{"as_of":"2026-08-22T12:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7bc7d27a564c4eb21a07d53bf0ea21265e9272ee9d97fe1f94e132f12c31b4bb","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T04:30:39.804735Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2608.08819/citation-record","integrity":"/paper/2608.08819/integrity","json":"/paper/2608.08819/citation-record.json","paper":"/paper/2608.08819"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T04:30:41.154701Z","title":"Mp2rage, a self bias-field corrected sequence for improved segmentation and t1-mapping at high field","venue":null,"work_id":"85b204c9-d0f0-4090-90a0-dd3d30f2b0f4","year":2010},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.137864Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:5c75fe73b5e995bbf4b0bddf4ec4140d914c307e4bb796f980ef4ff74c274e8b","observation_id":"9eaabd92-2b36-4c35-9119-b3bc4cc6346c","resolution":{"observed_at":"2026-08-14T04:30:41.158644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:41.135335Z","title":"Prostate cancer: multiparametric mr imaging for detection, localization, and staging","venue":null,"work_id":"40f87241-e41b-45fe-bbe3-3052a1032040","year":2011},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.142455Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:42d77e730d0f6886d80d3f92becf4d9f91486d5a01304770f13d8adff21528a8","observation_id":"909535aa-3d38-46bb-ba9f-f10bb50cc82e","resolution":{"observed_at":"2026-08-14T04:30:41.147101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:41.119235Z","title":"Systematic review and meta-analysis of ai-driven mri motion artifact detection and correction","venue":null,"work_id":"8cc7043d-1daf-4128-b6dd-8e0ba510fe98","year":2026},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.165740Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:29dfd3342a41968b47e202d23541b578b1f9da68176017f15533e346f47e1980","observation_id":"59364acf-1b1c-443d-b0e3-4fc0938bddfb","resolution":{"observed_at":"2026-08-14T04:30:41.126023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:41.074041Z","title":"Image super-resolution: A comprehensive review, recent trends, challenges and appli- cations","venue":null,"work_id":"5d80a3e3-83ef-4be4-a62b-c0d43a56131b","year":2023},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.187354Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:cf6e8ad314cfea7b3a9751759bb5fca7ada719e89777a8cf1c503f6b22faf433","observation_id":"ebd8eef8-3a71-47bf-9d95-b48b97bf031d","resolution":{"observed_at":"2026-08-14T04:30:41.090131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:39.212418Z","title":"Mri super-resolution with deep learning: A comprehensive survey","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.212418Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:3c994857c97b65334c1be365be9dcaae7930bc4df88e42734abea4cc331288aa","observation_id":"a5b73e51-0b2c-4617-9532-338ac3955f26","resolution":{"observed_at":"2026-08-14T04:30:39.212418Z","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":"2026-08-14T04:30:41.034841Z","title":"Generative adversarial network in medical imaging: A review","venue":null,"work_id":"f32541b4-e130-4e57-823b-e8445c17d593","year":2019},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.234748Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:bf870dc897d6cd75f2a653cd7d424dae2b6732041e38395130b033aa49a01092","observation_id":"f18c5763-0f8e-4356-8dec-3817d91785e4","resolution":{"observed_at":"2026-08-14T04:30:41.050948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.987658Z","title":"Structure-preserving super resolution with gradient guidance","venue":null,"work_id":"04fc89a8-4d5e-47dc-ad9a-d95c85ecd7e7","year":2020},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.274825Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:c3f4280e0e33675652512dfd5fa4b1bc9e691bf1d4b51452a35befe76b7e6957","observation_id":"63c01061-5203-437a-b6bc-1b76c3c0dc40","resolution":{"observed_at":"2026-08-14T04:30:41.001740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.958405Z","title":"Swinir: Image restoration using swin transformer","venue":null,"work_id":"645e1d04-b9d0-483f-bfed-530d99bb2adb","year":null},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.314818Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:0aad5f358d1d01e82823557b3e71f4f930a9f3fcc08b365630ef3e1aa2147753","observation_id":"e8d73594-74c4-403e-9b8a-3e6373c26bf0","resolution":{"observed_at":"2026-08-14T04:30:40.964698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.891044Z","title":"Mambair: A simple baseline for image restoration with state-space model","venue":null,"work_id":"33af7189-179e-4f92-9293-2d92eb36c404","year":2024},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.354756Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:553639f204b1802298905300875d167019cec320824eb1f4fab9ed38f4527db1","observation_id":"a3fa7aa9-cc0d-4684-92e0-819181a38ba4","resolution":{"observed_at":"2026-08-14T04:30:40.934753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.841946Z","title":"Self-supervised adversarial diffusion models for fast mri reconstruction","venue":null,"work_id":"24a7440a-a5b9-412a-8b1f-25fac97226fb","year":2025},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.402406Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:0fa38b9d95cd4e7edabf778a253443fe9f4a8dddd824ee8b4798f7b529b7398c","observation_id":"35d9d59b-6b6e-4837-b9a3-91ffb0182977","resolution":{"observed_at":"2026-08-14T04:30:40.856621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.802497Z","title":"Cycle-guided denoising diffusion probability model for 3d cross-modality mri synthesis","venue":null,"work_id":"dcb7804c-457f-4479-87b6-1e6c445a2b2b","year":2025},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.411678Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:3dcb46073ee00dd47a39a4b2c661b322a6c4659b02ca52cdfcb36a6b5382a81c","observation_id":"cbef0019-4ec2-4f92-b91b-6c7aa77b30cd","resolution":{"observed_at":"2026-08-14T04:30:40.811860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.780220Z","title":"Residual- shifted diffusion model for eﬀicient and accurate mri-based pet synthesis","venue":null,"work_id":"2eca6236-160f-425a-989a-6bb6dc227683","year":2026},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.445002Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:0e32bd094f018f852bddb4adf7b0ef7cd430f530db80b1d2922226d83a0af20b","observation_id":"c42447b6-3f52-4f05-beb3-beb62bc68d35","resolution":{"observed_at":"2026-08-14T04:30:40.786766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.722983Z","title":"High- resolution mri synthesis using a data-driven framework with denoising diffusion probabilistic modeling","venue":null,"work_id":"7d4d7ee8-b853-4029-b0bd-bcf85a00b6e9","year":2024},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.475632Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:ca0cbf24c7a8e11f37b20d24c35d3c824e2676987ff428dcd4c50bb2eda1d2b6","observation_id":"9e0f2e25-3d9a-4c37-ad5a-0f54b849d621","resolution":{"observed_at":"2026-08-14T04:30:40.746970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.694127Z","title":"Mri super-resolution reconstruc- 29 tion using eﬀicient diffusion probabilistic model with residual shifting","venue":null,"work_id":"5bc1f36f-f093-4ed0-9671-4a3460d9864e","year":2025},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.493157Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:4fd559c8a2cc7fc9b56c0899af8b643d3f3db4228e4a98240b87a6fcd4a3d206","observation_id":"0474abcb-5800-4890-ac14-6d596e0078c0","resolution":{"observed_at":"2026-08-14T04:30:40.713490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.667336Z","title":"Eﬀicient diffusion model for image restoration by residual shifting","venue":null,"work_id":"945ad54c-9fa1-46ec-997c-ec5f9d1300f9","year":2024},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.534750Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:bf063d708e3b93682182d1408945382f2d240fb8e1b43fdd5bd24a232b98bee9","observation_id":"5dd495b0-8de2-4da2-80dc-134e6bfce0e1","resolution":{"observed_at":"2026-08-14T04:30:40.677987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05872","last_updated":"2023-05-26T02:55:08Z","snapshot_observed_at":"2026-08-16T15:55:59.111718Z","submitted_at":"2023-02-12T08:35:39Z","title":"I$^2$SB: Image-to-Image Schr\\\"odinger Bridge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05872","snapshot_observed_at":"2026-08-14T04:30:39.576867Z","title":"I ˆ2 sb: Image-to-image schr\\” odinger bridge","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.576867Z"},"links":{"cited_paper":"/paper/2302.05872","citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:a76e18ceb8251f0c0b499e79905d719951e9849b92a278006e7f2385a6422276","observation_id":"f19e0696-07c0-4340-980c-c04593752ed7","resolution":{"observed_at":"2026-08-14T04:30:39.576867Z","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":"2026-08-14T04:30:40.649571Z","title":"Residual diffusion bridge model for image restoration","venue":null,"work_id":"58502a4d-7a61-443a-99b6-6377b7990233","year":2026},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.587895Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:27525e70ac21a8bf45e9759c9f9a93ce8a2ab178286d1346495b0339bfb0646b","observation_id":"f9de6351-ed29-40ee-bb65-db5045793d52","resolution":{"observed_at":"2026-08-14T04:30:40.657594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-14T04:30:39.614753Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.614753Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:fe417d3e6ac08a9c7c838e976342bc1519f586087b88a7cd02e01bfe14c0eebe","observation_id":"34b35b6f-dbef-4fc5-a341-b3a4b8014aff","resolution":{"observed_at":"2026-08-14T04:30:39.614753Z","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":"2026-08-14T04:30:40.622779Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":"c2a6340c-3c60-49b8-a8a9-35aa2b1f3378","year":2021},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.635410Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:07dc50a797612fd2d05365feaf10d9f2cf28946036e66a4126bdf1d1bef498e4","observation_id":"8b10bc97-d020-48f8-85be-d7672339abd7","resolution":{"observed_at":"2026-08-14T04:30:40.636676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.586905Z","title":"7 t lesion-attenuated magnetization- 30 prepared gradient echo acquisition for detection of posterior fossa demyelinating lesions in multiple sclerosis","venue":null,"work_id":"46f55c47-1940-484c-b383-ecc1b781e9fb","year":2024},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.674750Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:04d8ec5f010532a75ac4efea678235298e17b80c4d9683e97f7f43d009785b56","observation_id":"6b4b879c-ce07-4546-a4a0-c2d45dde02f1","resolution":{"observed_at":"2026-08-14T04:30:40.594161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.566527Z","title":"Prostatex challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images","venue":null,"work_id":"a39cfb3e-5e9b-4e27-86e0-16a2eb0d204f","year":2018},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.697726Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:d4cb073a12747ce69b646224acb3b961da63048013c5f156b2f956d757dae559","observation_id":"9eea4816-a475-43e7-872c-5e03b8aa099a","resolution":{"observed_at":"2026-08-14T04:30:40.572790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.485620Z","title":"Fast robust automated brain extraction","venue":null,"work_id":"474f52ab-afb9-4a52-8ebc-7a1b109d00a6","year":2002},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.715504Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:dba18df34064e5a486852cdadce6e33b2fc00f88b3ba0f107bda5d943f22d941","observation_id":"5ca5b5b6-0143-4b4c-8cd0-3dc977ef653b","resolution":{"observed_at":"2026-08-14T04:30:40.524731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.456182Z","title":"Eﬀicient vision mamba for mri super-resolution via hybrid selective scanning","venue":null,"work_id":"234deccc-3b58-4fa7-a9d7-234e2b4eb9f8","year":2026},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.720010Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:63a1948c00c8edbd00a4dad11f01e683efd18ee883dd5bcbf4edf49ec0cd5e3d","observation_id":"72ea5d55-1b6b-45b6-a498-d2a94daa302f","resolution":{"observed_at":"2026-08-14T04:30:40.469499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.424738Z","title":"Image-to-image translation with conditional adversarial networks","venue":null,"work_id":"435a013b-b032-4076-8773-7da22987c570","year":2017},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.724237Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:f92b8320ff6e9239ceb07669e6477b1e7f8cf7d157d27d239ed37598ca583078","observation_id":"bee2b7c4-c5ae-4fca-85a6-b45affbed6a8","resolution":{"observed_at":"2026-08-14T04:30:40.433069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.294833Z","title":"Unpaired image- to-image translation using cycle-consistent adversarial networks","venue":null,"work_id":"3fbfe6c4-cec5-4715-9d51-8908ce6feaf1","year":2017},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.744749Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:bb020d344711a278c12d5e5af07f11bbf4378f1854b2e27f518f13c7cc792946","observation_id":"d19fa791-2e53-4ae3-b784-18ae54aa7035","resolution":{"observed_at":"2026-08-14T04:30:40.319607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.216284Z","title":"Image qual- ity assessment: from error visibility to structural similarity","venue":null,"work_id":"44a25a63-9330-4a3d-9282-693cb8029b29","year":2004},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.755042Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:de4c7fe31dc76afe6f5e80d12f9e6f4aa5ea06e0f033b7a33fcf21bb1dfa499c","observation_id":"ec8c32aa-c53d-476d-b894-fae351f9346d","resolution":{"observed_at":"2026-08-14T04:30:40.236291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.141687Z","title":"Gradient magnitude similarity deviation: A highly eﬀicient perceptual image quality index","venue":null,"work_id":"5a303e02-f71b-445d-9442-2bee90b79927","year":2013},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.767690Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:e32c29d89627a9b9236c179df3d84d2276c0f33936d96787823ec7fbb5c16330","observation_id":"b70bb9f4-50d3-46c1-a78f-771490fcc5fb","resolution":{"observed_at":"2026-08-14T04:30:40.170380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.073608Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":"ebe6e818-58a5-467b-8026-3016c8ad1dd4","year":2018},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.776779Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:082bc565a5f596c102b8c126c3d06336e567110d9eb76252080f34ed1b6cd900","observation_id":"5acbdb39-48d5-462b-bf1e-836d1217e098","resolution":{"observed_at":"2026-08-14T04:30:40.091562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.043836Z","title":"Medfusiongan: multimodal medical image fusion using an unsupervised deep generative adversarial network","venue":null,"work_id":"7b2a5a14-0fd4-4760-b5d2-7b2f8a248eec","year":2023},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.780629Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:279b15e406a8feb9d824e0f68a3cb220a7b6bf4b0be79238e32fe17dcea7fe46","observation_id":"95f846b0-ca50-4baf-b3c8-32b867c8d92e","resolution":{"observed_at":"2026-08-14T04:30:40.052086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-14T04:30:40.025930Z","title":"The perception-distortion tradeoff","venue":null,"work_id":"fa25cd82-e484-423c-85b4-53d9cbdcbf8e","year":2018},"citing_paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T04:30:39.804735Z"},"links":{"citing_paper":"/paper/2608.08819"},"observation_digest":"sha256:3fe7a1b38e86b4e25535d1bff15e85dcf0fed6ad68345ce9102de27632300730","observation_id":"5df900a3-da0a-4cbc-9926-1bbf6a73bc9f","resolution":{"observed_at":"2026-08-14T04:30:40.032212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.08819","last_updated":"2026-08-09T17:09:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T02:19:45.433001Z","submitted_at":"2026-08-09T17:09:21Z","title":"MRI super-resolution in ten sampling steps using a diffusion bridge model"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":30},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2608.08819."}