":{"observed_at":"2026-08-11T13:40:39.588594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.140422Z","title":"Ocr-free document understanding transformer","venue":null,"work_id":"274b3db6-671b-48cc-9e66-c89e17abd321","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.592918Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:ac4ccecf34222f802c95a450e00ac8241a03ebb280e65b75e802abcae8c327d8","observation_id":"ea22a01c-c224-4ff8-8aa3-aaa0034c5661","resolution":{"observed_at":"2026-08-11T13:40:40.145285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.126654Z","title":"Dit: Self-supervised pre-training for docu- ment image transformer","venue":null,"work_id":"c732486f-792f-4921-8f07-c371afb66f1a","year":null},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.597159Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:6b31e0f260ab092e6d7154b09fa69cd3ded94397eba3ed52d89fc3058eae1d18","observation_id":"ea3e7d8c-81de-4470-aa51-c34996a3bc19","resolution":{"observed_at":"2026-08-11T13:40:40.130892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.601576Z","title":"Grounded language-image pre-training, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.601576Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:dc1d4a8e49cee2874a9f32b8aa90f60f1b9f01b0510455313cb24581b50b7dd8","observation_id":"c8d01da3-b4e8-4be9-ab56-c66f1ed1f1f8","resolution":{"observed_at":"2026-08-11T13:40:39.601576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.103800Z","title":"Grounded language-image pre-training","venue":null,"work_id":"b638fc70-2c26-4926-bd38-875f9f10385f","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.605247Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:ad666b6d3d57c4fcdd040d22022fda69a7549597981ada2923345013e65ff4f7","observation_id":"bfc282d1-3098-4457-bef1-d54bcbc8b982","resolution":{"observed_at":"2026-08-11T13:40:40.108412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.01038","last_updated":"2020-11-11T05:08:05Z","snapshot_observed_at":"2026-08-16T00:59:41.835882Z","submitted_at":"2020-06-01T16:04:30Z","title":"DocBank: A Benchmark Dataset for Document Layout Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.01038","snapshot_observed_at":"2026-08-11T13:40:39.609154Z","title":"Docbank: A bench- mark dataset for document layout analysis","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.609154Z"},"links":{"cited_paper":"/paper/2006.01038","citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:ea4fd407ae98e43bb3ae41f2fbb16813b476d7010cf6e8707fd2a1280bb1572c","observation_id":"c4734ac9-db7e-4647-8924-73316f72819b","resolution":{"observed_at":"2026-08-11T13:40:39.609154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.13155","last_updated":"2025-06-18T03:26:43Z","snapshot_observed_at":"2026-08-16T16:50:11.626092Z","submitted_at":"2022-06-27T09:58:34Z","title":"Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding","version":2},"cited_work":{"arxiv_id":"2206.13155","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.13155","snapshot_observed_at":"2026-08-11T13:40:39.799434Z","title":"Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding","venue":"cs.CV","work_id":"8e5d8a8c-3aa0-436c-9ba2-0d58a0742e83","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.613456Z"},"links":{"cited_paper":"/paper/2206.13155","citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:4288c4c39f164af5eb401daf305833d80d5a65a0f1ee7184932ac5af438f15c1","observation_id":"43b1ff01-eb7e-4580-8647-fe82aa0ad910","resolution":{"observed_at":"2026-08-11T13:40:39.806354Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.088773Z","title":"Docvqa: A dataset for vqa on document images","venue":null,"work_id":"42f93628-4fd6-4da7-ba9b-85d5e77a877e","year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.617708Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:d1037dcfe1e1f0a8c6cbad664d6c58e7da3336728cb68b850ca127817a4df8a4","observation_id":"74d9812c-b55e-4de7-ab11-fc769ecdbf2e","resolution":{"observed_at":"2026-08-11T13:40:40.092914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.075935Z","title":"Infographicvqa","venue":null,"work_id":"ce08b7e3-3c9a-4fec-bcc4-491ea89cab5c","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.621716Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:e609692165573fd542a1ac6a64e0436a3a5db76235379180e964adc8fa46905d","observation_id":"560bf376-4f24-4416-a1b2-1b777202dd5b","resolution":{"observed_at":"2026-08-11T13:40:40.080406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-11T13:40:39.625439Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.625439Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:00061c45df38d58f25cc427f4e01902ce707e7ad479e98de7f3b80ef047f0583","observation_id":"953a4027-a9fa-4bcc-8324-f6f675b6ec19","resolution":{"observed_at":"2026-08-11T13:40:39.625439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.062049Z","title":"{CORD}: A consolidated receipt dataset for post-{ocr} parsing","venue":null,"work_id":"aaacb482-1bc9-4a90-9bb7-de64d373b717","year":2019},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.630141Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:98d38da2188a0a0d892ab348dd78337a70873408bee098d21205a24a20ede967","observation_id":"d869e613-2f33-43ad-9ea8-26a9094cacf4","resolution":{"observed_at":"2026-08-11T13:40:40.066575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.049419Z","title":"Doclaynet: A large human- annotated dataset for document-layout segmentation","venue":null,"work_id":"d8faa270-55ba-44fe-bb6a-ef5436a978d0","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.633975Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:765f26d50145d37775685101a6c5e8aaea2e619918b1826ad90e3b21f456846c","observation_id":"93011028-67ea-4c9f-a3cc-353573530b1f","resolution":{"observed_at":"2026-08-11T13:40:40.053568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.036433Z","title":"Going full-tilt boogie on document understanding with text-image-layout transformer","venue":null,"work_id":"4cf08823-f64e-4c0b-bff1-a1e2b93ee44c","year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.637850Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:9e004adae6487a7e4af8db2858a063d72dd95cec8f05c1e139b52693a302122f","observation_id":"47f192b0-31d2-4a0b-9bac-37cddd362576","resolution":{"observed_at":"2026-08-11T13:40:40.040932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.641900Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.641900Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:d8202ee787fdd79fc45b15849455b3317bc8f90f0105a9e3d3d95328a8e894ed","observation_id":"fa9145ef-c848-49f8-9306-68dbc95b562f","resolution":{"observed_at":"2026-08-11T13:40:39.641900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.646043Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.646043Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:0bbdc2bb04d3cb5ee66644eac8834591d56f9ce74acf0b86c3c9c4a40a4b84f8","observation_id":"62979f90-35aa-4ffc-9a99-a2bb6acb48b9","resolution":{"observed_at":"2026-08-11T13:40:39.646043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.650138Z","title":"Laion-5b: An open large-scale dataset for training next generation image-text models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.650138Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:7c5221c61dc5247882c12e2a5d4641b223fc35abb89be30c479278c187c4cfa1","observation_id":"1e2f8194-9003-49d7-8273-f33e93b20574","resolution":{"observed_at":"2026-08-11T13:40:39.650138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:40.000001Z","title":"Complex document information processing (cdip) dataset, 2022","venue":null,"work_id":"35480a2f-afb7-4561-96be-ee357be72bdc","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.654274Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:22e5e11fe1afc77c5111c77e7ad1f3c5a6b24405f94f527c6762a254563035ca","observation_id":"65695760-db70-443f-aee2-426a6b7ce92f","resolution":{"observed_at":"2026-08-11T13:40:40.004602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.986013Z","title":"Kleister: key in- formation extraction datasets involving long documents with complex layouts","venue":null,"work_id":"7461d25e-b26d-4792-b06d-4d6278d77994","year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.658478Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:836976a3e633bf87f99f45c199efe4137f9fe7af878e91bc907264d136d7d943","observation_id":"345b4aa3-8ecc-4782-8dd1-45a40c9bd442","resolution":{"observed_at":"2026-08-11T13:40:39.990257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.972817Z","title":"Vl-bert: Pre-training of generic visual- linguistic representations","venue":null,"work_id":"f28507a1-02d3-4c7d-8364-afff16430348","year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.662701Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:2484881fdd587aa32ddbe584a57aa4b08d5194781ba774e960e8606ec9a0ce2c","observation_id":"5cabec59-b2ba-4b11-8a65-3bbbc638f022","resolution":{"observed_at":"2026-08-11T13:40:39.977499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.960935Z","title":"Revisiting unreasonable effectiveness of data in deep learning era","venue":null,"work_id":"e682d10b-203b-460c-b7dc-782a41c5d972","year":2017},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.667525Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:11ac8df1c4eca4b320a74a958a710e405713de26cbfa0d9658f63cd8fbfb9c59","observation_id":"2028df8f-6ae4-4a69-aa05-8d38aa65529e","resolution":{"observed_at":"2026-08-11T13:40:39.964964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.948424Z","title":"Unifying vision, text, and layout for universal document processing","venue":null,"work_id":"18ace7fd-695e-4fc8-a5ca-b9a7e0b8d0df","year":2023},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.671585Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:8a603da024d128412fec5b47378e959b372b588f9170e94bb6dee59034f277eb","observation_id":"8e70fc14-c584-4731-89ec-6bc277c3b6d3","resolution":{"observed_at":"2026-08-11T13:40:39.952752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.675226Z","title":"Yfcc100m: The new data in multimedia research","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.675226Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:78da255ff0f5db3c29f5f29a927dd6fc7ef7a59afe358c584a51cc75c5a49543","observation_id":"7bb084c8-b4ae-4cd3-9a6d-86c1bb614771","resolution":{"observed_at":"2026-08-11T13:40:39.675226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.679383Z","title":"Training data-efficient image transformers & distillation through at- tention, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.679383Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:884f4a1dc6d6196774188f4bd0d81852d2f56e8a777ff0dddbe9695bec70943c","observation_id":"c4e96163-fed6-436d-98d9-d8d38625d646","resolution":{"observed_at":"2026-08-11T13:40:39.679383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.683469Z","title":"Detectron2","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.683469Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:98826f1ca7e50ec6cb08619e73e251cebf0da7b415e1b0f924463a17d2b42810","observation_id":"91bc9e1f-cdd3-455c-81da-290aef275597","resolution":{"observed_at":"2026-08-11T13:40:39.683469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.913106Z","title":"Aggregated residual transformations for deep neural networks, 2017","venue":null,"work_id":"328e8589-5b94-4294-8d50-a05101807bbd","year":2017},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.687287Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:41b33a39b44448bd613f7da5609735a8b7cae4a26a36c6ff54c84d74d7ea2659","observation_id":"b5a48ba7-8102-4142-8dab-dd75a01539d3","resolution":{"observed_at":"2026-08-11T13:40:39.917601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.691284Z","title":"Layoutlm: Pre-training of text and layout for document image understanding","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.691284Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:fbef95cb5f667567954c29ba3289af6588e454767a8863989d61b8e4be8a41d9","observation_id":"90ce9152-23a7-469c-bfe6-609d473f6bb1","resolution":{"observed_at":"2026-08-11T13:40:39.691284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.14740","last_updated":"2022-01-10T04:08:10Z","snapshot_observed_at":"2026-08-16T18:55:28.472138Z","submitted_at":"2020-12-29T13:01:52Z","title":"LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.14740","snapshot_observed_at":"2026-08-11T13:40:39.695400Z","title":"Layoutlmv2: Multi-modal pre-training for visually-rich document understanding","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.695400Z"},"links":{"cited_paper":"/paper/2012.14740","citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:5350c3c4b9011933fc90b8ec2fa5f3005082eaf423cd16e5dba016854c037058","observation_id":"d8d7e7f7-c9b3-4c1b-a072-821c6f2242fb","resolution":{"observed_at":"2026-08-11T13:40:39.695400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.890254Z","title":"FILIP: Fine-grained interactive language- image pre-training","venue":null,"work_id":"bc394b9f-dd5a-4788-b0d4-0ff2aa12613a","year":2022},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.700181Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:6a58c412dcc4d952036b92a517ef2c9b58da0b73254a626c5325d5f48beb0b90","observation_id":"b6ede840-0a43-439c-b526-01ee219b47c5","resolution":{"observed_at":"2026-08-11T13:40:39.895575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00289","last_updated":"2023-03-01T07:32:51Z","snapshot_observed_at":"2026-08-16T17:05:51.890495Z","submitted_at":"2023-03-01T07:32:51Z","title":"StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.00289","snapshot_observed_at":"2026-08-11T13:40:39.704209Z","title":"Structextv2: Masked visual- textual prediction for document image pre-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.704209Z"},"links":{"cited_paper":"/paper/2303.00289","citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:82979e0e551c3a1131ce6c70ba3b4f5b9f54474c69e632ad7b9a31d07685ba26","observation_id":"3c7e747a-4390-4ab8-b9ed-b9d9b70a1ea0","resolution":{"observed_at":"2026-08-11T13:40:39.704209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.709000Z","title":"Sigmoid loss for language image pre-training,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.709000Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:77606c83d5c514663050621aae7e5b2e30126cd2d18b10461a9f1a37e4353490","observation_id":"97cf0576-2402-4cb0-9cd7-0e7c19cab44c","resolution":{"observed_at":"2026-08-11T13:40:39.709000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.863987Z","title":"Zhang, H","venue":null,"work_id":"6656e856-b541-4623-b858-004017e1a626","year":2024},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.713836Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:b81fd4d447215e0e7eefa3157aff32c586a5ff30f69f5528e9fcf0ff52de84e9","observation_id":"2c154ed6-f2a5-4984-8f95-5f5575dfe960","resolution":{"observed_at":"2026-08-11T13:40:39.868641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:40:39.844981Z","title":"Pub- laynet: largest dataset ever for document layout analysis","venue":null,"work_id":"bf56fd97-5e08-4291-8d6f-9eeaf836504c","year":2019},"citing_paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T13:40:39.718350Z"},"links":{"citing_paper":"/paper/2412.12902"},"observation_digest":"sha256:c9bd1d4502029ea6948ac2e6f7daca22297f9cc7f30d6a1b89082bb32f0b9bc1","observation_id":"58d87be3-8112-40c9-9d50-d2d0c2364bd7","resolution":{"observed_at":"2026-08-11T13:40:39.850776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.12902","last_updated":"2025-03-09T14:17:02Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T06:03:44.322043Z","submitted_at":"2024-12-17T13:26:31Z","title":"DoPTA: Improving Document Layout Analysis using Patch-Text Alignment"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":1,"verified_fuzzy":30},"total_outbound_references":54},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2412.12902."}