{"as_of":"2026-08-17T02:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c84d1f2b49161f28ecded68e1014bcda846482ec39f60065b460297dab57b877","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:10:33.746821Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2504.13524/citation-record","integrity":"/paper/2504.13524/integrity","json":"/paper/2504.13524/citation-record.json","paper":"/paper/2504.13524"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:10:34.476471Z","title":"Obc306:Alarge-scaleoraclebonecharacterrecognition dataset","venue":null,"work_id":"f36d5e47-53f3-4620-b3a0-47919d66f9d6","year":2019},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.496309Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:0e73ee93537c197ff648745999a71d648aff9934b629ca2611c3d2f68b683fde","observation_id":"e0e59206-af19-4b4a-ad9b-0561d51d0fa0","resolution":{"observed_at":"2026-08-16T12:10:34.480507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.464256Z","title":"Comparison of different image denoising algorithms for chinese calligraphy images.Neurocomputing, 188:102–112, 2016","venue":null,"work_id":"398b2b0e-cd2e-4a5b-9edf-90fd04862af5","year":2016},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.500916Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:360a3d9803789b0f5f156bfcdc3ae8c4e7c4ae42eace67baf6fc0c55bb203ddb","observation_id":"2751afaa-bf50-46df-9905-b4ad6aac01e6","resolution":{"observed_at":"2026-08-16T12:10:34.468861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.449683Z","title":"Restora- tion method of characters on jiagu rubbings based on poisson distri- bution and fractal geometry","venue":null,"work_id":"044da2c4-49f0-4a47-bdb9-ca1a2d4fde8c","year":2010},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.505190Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:2108b6995a122d3b9a87033c3db81e317552574a74dbf9f89ada191e14d886ad","observation_id":"c0ace882-31c9-4d66-a4e5-36077d9d725c","resolution":{"observed_at":"2026-08-16T12:10:34.456147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.437218Z","title":"Restoration of degraded historical document image: Anadaptive multilayer-informationbinarization technique.J","venue":null,"work_id":"0eb69a1c-9567-4920-a18d-3f2fe8f34fd3","year":2014},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.509899Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:05efe76a0c3989afd58c916c5758fd7bc7ea82c4e529b29f40814c790e6c57d5","observation_id":"62273c0e-49d1-4c78-a36f-b54e001dfaf1","resolution":{"observed_at":"2026-08-16T12:10:34.441330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.424785Z","title":"Robust kronecker-decomposablecomponentanalysisforlow-rankmodeling","venue":null,"work_id":"977ad9dc-b814-422e-86a2-570d551866e0","year":2017},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.514945Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:d623b223dc4f1913e4da785e8f0d7650eec931f1056ea01f3b60976dae9b13ac","observation_id":"168663c9-7b97-48fe-8d51-e6299c5ddf66","resolution":{"observed_at":"2026-08-16T12:10:34.429525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.412523Z","title":"Robust kroneckercomponentanalysis","venue":null,"work_id":"b2fcc6bd-610d-4379-9ea3-a27d2fba6567","year":2018},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.519369Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:38b95a5537323845b2343cc2ef20aac16b133907dceedb50019df78416836c6d","observation_id":"4cef4f56-5cca-43f3-9c42-94b31cabcbca","resolution":{"observed_at":"2026-08-16T12:10:34.416730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.399787Z","title":"Kroneckercomponentwithrobustlow-rank dictionary for image denoising.Displays, 74:102194, 2022","venue":null,"work_id":"21b3ed91-55be-4a01-a78a-1992097eafc2","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.524117Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:22d806be6fd610f8379098bf4d3c86235e09310b7512d901e76022b2da8fa282","observation_id":"864964d7-aa12-4a7d-bfc2-ed8f5d8c82ef","resolution":{"observed_at":"2026-08-16T12:10:34.403716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.386147Z","title":"Robust low-rank analysis with adaptive weighted tensor for image denoising.Displays, 73:102200, 2022","venue":null,"work_id":"3c2c377c-9183-4baf-8ef9-7ed7d0aade22","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.528102Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:603b4ca38f717fe8813b0c5c5798024cc8a2d1b3361a012d22e950fdf5aa0857","observation_id":"2ce92237-8edd-440f-8e5e-5bfd70fc0f7b","resolution":{"observed_at":"2026-08-16T12:10:34.390546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.532622Z","title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.532622Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:28d036c7d27b05d9941a8e75c3da34964b6594551513d25c2e606551c9af7ce4","observation_id":"e7d99f1f-1ad9-4e21-9b1d-0311c855150a","resolution":{"observed_at":"2026-08-16T12:10:33.532622Z","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":"2026-08-16T12:10:34.366768Z","title":"Restormer: Efficient transformer for high-resolution image restoration","venue":null,"work_id":"d7267039-2139-44f1-aa5a-0e4a8324fa7e","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.536575Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:218e411a56090657a3c25bd528e3fa6e01ba9d2bc2a7ad69a8e1b6c0b72be919","observation_id":"57e7945a-8589-48b7-9766-74c65403364f","resolution":{"observed_at":"2026-08-16T12:10:34.370679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.356001Z","title":"Rcrn: Real-world character image restoration network via skeleton extraction","venue":null,"work_id":"f2988d80-ab90-4523-a472-17e2d778e7c4","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.540540Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:cd12d894e9e5522fc56b9960a9ddec84db5c99d5a681a467a3d25de0947179c8","observation_id":"e7de51be-b656-4c03-a12d-e3e92a1d241d","resolution":{"observed_at":"2026-08-16T12:10:34.359616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.344322Z","title":"Charformer: A glyph fusion based attentive framework for high-precision character image denoising","venue":null,"work_id":"d230f496-ecd1-47ad-a9bc-8f8752fadad1","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.544493Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:6cabc80839fdaf842f2a59b758596f1fdb0121ff58d9ec9b8679c72ad0a9d7d6","observation_id":"653ad3c1-e91a-48b5-bbc7-777b69f78f9b","resolution":{"observed_at":"2026-08-16T12:10:34.348571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.331967Z","title":"Self-supervised learning of orc-bert augmentator for recog- nizing few-shot oracle characters","venue":null,"work_id":"05394686-0e4e-4a0a-bccb-66115f0a719e","year":2020},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.548925Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:d3ae80486f3117bf7adccc3916a999b024fe3aa1650d4397bb039c61af304849","observation_id":"f0ce02fa-18a3-47d7-968b-7a22d741da84","resolution":{"observed_at":"2026-08-16T12:10:34.336523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.320032Z","title":"Unsupervised structure-texture separation network for oracle character recognition","venue":null,"work_id":"3fd48137-7334-4bdc-8014-6e38d73c98d8","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.553622Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:fb96d4e2c0f4ebf8e683066d038c747ddcfcf260fa928f76c85e13b46510c94d","observation_id":"02a89305-8098-4f96-a191-2f9c351764f7","resolution":{"observed_at":"2026-08-16T12:10:34.324178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.307431Z","title":"Obi- bench:Canlmmsaidinstudyofancientscriptonoraclebones?,2025","venue":null,"work_id":"52ffa675-7483-44aa-bfb7-512e496d38cb","year":2025},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.557776Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:0cc8076501fac8a8d00fe7a3d105fb87ab0c2a88aa7bd5cbefce570c8559e045","observation_id":"42fca2ab-b1e7-4fea-93a8-3f43243205ff","resolution":{"observed_at":"2026-08-16T12:10:34.311258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.294848Z","title":"Hwobc-ahandwritingoraclebonecharacterrecognition database","venue":null,"work_id":"cb6c247c-ce24-4c7f-9c05-8f5d86b019bd","year":2020},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.565298Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:02a724894ba672fa8240d9a8e8d643bbce3c7087d972b2f677c7fc9fa7d09e96","observation_id":"f0a5df26-8147-464d-a67d-4caeb3a1b133","resolution":{"observed_at":"2026-08-16T12:10:34.299394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.283335Z","title":"Study on the evolution of chinese characters based on few-shot learning: From oracle bone inscriptions to regular script","venue":null,"work_id":"18bdc44c-b09f-4de4-a537-b719d66bcc0c","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.569161Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:8abe3bb82b7202d6125233b5e487ccbfd0133dbc689862eb6625b90a722e5594","observation_id":"1c13ca44-08c1-47da-b750-4a8e5c3d9776","resolution":{"observed_at":"2026-08-16T12:10:34.287240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.270919Z","title":"Dy- namic dataset augmentation for deep learning-based oracle bone inscriptions recognition","venue":null,"work_id":"89b650a8-dfde-430f-8c97-6a573b579082","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.573383Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:18215426a445d174edb1fac3feff1bacc17caabde15a008b4a4457182dcd59ea","observation_id":"0c8038ef-40d9-4471-83db-14d1a0c4c0d9","resolution":{"observed_at":"2026-08-16T12:10:34.275126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12467","last_updated":"2024-02-13T08:21:50Z","snapshot_observed_at":"2026-08-16T14:25:21.268284Z","submitted_at":"2024-01-23T03:30:47Z","title":"An open dataset for the evolution of oracle bone characters: EVOBC","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12467","snapshot_observed_at":"2026-08-16T12:10:33.577888Z","title":"An open dataset for the evolution of oracle bone characters: Evobc.arXiv preprint arXiv:2401.12467, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.577888Z"},"links":{"cited_paper":"/paper/2401.12467","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:e652661cbfddf0e5cf0d851f0717c1729b757eaf2c525065abbd6f903b760154","observation_id":"683f73f8-1511-4fef-916b-84f91120adb4","resolution":{"observed_at":"2026-08-16T12:10:33.577888Z","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":"2026-08-16T12:10:34.259204Z","title":"Building hierarchical representations for oracle character and sketch recognition","venue":null,"work_id":"c753ecd5-d934-4048-9790-824cd2d99563","year":2015},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.582188Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:40587e6c2ab813351bea4d1f7ca337ab7ff86ba472a20814a3b4bc5a2c8de66c","observation_id":"d21c4852-0056-4072-b00d-b7acf821d054","resolution":{"observed_at":"2026-08-16T12:10:34.263374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.246994Z","title":"Accurateoracleclassificationbasedondeepconvolu- tionalneuralnetwork.In 2018IEEE18thInternationalConferenceon Communication Technology (ICCT), pages 1188–1191","venue":null,"work_id":"8d8bd010-d1ef-4313-a491-ca7c2e2f9271","year":2018},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.585994Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:f4602755b3d1dd49ff372bf208c8b6025730bc3ddd0fbeaecf1fcfe24cb09d86","observation_id":"daf3a12e-31e6-458b-951b-377270fbbcce","resolution":{"observed_at":"2026-08-16T12:10:34.251455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.233518Z","title":"Deep self-supervised learning for oracle bone inscriptions features representation","venue":null,"work_id":"f9944002-a4b2-4131-9e2a-fd3c10ee7d24","year":2021},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.589680Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:bdeec27caca7b40bd2c979e4691706ae6ebb1cc1a7e3d50213880cc2f920bedc","observation_id":"b2b06f1d-7aa2-4be0-ad26-9c379fac6631","resolution":{"observed_at":"2026-08-16T12:10:34.237726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.219210Z","title":"Large-scale oracle bone inscriptions dataset construc- tionandalgorithmresearch","venue":null,"work_id":"ad19f3e1-7bf9-4e3e-8c76-b6b7752c8e26","year":2020},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.594377Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:1bf7fa45b7ac2e96c747e615fa5b4632eeadd92a6635b6723270043f8ff4546a","observation_id":"6c14b8ab-fca2-4ef7-8069-89a9970e7216","resolution":{"observed_at":"2026-08-16T12:10:34.224410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.203098Z","title":"Oracle bone inscriptions recognition based on deep convolutional neural network","venue":null,"work_id":"474d39c1-4139-4e60-b910-174c17e03920","year":2020},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.598622Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:d9790cf311b705cbf5e43269580eddd4b16674aa9caea1b0568bb726766de1f3","observation_id":"78935b64-f810-48fc-9db9-8e64afe530c4","resolution":{"observed_at":"2026-08-16T12:10:34.208274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.189421Z","title":"Ai-powered oracle bone inscriptions recognition and fragments rejoining","venue":null,"work_id":"533adf58-0e77-4800-ba8c-c5b459f58155","year":2021},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.603840Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:4e66ccf5b8a064f448b3d18827d0b06d82ab5aca3bfbbb4eff5d71c6ddc56f00","observation_id":"2ebb71c4-54b3-4381-a1e1-f627ec689423","resolution":{"observed_at":"2026-08-16T12:10:34.194170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.176105Z","title":"Recognition of oracle bone inscriptions by using two deep learning models","venue":null,"work_id":"610dc227-aac6-4755-bc71-74f371f7f19c","year":2023},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.607959Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:726762dd7c3f04b74c40daa28a436712c09009e65a2f6318b04a5ea208745a7f","observation_id":"0bd35278-10b1-457f-ade7-df2b81ca5c4f","resolution":{"observed_at":"2026-08-16T12:10:34.180148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.163609Z","title":"Data-driven oracle bone rejoining: A dataset and practical self-supervised learning scheme","venue":null,"work_id":"eeb9f1c1-8bb6-477b-9239-3703c22cbe67","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.612044Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:0d0e82cfb1ec9a4578ad688d1be7766a8d696a0d884810bfe9557cb37795ce10","observation_id":"1a74f79c-669c-4753-8f56-d860fff4cabf","resolution":{"observed_at":"2026-08-16T12:10:34.167582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.150290Z","title":"A dataset of oracle characters for benchmarking machine learning algorithms","venue":null,"work_id":"58608a9f-fb86-46a2-bf4e-bbfed59975ff","year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.616123Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:ebd1fe4e2a382704532ba361726c67970966d3fa4691a2a6d7db25772c1c46df","observation_id":"2bf9da9c-1424-4508-95e8-f31a2ce8ef99","resolution":{"observed_at":"2026-08-16T12:10:34.154073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15365","last_updated":"2024-09-02T14:07:08Z","snapshot_observed_at":"2026-08-16T14:24:02.279828Z","submitted_at":"2024-01-27T09:54:16Z","title":"An open dataset for oracle bone script recognition and decipherment","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15365","snapshot_observed_at":"2026-08-16T12:10:33.620760Z","title":"An open dataset for oracle bone script recognition and decipherment.arXiv preprint arXiv:2401.15365, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.620760Z"},"links":{"cited_paper":"/paper/2401.15365","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:3fbcbaf18afbdaee420675f16e9fedec28ee1b4e52cefd46a9ef244385a8d5bb","observation_id":"c3ae7128-d405-4713-9d9b-ab698177891c","resolution":{"observed_at":"2026-08-16T12:10:33.620760Z","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":"2026-08-16T12:10:34.137038Z","title":"Mitigating long-tail distribution in oracle bone inscriptions: Dataset, model, and benchmark, 2025","venue":null,"work_id":"c6b09f19-dacb-45bf-96cb-4cb8daf80345","year":2025},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.625291Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:1a526731c37641978c3107b757ce0e4a394b2d18818aea95754dd4afe33390d6","observation_id":"8a1571f7-2463-44a8-a074-611385366f0b","resolution":{"observed_at":"2026-08-16T12:10:34.141276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.124562Z","title":"Oracle bone inscriptions in the collection of shanghai museum (volume i), 2009","venue":null,"work_id":"b9cd4f26-d63f-4e3f-bb00-64b4025738f2","year":2009},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.629155Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:af038de3c2042cec6e54a5efc7a616c2e533141c0c7487de3b733d1527a7e70b","observation_id":"6f7fefe6-7747-4626-ba29-117bf458d506","resolution":{"observed_at":"2026-08-16T12:10:34.128609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.633328Z","title":"Gradient-based learning applied to document recognition.Proceed- ings of the IEEE, 86(11):2278–2324, 1998","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.633328Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:e310300b67277cd34bfabbfbc021ea2a5a6ad37301aef15f6b269cb800c816f1","observation_id":"b715ccbc-faa9-4566-940a-d484cd134256","resolution":{"observed_at":"2026-08-16T12:10:33.633328Z","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":"2026-08-16T12:10:33.637756Z","title":"Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.637756Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:a710f1afbb7ed52f1a47b5ae667fa8c2066c2429b1b6187057f4467b008ee92d","observation_id":"d18b9195-6ed5-463a-b099-2fa578073676","resolution":{"observed_at":"2026-08-16T12:10:33.637756Z","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":"2026-08-16T12:10:33.641562Z","title":"Inception-v4, inception-resnet and the impact of residual connections on learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.641562Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:4679f610cf9495b1ca248ba153e24327841f59aa2742acae65c8dc04002d8dc7","observation_id":"63f96b8f-edd7-4a53-b8e4-5382b3f92870","resolution":{"observed_at":"2026-08-16T12:10:33.641562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-16T12:10:33.646243Z","title":"Very deep convolu- tional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.646243Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:d3e0826a2720d324bbb7de9a7e24247791fcde787657c6a3d88fe1e1415bb0a5","observation_id":"bf3cb19e-247a-4f7f-9563-dbfb7df9afa1","resolution":{"observed_at":"2026-08-16T12:10:33.646243Z","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":"2026-08-16T12:10:33.651463Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.651463Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:56867a3f1bb5d90a95e278ae393524635ab1e78d06dd1a928edadd9e8107c969","observation_id":"733b1736-5ee3-48ba-ac7b-4894ac5b9d4c","resolution":{"observed_at":"2026-08-16T12:10:33.651463Z","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":"2026-08-16T12:10:34.081922Z","title":"Oracle character recognition by nearest neighbor classifica- tion with deep metric learning","venue":null,"work_id":"9e70a6f4-1134-44a3-8d37-2f284903d120","year":2019},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.655789Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:3df210db24987b4f366678edcc0fc63014365f9e9ddac7d3c3ad14f1104859a7","observation_id":"ca818bcb-d65d-4704-ad18-a431530d449a","resolution":{"observed_at":"2026-08-16T12:10:34.087245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.068396Z","title":"Oraclecharacterrecognition using unsupervised discriminative consistency network","venue":null,"work_id":"2852e9ad-c019-492c-88b0-2ae8665d04e8","year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.659614Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:361b30cbd41eff57a5a1acf893270ace99e9f3487cb325e16e696aa8fbebb2e6","observation_id":"5eaa6a47-1873-4c26-bcc5-b52b21a9aaac","resolution":{"observed_at":"2026-08-16T12:10:34.073314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.056516Z","title":"Statistical techniques for digital pre-processing of computed tomography medi- cal images: A current review.Displays, 85:102835, 2024","venue":null,"work_id":"b90903de-5c85-4c33-8903-bd5ece36070a","year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.664532Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:e2d456a7ab4ba4e9a4caa3764a7630e17ce1306eac02f94af3a98ba7b6735373","observation_id":"446b27b6-dc2d-4612-b751-a3571f388672","resolution":{"observed_at":"2026-08-16T12:10:34.060941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.044081Z","title":"Lesion-inspired denoising network: Connecting medical image denoising and lesion detection","venue":null,"work_id":"2ea41093-656a-4042-85e6-3e81d1811d1b","year":2021},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.668518Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:261b35041a06a5aa718726de986c743afaca339933cd9e2d6c94303f8ff91574","observation_id":"b1915253-2a7c-43d4-b84f-c9a2956824ba","resolution":{"observed_at":"2026-08-16T12:10:34.049072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.030520Z","title":"Indeandcoe:Aframeworkbasedonmulti-scalefeaturefusion and residual learning for interferometric sar remote sensing image denoising and coherence estimation.Displays, 79:102496, 2023","venue":null,"work_id":"5c680aff-c4a0-4cbb-a3a9-58230b4a3e4d","year":2023},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.672902Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:7704297a057a4dac378e1d9c4d5c472bac3daf944930982dda0c099cdea081fc","observation_id":"08ee20a3-4bbd-4056-9945-43ae24c3eade","resolution":{"observed_at":"2026-08-16T12:10:34.035207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:34.017483Z","title":"Selectiveresidualm-netforrealimagedenoising","venue":null,"work_id":"a1be15c9-e403-4e7a-9a35-6bd039ecd8b3","year":null},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.677172Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:d2562c357a0172f66d5b2826cfe7194925042981febece954937b8d1f976fd3e","observation_id":"a4011a56-82ff-445b-9629-fd1b25fb6e5e","resolution":{"observed_at":"2026-08-16T12:10:34.021971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.680995Z","title":"U-net: Con- volutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.680995Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:846be4c379fcd7045a81de5ffc8f217343223f1d00fbbc47ca5b62ddc02291f0","observation_id":"4c0c0267-c53f-4608-b6dd-8a230971d126","resolution":{"observed_at":"2026-08-16T12:10:33.680995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.02881","last_updated":"2023-03-06T04:17:29Z","snapshot_observed_at":"2026-08-16T15:50:35.929157Z","submitted_at":"2023-03-06T04:17:29Z","title":"KBNet: Kernel Basis Network for Image Restoration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.02881","snapshot_observed_at":"2026-08-16T12:10:33.684935Z","title":"Kbnet: Kernel basis network for image restoration","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.684935Z"},"links":{"cited_paper":"/paper/2303.02881","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:de6a5279f2ff8432154750fe0076d5546124a0c3f7eefcbe545d07908ca0ee10","observation_id":"254937ad-3067-4600-a8b1-a42a7e84763f","resolution":{"observed_at":"2026-08-16T12:10:33.684935Z","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":"2026-08-16T12:10:33.997208Z","title":"Invertible denoising network: A light solution for real noise removal","venue":null,"work_id":"b0629fed-c73a-4a2c-9407-b9af892eba8a","year":2021},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.689468Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:44ad9708f5e52b604e66f7cdc57d3cdf7ead225c7c8b8137cc3b7caa829d16a5","observation_id":"dfb17d89-65cb-4bea-9f94-efe27ff00912","resolution":{"observed_at":"2026-08-16T12:10:34.001455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.983152Z","title":"Dual adversarial network: Toward real-world noise removal and noise generation","venue":null,"work_id":"beb98b25-f119-43f7-ba82-157361d33a98","year":2020},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.693924Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:0801a74a93f5da71a85d9f14774f9ef609b9fa86c4e905da85a9bfbf4eff5fe7","observation_id":"f5a7225a-2fa8-47e1-9815-f91e3c1bd528","resolution":{"observed_at":"2026-08-16T12:10:33.988328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.970601Z","title":"Animageisworth16x16words:Transformersfor image recognition at scale","venue":null,"work_id":"1a22b008-b40a-407b-9520-d628b0cacdcd","year":2021},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.697904Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:f9add340708d84336f3441812a8cb963c401acee192142e8c9a1e9da6834852c","observation_id":"06e8d47c-875d-45a2-a91f-df6fe4ec8632","resolution":{"observed_at":"2026-08-16T12:10:33.975172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.957188Z","title":"Uformer: A general u-shaped transformer for image restoration","venue":null,"work_id":"64ac6c78-a33a-4d71-97bf-c68887ff23e2","year":2022},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.701991Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:7019937ef84b151b51e90094a392929b9628f1f17607c95f66f6968bbd0477c0","observation_id":"918891aa-e1cb-443f-9f7d-1a3cabf3ebee","resolution":{"observed_at":"2026-08-16T12:10:33.961818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.944338Z","title":"Cascadedgaze: Efficiency in global context extraction for image restoration.Trans- actions on Machine Learning Research, 2024","venue":null,"work_id":"8f5f9487-9cd4-4c62-9971-61f72ea41e56","year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.706117Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:bb1385e4d4fb5b4ac4a507c8d3ec03ae491a2f595e49b6bff21e70453280d579","observation_id":"e8489153-8246-4e5e-857c-049a513973b8","resolution":{"observed_at":"2026-08-16T12:10:33.948611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.926771Z","title":"Selectivekernel networks","venue":null,"work_id":"d8b8eb4b-5d64-43c1-b0b6-3d4a04b780a1","year":2019},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.709832Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:b0636e83df25400a9d24bdce040da6181f1b443cfca365f459e2dcddcb15a2bb","observation_id":"ebf42736-e12a-43e6-b88e-921ca0757ea5","resolution":{"observed_at":"2026-08-16T12:10:33.931437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.713772Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.713772Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:2180227c4af40ad18aab12f4fb1242ae6354dfd6aa688c1e762243bffd604234","observation_id":"489ea0d8-fc10-40ec-8d2e-53e184505e53","resolution":{"observed_at":"2026-08-16T12:10:33.713772Z","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":"2026-08-16T12:10:33.904833Z","title":"Chinesecharacters strokethinningandextractionbasedonmathematicalmorphology[j]","venue":null,"work_id":"f2fbbe83-fa20-43da-9e3a-db212f8b5340","year":2005},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.717733Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:3ca04672b1a3c61091bf178c3bc7c25d17bfe96b0a3a0d9708ab732c6bc529d6","observation_id":"64bddf79-24ff-45b7-a324-7cb762ef22e8","resolution":{"observed_at":"2026-08-16T12:10:33.909660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.891739Z","title":"Jiaguwen zixing biao (a list of oracle characters)","venue":null,"work_id":"bae3b830-7300-43c8-8480-efe39a348a55","year":2008},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.722068Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:1dc6af24ca4be606eba7b849bfecb35688a6710dc66866aba4878e55a1a9de72","observation_id":"283a3dc8-7fa1-4bbb-b55a-46621a356221","resolution":{"observed_at":"2026-08-16T12:10:33.896311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-16T12:10:33.726103Z","title":"Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.726103Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:122bad76179ba9e36fe7cbca3241d2226e0b8163b65a86ea2480b259da89b627","observation_id":"5c9a3f49-1240-4c69-88e9-9eb14436de03","resolution":{"observed_at":"2026-08-16T12:10:33.726103Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00031","last_updated":"2024-08-17T13:20:55Z","snapshot_observed_at":"2026-08-16T13:25:51.254327Z","submitted_at":"2024-08-17T13:20:55Z","title":"Quality Assessment in the Era of Large Models: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.00031","snapshot_observed_at":"2026-08-16T12:10:33.730197Z","title":"Quality assessment in the era of large models: A survey.arXiv preprint arXiv:2409.00031, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.730197Z"},"links":{"cited_paper":"/paper/2409.00031","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:58a0555eed80a50014e286de55b0b80aa82f0f314bef0904dd981e1c81a938a6","observation_id":"943dc2be-849c-4930-a4e4-41817da30134","resolution":{"observed_at":"2026-08-16T12:10:33.730197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03070","last_updated":"2025-02-07T04:20:54Z","snapshot_observed_at":"2026-08-16T13:45:58.926186Z","submitted_at":"2024-06-05T08:55:02Z","title":"A-Bench: Are LMMs Masters at Evaluating AI-generated Images?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03070","snapshot_observed_at":"2026-08-16T12:10:33.734620Z","title":"A-bench:Arelmmsmastersatevaluatingai-generatedimages? arXiv preprint arXiv:2406.03070, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.734620Z"},"links":{"cited_paper":"/paper/2406.03070","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:17e733ee1814d8960323817a6de4b1dfdf28f2fa5c728b5e8fa8bcaf4d6b18b9","observation_id":"d86d2b7b-ab6c-40d5-a672-1d5d8d1c8dcd","resolution":{"observed_at":"2026-08-16T12:10:33.734620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.20063","last_updated":"2025-03-02T12:17:51Z","snapshot_observed_at":"2026-08-16T13:14:13.761126Z","submitted_at":"2024-09-30T08:05:00Z","title":"Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.20063","snapshot_observed_at":"2026-08-16T12:10:33.738860Z","title":"Q-bench-video: Benchmarking the video quality understanding of lmms.arXiv preprint arXiv:2409.20063, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.738860Z"},"links":{"cited_paper":"/paper/2409.20063","citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:56621b7e26086119c4b1bc8d7b32e6061a0245c85b6d87c4ce21c7c4ebd666c7","observation_id":"23df172d-7c4a-4f0d-96a8-597794770167","resolution":{"observed_at":"2026-08-16T12:10:33.738860Z","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":"2026-08-16T12:10:33.879336Z","title":"Study of subjective and objective naturalness assessment of ai-generated images","venue":null,"work_id":"953ebfad-10fa-4324-8e79-a2b7732df6db","year":2024},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.742953Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:17a6a79ef09e0294f5583ebff624a4b5f57d237010eb8e4f6db5589c625a98a5","observation_id":"c787c68a-42c1-4eef-a6ea-04fc55324794","resolution":{"observed_at":"2026-08-16T12:10:33.883688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T12:10:33.865760Z","title":"Image quality metrics: Psnr vs","venue":null,"work_id":"9ab30717-7537-4ada-892d-5f3863e70db1","year":2010},"citing_paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-16T12:10:33.746821Z"},"links":{"citing_paper":"/paper/2504.13524"},"observation_digest":"sha256:d04500e90849159caac56ce8cbfc50fc0938b31c10f338ad4100484b71ed45f3","observation_id":"34a11c54-842b-4b84-ae76-0a5ad4e2b0d7","resolution":{"observed_at":"2026-08-16T12:10:33.871399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.13524","last_updated":"2025-04-18T07:24:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T12:04:26.653681Z","submitted_at":"2025-04-18T07:24:35Z","title":"OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":44},"total_outbound_references":59},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2504.13524."}