{"as_of":"2026-08-12T07:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a13494de3fa90938693ce58ad4525d71df43e2e6268a297164116d0c54dc9599","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:34:12.338865Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:17:33.709950Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T14:00:12.993235Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24406","snapshot_observed_at":"2026-08-06T20:17:33.709950Z","title":"Irbridge: Solving image restoration bridge with pre-trained generative diffusion mod- els","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.03304","last_updated":"2025-07-04T05:17:32Z","snapshot_observed_at":"2026-08-11T14:06:14.251005Z","submitted_at":"2025-07-04T05:17:32Z","title":"Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:17:33.709950Z"},"links":{"cited_paper":"/paper/2505.24406","citing_paper":"/paper/2507.03304"},"observation_digest":"sha256:bd193102fe26fabb7107bdcbfd9c70ddc11ca09c9889675f3b6f8ea0477a0e74","observation_id":"4d4e4a51-aac1-4641-a3e7-efe0001d5e29","resolution":{"observed_at":"2026-08-06T20:17:33.709950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24406","snapshot_observed_at":"2026-08-06T17:39:37.941964Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10293","last_updated":"2025-07-14T14:01:37Z","snapshot_observed_at":"2026-08-07T11:31:38.708954Z","submitted_at":"2025-07-14T14:01:37Z","title":"Show and Polish: Reference-Guided Identity Preservation in Face Video Restoration","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:39:37.941964Z"},"links":{"cited_paper":"/paper/2505.24406","citing_paper":"/paper/2507.10293"},"observation_digest":"sha256:f527e2cce7ee917904bc04f635bcc3d914b4ed0e9c02318251140a76f4373319","observation_id":"682fd2f0-5e04-4538-b960-6f4c8abf7f97","resolution":{"observed_at":"2026-08-06T17:39:37.941964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24406","snapshot_observed_at":"2026-08-06T15:48:31.574133Z","title":"Irbridge: Solving image restoration bridge with pre-trained generative diffusion mod- els","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.14935","last_updated":"2025-07-20T12:09:19Z","snapshot_observed_at":"2026-08-09T02:21:57.024481Z","submitted_at":"2025-07-20T12:09:19Z","title":"Open-set Cross Modal Generalization via Multimodal Unified Representation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T15:48:31.574133Z"},"links":{"cited_paper":"/paper/2505.24406","citing_paper":"/paper/2507.14935"},"observation_digest":"sha256:5a6ee1b65c5ccf1da10e19527cb8e18f9dbe1801340211061b37dd1511cb1656","observation_id":"4f23e8e0-ffe8-4fd5-b1ec-ea7f4cc4fc3e","resolution":{"observed_at":"2026-08-06T15:48:31.574133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24406","snapshot_observed_at":"2026-08-05T21:53:31.545115Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.07878","last_updated":"2025-08-11T11:51:06Z","snapshot_observed_at":"2026-08-07T06:18:31.329878Z","submitted_at":"2025-08-11T11:51:06Z","title":"TAP: Parameter-efficient Task-Aware Prompting for Adverse Weather Removal","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T21:53:31.545115Z"},"links":{"cited_paper":"/paper/2505.24406","citing_paper":"/paper/2508.07878"},"observation_digest":"sha256:9d78b03c176e8c24c5063ea4dbdc2f08493832c97fdb62b5573a9e5e30918c91","observation_id":"97b78efa-805e-40c6-ba3b-0ffd08c1ebae","resolution":{"observed_at":"2026-08-05T21:53:31.545115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"cited_work":{"arxiv_id":"2505.24406","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.24406","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Irbridge: Solving image restoration bridge with pre-trained generative diffu- sion models","venue":null,"work_id":"06d2ca94-4715-4649-a205-9e1ec28cd478","year":2025},"citing_paper":{"arxiv_id":"2601.20306","last_updated":"2026-05-17T09:55:00Z","snapshot_observed_at":"2026-07-06T22:43:17.026472Z","submitted_at":"2026-01-28T06:55:07Z","title":"TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-05-21T14:00:00.292925Z"},"links":{"cited_paper":"/paper/2505.24406","citing_paper":"/paper/2601.20306"},"observation_digest":"sha256:ca9a411c60d56d5b447246a2626bf542e39e296410be3f83285d35963bc44b9d","observation_id":"8c6a279b-6357-4c0b-b600-1f2495771760","resolution":{"observed_at":"2026-05-21T14:00:12.995472Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.24406/citation-record","integrity":"/paper/2505.24406/integrity","json":"/paper/2505.24406/citation-record.json","paper":"/paper/2505.24406"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:34:13.167017Z","title":null,"venue":null,"work_id":"ff0e1412-0c57-4e92-8d68-c7a39eafa569","year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:12.160260Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:1f183a021157bb04960985307985c3f4421beaeff7c3d154f5768ab30d4bd35b","observation_id":"190521f4-6d34-4fa7-b413-88ca564fa273","resolution":{"observed_at":"2026-08-07T12:34:13.229517Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T12:34:10.944098Z","title":"Li, B., Xue, K., Liu, B., and Lai, Y .-K","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:10.944098Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:0d88fd33f8cee994434700d68facfbedf1d557dc53a9520298c1fbca6709b15d","observation_id":"5d7c4bd8-6802-4fd0-8cc4-a3cc3f9c794f","resolution":{"observed_at":"2026-08-07T12:34:10.944098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-07T12:34:11.157056Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.157056Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:c059465b6fe9e3c11cd830f1a1ce5864704dcce48f92b34c1e088467444255ab","observation_id":"f9f2efe4-a331-4a86-b991-13d1cedb5112","resolution":{"observed_at":"2026-08-07T12:34:11.157056Z","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":"2026-08-07T12:34:11.314466Z","title":"Peng, X., Zheng, Z., Dai, W., Xiao, N., Li, C., Zou, J., and Xiong, H","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.314466Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:53d35d2967c3510404fecdd423880a6ca0f83b9594d86aefe632f8fdbe90d13f","observation_id":"4c90527f-0092-449f-9402-eecffbf546b9","resolution":{"observed_at":"2026-08-07T12:34:11.314466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17687","last_updated":"2025-03-22T18:40:40Z","snapshot_observed_at":"2026-08-09T12:23:48.858800Z","submitted_at":"2024-11-26T18:55:49Z","title":"GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration","version":2},"cited_work":{"arxiv_id":"2411.17687","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.17687","snapshot_observed_at":"2026-08-07T12:34:12.466162Z","title":"GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration","venue":"cs.CV","work_id":"ebaedc2a-9610-41d9-83c6-f2a91ce7e72c","year":2024},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.535421Z"},"links":{"cited_paper":"/paper/2411.17687","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:4bad75225f5276930b629c48724ed1e34dc63fad4a7fec14d5dce9e182484600","observation_id":"50e1f772-454f-4070-88ce-739886c988ac","resolution":{"observed_at":"2026-08-07T12:34:12.525539Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T12:34:11.716876Z","title":"P., Kumar, A., Er- mon, S., and Poole, B","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.716876Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:63aff75d77affdd347a9a37d7330abaf14989fa1253826a41c859a2919448925","observation_id":"9a117e28-88b3-47db-9eb0-6923239b06b7","resolution":{"observed_at":"2026-08-07T12:34:11.716876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07538","last_updated":"2025-05-27T11:07:09Z","snapshot_observed_at":"2026-08-09T03:16:49.369471Z","submitted_at":"2025-05-12T13:19:08Z","title":"Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07538","snapshot_observed_at":"2026-08-07T12:34:11.795076Z","title":"Y ., Pan, J., Wu, W., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.795076Z"},"links":{"cited_paper":"/paper/2505.07538","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:071306bf89f779ab4ab08c880bd9ec4111ad04f9e5b746bcc2d9c5833a2a1426","observation_id":"03ed1463-9d7e-49f5-bd31-d818ef5b29e3","resolution":{"observed_at":"2026-08-07T12:34:11.795076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10299","last_updated":"2024-05-18T03:46:52Z","snapshot_observed_at":"2026-08-09T22:46:19.989799Z","submitted_at":"2023-12-16T03:09:28Z","title":"Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10299","snapshot_observed_at":"2026-08-07T12:34:11.929745Z","title":"Image restoration through generalized ornstein-uhlenbeck bridge.arXiv preprint arXiv:2312.10299, 2023a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.929745Z"},"links":{"cited_paper":"/paper/2312.10299","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:4d03a1a780a583b6933cb7daab831e6c7336dd44d7b330d77a9f0c1d9b22e7d7","observation_id":"ecf4198e-516c-4693-802f-ddc2821537d4","resolution":{"observed_at":"2026-08-07T12:34:11.929745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16948","last_updated":"2023-12-05T08:01:39Z","snapshot_observed_at":"2026-08-11T04:35:09.329441Z","submitted_at":"2023-09-29T03:24:24Z","title":"Denoising Diffusion Bridge Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16948","snapshot_observed_at":"2026-08-07T12:34:12.012934Z","title":"Denoising dif- fusion bridge models.arXiv preprint arXiv:2309.16948,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:12.012934Z"},"links":{"cited_paper":"/paper/2309.16948","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:7b3edd557ec85388e2a4f364ed8f6344c790355f399b2a4980a5fce5b4d9cb28","observation_id":"c3286874-8e1b-4ee3-a6e9-adbcd51f0cb2","resolution":{"observed_at":"2026-08-07T12:34:12.012934Z","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":"2026-08-07T12:34:13.287324Z","title":"For detailed definitions of the symbols shown, please refer to the corresponding original paper","venue":null,"work_id":"4671692e-4b04-4a6f-98e8-fc854da09474","year":2020},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:12.088863Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:3ad5d4821954301a1e984ac75fe6f691d54d293a38140c1077245a44b5899a91","observation_id":"3836f110-f072-4fa9-846d-7d73db04a3dc","resolution":{"observed_at":"2026-08-07T12:34:13.344043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T12:34:13.030128Z","title":"To manage the learning rate, a constant schedule is employed with 500 warmup steps to gradually ramp up the learning rate at the start of training","venue":null,"work_id":"40843dfa-2d6d-4d03-9555-9e7842c2968f","year":2023},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:12.231100Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:fd25645f3895ca2b8f98d309640eb7af1969c6047bb78063809916be85474a5b","observation_id":"5828dc37-c564-4522-9338-783189ba1f01","resolution":{"observed_at":"2026-08-07T12:34:13.099148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T12:34:12.923125Z","title":null,"venue":null,"work_id":"2b8677d3-0ce6-4fad-aa60-512024cb5f48","year":2021},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:12.286954Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:f9d84c74b1d105c642376b4b747b54e475e16489ec4bf6b514bf527507069c52","observation_id":"a448f16c-3353-41ae-bda9-ef04824bc1c3","resolution":{"observed_at":"2026-08-07T12:34:12.983823Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T12:34:12.798361Z","title":null,"venue":null,"work_id":"b92d9fad-59c7-4b95-99a6-18e7393bd684","year":2024},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:12.338865Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:9b1bffd872d58fd362e317cc964ef80e3b9942cf95e88b299be561d1f572f704","observation_id":"67f63dca-c2f7-4266-9851-555cdd3a5fdd","resolution":{"observed_at":"2026-08-07T12:34:12.844236Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.04560","last_updated":"2018-08-14T07:20:55Z","snapshot_observed_at":"2026-08-02T03:51:27.789282Z","submitted_at":"2018-08-14T07:20:55Z","title":"Deep Retinex Decomposition for Low-Light Enhancement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.04560","snapshot_observed_at":"2026-08-07T12:34:11.864433Z","title":"Deep retinex decomposition for low-light enhancement.arXiv preprint arXiv:1808.04560,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2004,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.864433Z"},"links":{"cited_paper":"/paper/1808.04560","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:e549aab1d75351f17dd5089effff7d9113ec37bb4220f603dc93146f843b77c9","observation_id":"b4a89759-73a3-442d-9658-546ba8008fb3","resolution":{"observed_at":"2026-08-07T12:34:11.864433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-07T12:34:11.626153Z","title":"Denoising diffusion implicit models.arXiv:2010.02502, October 2020a","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.626153Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:c5152abd95a3208a0af89bfd9838dcb43675e88033f73a4990f55dd9e14b1618","observation_id":"4ab9492c-905a-4c63-abb8-b34fe36fda00","resolution":{"observed_at":"2026-08-07T12:34:11.626153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19754","last_updated":"2025-03-04T03:11:53Z","snapshot_observed_at":"2026-08-09T14:10:49.229856Z","submitted_at":"2025-02-27T04:34:03Z","title":"Finding Local Diffusion Schr\\\"odinger Bridge using Kolmogorov-Arnold Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.19754","snapshot_observed_at":"2026-08-07T12:34:11.411603Z","title":"Finding local diffusion schr\\” odinger bridge using kolmogorov-arnold network.arXiv preprint arXiv:2502.19754,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.411603Z"},"links":{"cited_paper":"/paper/2502.19754","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:81419d2e0efb50ec94b7ab6dd4fbc0966cebcf4204884876416a62bc7877aa12","observation_id":"87c13ea3-5ede-4e29-9326-b77e22e7cb8b","resolution":{"observed_at":"2026-08-07T12:34:11.411603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14687","last_updated":"2024-05-20T04:23:45Z","snapshot_observed_at":"2026-08-03T03:59:22.374270Z","submitted_at":"2022-09-29T11:12:27Z","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14687","snapshot_observed_at":"2026-08-07T12:34:10.608840Z","title":"T., Klasky, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:10.608840Z"},"links":{"cited_paper":"/paper/2209.14687","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:dd5623d8fcb3eff7ffb93d8aa741ff19dd9a903f30f4269ea2edfdf24d280ff7","observation_id":"87ce1b8a-b30a-4cd7-a713-421d1b88f3db","resolution":{"observed_at":"2026-08-07T12:34:10.608840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15510","last_updated":"2025-01-26T13:03:37Z","snapshot_observed_at":"2026-08-10T14:11:01.048495Z","submitted_at":"2025-01-26T13:03:37Z","title":"Universal Image Restoration Pre-training via Degradation Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15510","snapshot_observed_at":"2026-08-07T12:34:10.780702Z","title":"Universal image restora- tion pre-training via degradation classification.arXiv preprint arXiv:2501.15510,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:10.780702Z"},"links":{"cited_paper":"/paper/2501.15510","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:db888be753881a6465e320c3ba4e02e2e885f3194599bde35b4613b45a88b911","observation_id":"626eb50c-7372-496e-96b6-a55cd3d9e30d","resolution":{"observed_at":"2026-08-07T12:34:10.780702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11435","last_updated":"2024-02-02T18:52:51Z","snapshot_observed_at":"2026-08-06T06:26:04.600331Z","submitted_at":"2023-03-20T20:28:17Z","title":"Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11435","snapshot_observed_at":"2026-08-07T12:34:10.678133Z","title":"and Milanfar, P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:10.678133Z"},"links":{"cited_paper":"/paper/2303.11435","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:0bed43ef1c0ba19e2a4a48a36a859a09586e970bf5a5e79a56f32d76ebeb2867","observation_id":"a4d81d79-3e4f-4cf6-877f-f77ad7d71220","resolution":{"observed_at":"2026-08-07T12:34:10.678133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06914","last_updated":"2024-05-11T05:01:53Z","snapshot_observed_at":"2026-07-06T18:12:55.888082Z","submitted_at":"2024-05-11T05:01:53Z","title":"Non-confusing Generation of Customized Concepts in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06914","snapshot_observed_at":"2026-08-07T12:34:11.010339Z","title":"Tavt: Towards transferable audio-visual text generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.010339Z"},"links":{"cited_paper":"/paper/2405.06914","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:dbcd92c31f1b235c69bdda61e6cd203bfc17fe7f8ada5503f8e664a51afa3e26","observation_id":"465e1b76-7f83-44e5-bf7a-bf896dcb4b5e","resolution":{"observed_at":"2026-08-07T12:34:11.010339Z","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":"2026-08-07T12:34:13.421007Z","title":"K., and Bovik, A","venue":null,"work_id":"0f6ed12c-e01e-4501-9ead-e4535c6312b5","year":2011},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:11.272317Z"},"links":{"citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:77eea11a9a5c07b646ccd5931dfd250ec938a9aaf54313facd348bccc37d14e9","observation_id":"e5493432-4edc-41f0-bb2c-63ef8ae7a209","resolution":{"observed_at":"2026-08-07T12:34:13.489421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.13170","last_updated":"2019-12-31T04:49:30Z","snapshot_observed_at":"2026-08-11T16:45:25.374287Z","submitted_at":"2019-12-31T04:49:30Z","title":"Schr\\\"odinger Bridge Samplers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.13170","snapshot_observed_at":"2026-08-07T12:34:10.527192Z","title":null,"venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:10.527192Z"},"links":{"cited_paper":"/paper/1912.13170","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:dba0a50a056db2c41e6235841003deabbb238be1690d20628a20ddbfc0572691","observation_id":"cd9cbcf9-a2fb-4534-8759-f0ef1f484b4c","resolution":{"observed_at":"2026-08-07T12:34:10.527192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05168","last_updated":"2025-06-01T05:09:27Z","snapshot_observed_at":"2026-07-06T17:41:31.462546Z","submitted_at":"2024-03-08T09:16:47Z","title":"Enhancing Multimodal Unified Representations for Cross Modal Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05168","snapshot_observed_at":"2026-08-07T12:34:10.867408Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T12:34:10.867408Z"},"links":{"cited_paper":"/paper/2403.05168","citing_paper":"/paper/2505.24406"},"observation_digest":"sha256:06412c325b043920916bfd696213e5403a00099c21329d65dd749beda31d7bf3","observation_id":"288bda4c-ce78-4799-8c56-e629948e9b1c","resolution":{"observed_at":"2026-08-07T12:34:10.867408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.24406","last_updated":"2025-05-30T09:45:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T12:23:21.736774Z","submitted_at":"2025-05-30T09:45:41Z","title":"IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":23},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 5 inbound Pith citation observations for arXiv:2505.24406."}