{"as_of":"2026-08-18T03:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d4279e5b0138ff9d94e15784d9790c580eca026249e4a4d8c5d900be5f7f2eae","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T01:09:10.191768Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.02159/citation-record","integrity":"/paper/2505.02159/integrity","json":"/paper/2505.02159/citation-record.json","paper":"/paper/2505.02159"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-16T01:09:09.894435Z","title":"Long- former: The long-document transformer","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.894435Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:10d27e682a8b3a1750dfe6179e3d2094b513768a33adacbec7d4d8bb8110f73b","observation_id":"ab54a669-4784-4513-b304-2f37d68cbdcc","resolution":{"observed_at":"2026-08-16T01:09:09.894435Z","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":"2026-08-16T01:09:11.319610Z","title":"Real- time video super-resolution with spatio-temporal networks and motion compensation","venue":null,"work_id":"7866e783-9a60-4fab-b24d-19d0f017bf0c","year":2017},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.900334Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:92d6d6f64eb5961be200c5d43e86e5c883b8f7bd2894080e1822df2fb854291c","observation_id":"fe0d8285-68fa-4b11-b00e-d4206bdb7725","resolution":{"observed_at":"2026-08-16T01:09:11.325736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.06847","last_updated":"2023-07-04T15:30:58Z","snapshot_observed_at":"2026-08-16T18:17:35.513526Z","submitted_at":"2021-06-12T20:00:32Z","title":"Video Super-Resolution Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06847","snapshot_observed_at":"2026-08-16T01:09:09.905142Z","title":"Video super-resolution transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.905142Z"},"links":{"cited_paper":"/paper/2106.06847","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:0d65c894913f3387c77e652714f9809660903dbbced2230d4a2b61ce18336016","observation_id":"d23daf56-037c-4008-b8dc-61fd03a0f6ea","resolution":{"observed_at":"2026-08-16T01:09:09.905142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.02181","last_updated":"2021-04-07T11:23:38Z","snapshot_observed_at":"2026-08-16T19:01:09.155670Z","submitted_at":"2020-12-03T18:56:14Z","title":"BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond","version":2},"cited_work":{"arxiv_id":"2012.02181","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.02181","snapshot_observed_at":"2026-08-16T01:09:10.436441Z","title":"BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond","venue":"cs.CV","work_id":"8b25d474-68f1-4791-befc-3a8630f2434a","year":2020},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.910926Z"},"links":{"cited_paper":"/paper/2012.02181","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:3bb1438f877a0203e74c1b99382957d05491a3472cab0d0b4176200557b93b42","observation_id":"044d9f3a-3d39-46ca-bbd6-0f48309ba300","resolution":{"observed_at":"2026-08-16T01:09:10.824100Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.306256Z","title":"Basicvsr: The search for essential compo- nents in video super-resolution and beyond","venue":null,"work_id":"e1fc13c8-1f96-4355-a178-5dd63a019333","year":2021},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.915809Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:bce6bb1aed68a6a0f8c3b04f8acba60ba5aba9f2f907ff5d016447e79d55a87e","observation_id":"fc0730b1-c0f0-4773-b52a-64701a937bc6","resolution":{"observed_at":"2026-08-16T01:09:11.311270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.293697Z","title":"Basicvsr++: Improving video super- resolution with enhanced propagation and alignment","venue":null,"work_id":"4e0d36dd-d40e-4479-8e9d-0650502b421e","year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.920392Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:9acfd8a5d70d7614138f368d3d5bb8e919f426a740f9f6aa583b9762dd5720b4","observation_id":"ddc71bae-05d0-470e-a59e-779be4219610","resolution":{"observed_at":"2026-08-16T01:09:11.297736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.280329Z","title":"Investigating tradeoffs in real-world video super-resolution","venue":null,"work_id":"4317a629-6e35-4b0a-a220-d2a5bf7e7fca","year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.926422Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:bef5664db47e0109ea1d55307f3f0bfe7a58967c4d9eaabfb9491497ed71a5b1","observation_id":"8ae29442-efbc-4f54-9eba-6b1b9faf483d","resolution":{"observed_at":"2026-08-16T01:09:11.285053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.262345Z","title":"Two deterministic half-quadratic regular- ization algorithms for computed imaging","venue":null,"work_id":"86188a08-91cc-40bf-92cf-322672616600","year":1994},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.931574Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:6110e4c775599c30337f7ab043e6450cb0f92f09c8d2b759f938aa188db5dbe5","observation_id":"9d2525a6-e813-420a-8eb4-85242d651e34","resolution":{"observed_at":"2026-08-16T01:09:11.269656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.10509","last_updated":"2019-04-23T19:29:47Z","snapshot_observed_at":"2026-08-16T10:03:03.268538Z","submitted_at":"2019-04-23T19:29:47Z","title":"Generating Long Sequences with Sparse Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.10509","snapshot_observed_at":"2026-08-16T01:09:09.938424Z","title":"Generating long sequences with sparse transformers","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.938424Z"},"links":{"cited_paper":"/paper/1904.10509","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:2a41f75c4448494c6eb7a9e67897a027a6f7896c55e392623ac24b9893cad4a4","observation_id":"1d341f44-d7dd-49de-b47f-fc9b2b0bb87e","resolution":{"observed_at":"2026-08-16T01:09:09.938424Z","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":"2026-08-16T01:09:11.243245Z","title":"Learning temporal coherence via self- supervision for gan-based video generation","venue":null,"work_id":"da8ca306-4101-4801-b74e-855b63de9c67","year":2020},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.945142Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:060b4f4cd882807b5b4857a2f0724c9dfd9334dc94be7e15fe9ef5b19db4d7e3","observation_id":"45204a22-af48-43df-9d08-a387d79151e9","resolution":{"observed_at":"2026-08-16T01:09:11.248057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.229325Z","title":"Deformable convolutional net- works","venue":null,"work_id":"76f79b85-1ac5-482f-94e2-217c77baf2e6","year":2017},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.949174Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:ed4040aaea7abd26aa284508e3aa7d040636e4a949c3ba717fa3bac6e86d1d49","observation_id":"b7a4860b-1ed5-4389-848f-4c88adc12bda","resolution":{"observed_at":"2026-08-16T01:09:11.233448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.216226Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","venue":null,"work_id":"dca21a86-bb5d-4c7f-9827-ca0a9a0163f2","year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.954723Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:a6cf520bc5c926b7ce123f9bda64ed677d95f900a94f4ecf431163a42a2f50f2","observation_id":"487f2ae8-3c61-4c7e-bfdd-daa5c78e84c4","resolution":{"observed_at":"2026-08-16T01:09:11.220505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02486","last_updated":"2023-07-19T12:25:35Z","snapshot_observed_at":"2026-08-16T15:18:27.962961Z","submitted_at":"2023-07-05T17:59:38Z","title":"LongNet: Scaling Transformers to 1,000,000,000 Tokens","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.02486","snapshot_observed_at":"2026-08-16T01:09:09.958449Z","title":"Longnet: Scaling transformers to 1,000,000,000 tokens.arXiv preprint arXiv:2307.02486, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.958449Z"},"links":{"cited_paper":"/paper/2307.02486","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:1fa6a0b0bfca42a1fc97a82f2b6e2b97dc112104abdb61cd4ce0e9ebacee3627","observation_id":"ba9cc3b6-ddc2-448c-989b-9a2f871ea893","resolution":{"observed_at":"2026-08-16T01:09:09.958449Z","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":"2026-08-16T01:09:11.200568Z","title":"Efficient video super-resolution through recurrent latent space propagation","venue":null,"work_id":"45c2e1c0-c8a6-4a44-9e60-05bcf06ad410","year":2019},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.968892Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:c754b38f456e654a59b30ff7cad4d6462b2ac2b24648089f717481422dbd1c1c","observation_id":"275f777b-828d-4d41-9b8c-6a945997e542","resolution":{"observed_at":"2026-08-16T01:09:11.206522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-16T01:09:09.978438Z","title":"Efficiently mod- eling long sequences with structured state spaces","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.978438Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:148863d2998c83c16ef889cf17bcd84a339953edd884f066823243146027a92c","observation_id":"e10ee485-676c-4c44-8e6a-57a8a41b1685","resolution":{"observed_at":"2026-08-16T01:09:09.978438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.00455","last_updated":"2020-08-02T11:01:19Z","snapshot_observed_at":"2026-08-08T13:57:04.280839Z","submitted_at":"2020-08-02T11:01:19Z","title":"Video Super-Resolution with Recurrent Structure-Detail Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.00455","snapshot_observed_at":"2026-08-16T01:09:09.986679Z","title":"Video super-resolution with recurrent structure-detail network","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.986679Z"},"links":{"cited_paper":"/paper/2008.00455","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:eff3ffd41428405b9c3884142e4c95ce99a0b2554520e827d2c25f8969f3f918","observation_id":"6944ae22-81c6-49c6-8a8f-c2c644185b91","resolution":{"observed_at":"2026-08-16T01:09:09.986679Z","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":"2026-08-16T01:09:11.184220Z","title":"Video super-resolution with temporal group attention","venue":null,"work_id":"7e48e941-3c58-471f-9593-7b6e2fb687ee","year":2020},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.992388Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:7ecad3832aea8ab7ae05ba8285d19b6c853448fb80cf7ac61b1cc5d039ccd897","observation_id":"5a5ce5e1-c534-4b40-a223-1ed6757d8f69","resolution":{"observed_at":"2026-08-16T01:09:11.189912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.171412Z","title":"Look back and forth: Video super-resolution with explicit temporal difference modeling","venue":null,"work_id":"b6629324-51d8-4e29-9b74-716b6d925584","year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:09.996386Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:56ad858283c6099715912c89465ae92747c88707e4dca72c93a10f13fdc1f48d","observation_id":"4c1ac9d2-a4e6-40fc-a25d-f1a8efaee74f","resolution":{"observed_at":"2026-08-16T01:09:11.175323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.155992Z","title":"Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation","venue":null,"work_id":"109abed7-2fba-42cc-a77d-6924f99a1371","year":2018},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.000978Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:6d5814aa9bb5ba086ae9776ebde96b60732a145646f6f413829f496f1c2853dc","observation_id":"3505aeea-0cd4-4723-83ba-6d9df91b0a66","resolution":{"observed_at":"2026-08-16T01:09:11.161448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.138599Z","title":"Transformers are rnns: Fast autoregressive transformers with linear attention","venue":null,"work_id":"feb80984-cae4-41f4-9b8a-2353c672df0d","year":2020},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.007107Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:87028ccce33ef722c695d9bbc50064749662dd4e616bf846b348b01ed7a1f27e","observation_id":"87327aab-a809-4ccc-9889-3ccb0339218b","resolution":{"observed_at":"2026-08-16T01:09:11.143907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.125971Z","title":"Efficient memory management for large lan- guage model serving with pagedattention","venue":null,"work_id":"d634ae84-3a4c-4c7e-8f46-56bfc762ab06","year":2023},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.012339Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:ff81806a4b7403e3326b14bed9adc090bab8f36f2fcc065cf6ddd1e8630e48ca","observation_id":"e9922734-07fc-47e2-a455-ab2d2197e796","resolution":{"observed_at":"2026-08-16T01:09:11.130226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.13120","last_updated":"2022-05-21T06:03:54Z","snapshot_observed_at":"2026-08-16T18:22:05.145735Z","submitted_at":"2021-05-26T13:40:58Z","title":"Sequence Parallelism: Long Sequence Training from System Perspective","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.13120","snapshot_observed_at":"2026-08-16T01:09:10.017189Z","title":"Sequence parallelism: Long sequence training from system perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.017189Z"},"links":{"cited_paper":"/paper/2105.13120","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:69ff093dad281d58ac2235faf5dc2f599429f7cb20ff89ea7cccde9240b3c042","observation_id":"444b20d1-5d06-4556-a7e4-cc13d0cd0df1","resolution":{"observed_at":"2026-08-16T01:09:10.017189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.11803","last_updated":"2020-07-23T05:41:27Z","snapshot_observed_at":"2026-08-13T22:03:07.435196Z","submitted_at":"2020-07-23T05:41:27Z","title":"MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.11803","snapshot_observed_at":"2026-08-16T01:09:10.025629Z","title":"Mucan: Multi-correspondence aggregation network for video super-resolution","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.025629Z"},"links":{"cited_paper":"/paper/2007.11803","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:0fda7777c844e8a99dd990a31bd8d8e7134eb7eba004e347172175859c8a3acc","observation_id":"98df0be7-10a7-432a-a225-c2da68ba2b37","resolution":{"observed_at":"2026-08-16T01:09:10.025629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12288","last_updated":"2022-06-15T17:17:05Z","snapshot_observed_at":"2026-08-16T17:25:10.483480Z","submitted_at":"2022-01-28T17:54:43Z","title":"VRT: A Video Restoration Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12288","snapshot_observed_at":"2026-08-16T01:09:10.033258Z","title":"Vrt: A video restoration transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.033258Z"},"links":{"cited_paper":"/paper/2201.12288","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:fd11645ff4fe2199d44c9567eff98451a2aae17dba4b2fc98060ef3c72867bd1","observation_id":"a142d09f-99af-419c-9f95-a989bcbbedc1","resolution":{"observed_at":"2026-08-16T01:09:10.033258Z","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":"2026-08-16T01:09:11.112641Z","title":"Recurrent video restoration transformer with guided deformable attention","venue":null,"work_id":"b0163fec-8b0c-4572-8ae9-0d401809470c","year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.043302Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:16ab9b00331491255158998004c9fb2737acce39e4bed7dd48e31fc12a30f20e","observation_id":"cbedc642-c7fe-4216-b532-154ada117e32","resolution":{"observed_at":"2026-08-16T01:09:11.117290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.096381Z","title":"Learning trajectory-aware transformer for video super- 9 resolution","venue":null,"work_id":"03cf5cca-a412-436f-98ef-208a75d047ef","year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.048984Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:0956936ffd462d74981b46238729f88f3b85a2dbbed440574a5265236c815281","observation_id":"e6ea2b44-6868-4520-b153-88219e1e123d","resolution":{"observed_at":"2026-08-16T01:09:11.101331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.080818Z","title":"Robust video super-resolution with learned temporal dynamics","venue":null,"work_id":"6af27b92-e860-45b1-9840-bd728624e4ca","year":2017},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.055501Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:c3a187a4cdac556a35e10b19d8704a886e62e7cb1ec025339e8bd7877146da7a","observation_id":"ac3f869f-e45c-460c-8966-589607da5cc5","resolution":{"observed_at":"2026-08-16T01:09:11.085598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.10198","last_updated":"2018-01-30T20:07:01Z","snapshot_observed_at":"2026-08-14T19:50:32.053437Z","submitted_at":"2018-01-30T20:07:01Z","title":"Generating Wikipedia by Summarizing Long Sequences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.10198","snapshot_observed_at":"2026-08-16T01:09:10.061058Z","title":"Generating wikipedia by summarizing long sequences","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.061058Z"},"links":{"cited_paper":"/paper/1801.10198","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:70c06fae5c927427fa7660799a8ea397719df21c3b7d40300ecc8a7e2d0ce29e","observation_id":"a6cd9c30-c60e-413b-8056-56cd3620cff0","resolution":{"observed_at":"2026-08-16T01:09:10.061058Z","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":"2026-08-16T01:09:11.066676Z","title":"Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study","venue":null,"work_id":"6ebe0255-4245-4d79-8c40-f3f6ef1c3b4a","year":2019},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.066801Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:bb08c5d36613334c63ab19eef0e02e1e677778abbb84e13943e9e6a19017f016","observation_id":"ef14bbad-5a0b-47fc-a20d-d6d7a4cfbe4c","resolution":{"observed_at":"2026-08-16T01:09:11.072038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1507.07680","last_updated":"2015-11-20T22:29:38Z","snapshot_observed_at":"2026-08-14T22:38:44.617592Z","submitted_at":"2015-07-28T08:26:50Z","title":"Training recurrent networks online without backtracking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1507.07680","snapshot_observed_at":"2026-08-16T01:09:10.073775Z","title":"Train- ing recurrent networks online without backtracking","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.073775Z"},"links":{"cited_paper":"/paper/1507.07680","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:59096878e2de6a77af1cee310ccaa57cad832375359d41f08c0647eb9880ba7a","observation_id":"a3b7fae8-0b40-45f5-94c8-09ff1d3cb160","resolution":{"observed_at":"2026-08-16T01:09:10.073775Z","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":"2026-08-16T01:09:11.050730Z","title":"Pgt: A pro- gressive method for training models on long videos","venue":null,"work_id":"3be721fe-7423-4d13-b8c1-340bd8a3a681","year":2021},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.081291Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:1fefab8b1627e68c094f1fee4c805fd3a3da721a903061ebd12370e2b2e8f951","observation_id":"68170d3f-ef20-4529-97da-101bbb62c7f5","resolution":{"observed_at":"2026-08-16T01:09:11.056955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.036358Z","title":"Learning spatiotemporal frequency-transformer for com- pressed video super-resolution","venue":null,"work_id":"c4303824-12b0-4100-a77b-b9266b96a3a8","year":2022},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.091312Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:cac1fd1e210a21b71acabb2b1588229cad463ff252bf69052734c023cabe3ffa","observation_id":"7eb13227-2439-47a2-90c4-93367bea952c","resolution":{"observed_at":"2026-08-16T01:09:11.041201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:11.021383Z","title":"Frame-recurrent video super-resolution","venue":null,"work_id":"49376ae4-c0d6-4019-aeea-be0edfb640e6","year":2018},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.099011Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:fb135f3302af5bab6337b898f9f7fa4cdd60dfa5f886ab0db1e3616f4b1fd25e","observation_id":"aea92930-82d4-4c57-a8ee-6f928ab453ca","resolution":{"observed_at":"2026-08-16T01:09:11.026010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02150","last_updated":"2019-11-06T00:19:05Z","snapshot_observed_at":"2026-07-06T08:35:01.386074Z","submitted_at":"2019-11-06T00:19:05Z","title":"Fast Transformer Decoding: One Write-Head is All You Need","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02150","snapshot_observed_at":"2026-08-16T01:09:10.105446Z","title":"Fast transformer decoding: One write-head is all you need","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.105446Z"},"links":{"cited_paper":"/paper/1911.02150","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:e53acec0cbd5507f15a941e2970c629ac643090a8000e477193a7c5f5e8f1333","observation_id":"c8efe0e3-fe82-4c9f-8dcc-c6adc28589dd","resolution":{"observed_at":"2026-08-16T01:09:10.105446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08494","last_updated":"2022-10-09T08:58:05Z","snapshot_observed_at":"2026-08-16T16:45:13.564478Z","submitted_at":"2022-07-18T10:20:23Z","title":"Rethinking Alignment in Video Super-Resolution Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.08494","snapshot_observed_at":"2026-08-16T01:09:10.113240Z","title":"Rethinking alignment in video super- resolution transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.113240Z"},"links":{"cited_paper":"/paper/2207.08494","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:750fff1c739b0d6a3e271d235e952e6d6da866e769db30d427e37e1f97660a3b","observation_id":"502c0c2d-3ea7-4549-a515-802888dc4269","resolution":{"observed_at":"2026-08-16T01:09:10.113240Z","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":"2026-08-16T01:09:10.998887Z","title":"Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","venue":null,"work_id":"09c8c334-f7f7-4ac6-9747-bf81f8fc46ea","year":2016},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.128086Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:c0f3fb16c0c5518835ad498e49523ea479d7ba2ed11de9476e9fbe8aa070f66e","observation_id":"a7313377-ef7a-47af-8b1a-41cb025ef6a3","resolution":{"observed_at":"2026-08-16T01:09:11.003640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1702.05043","last_updated":"2017-05-23T11:42:03Z","snapshot_observed_at":"2026-08-15T10:35:56.722584Z","submitted_at":"2017-02-16T16:38:08Z","title":"Unbiased Online Recurrent Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.05043","snapshot_observed_at":"2026-08-16T01:09:10.132862Z","title":"Unbiased online recurrent optimization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.132862Z"},"links":{"cited_paper":"/paper/1702.05043","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:854854f08586e7d115f7a880f46c73605d2d305f08bae5170cf63e53730ba73f","observation_id":"18b886c7-74b0-46a8-80f1-e6e533c99d27","resolution":{"observed_at":"2026-08-16T01:09:10.132862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.08209","last_updated":"2017-05-23T12:32:48Z","snapshot_observed_at":"2026-08-14T20:58:47.876564Z","submitted_at":"2017-05-23T12:32:48Z","title":"Unbiasing Truncated Backpropagation Through Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.08209","snapshot_observed_at":"2026-08-16T01:09:10.139896Z","title":"Unbiasing truncated back- propagation through time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.139896Z"},"links":{"cited_paper":"/paper/1705.08209","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:a654210931d1a514b06ed9ce5baf208dd05d9865ab7702a9e2adc8d9f0f84dbd","observation_id":"e09264e2-86ee-440e-9742-953b9d0b78ac","resolution":{"observed_at":"2026-08-16T01:09:10.139896Z","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":"2026-08-16T01:09:10.984045Z","title":"Detail-revealing deep video super-resolution","venue":null,"work_id":"5829590a-3f94-4482-bfac-3958f8dbc811","year":2017},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.144894Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:cbb62d79ac03257e464753b99f61e7952cdbb4d23638153463d0f47bf59f7c4a","observation_id":"6e851af7-51bf-4b82-9a0f-bd53387afbe6","resolution":{"observed_at":"2026-08-16T01:09:10.989735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.967984Z","title":"Tdan: Temporally-deformable alignment network for video super- resolution","venue":null,"work_id":"4e1cffe1-e22a-4208-83f6-a3a52cf7d766","year":2020},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.149450Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:e2c834a8bc0c758f5449df8d0d537e0f3ae403812b660df7ef2dca49bd0cdcad","observation_id":"9ceddd83-470f-4cb8-994b-cba04e09488c","resolution":{"observed_at":"2026-08-16T01:09:10.973969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.948952Z","title":"Edvr: Video restoration with enhanced deformable convolutional networks","venue":null,"work_id":"6bdbe609-4b76-4ad4-9059-58126f452a47","year":2019},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.155774Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:11c18566fceb54bac1207161f819993c7a2d2f873a650de634bb4c091874eac0","observation_id":"12493779-4d68-4370-80ff-60ee65387604","resolution":{"observed_at":"2026-08-16T01:09:10.954134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.161070Z","title":"Backpropagation through time: what it does and how to do it.Proceedings of the IEEE, 78(10):1550–1560,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.161070Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:071aa3d7a74c480027ed031ce61a2137486b4473052c7adb9a082bbc607bfc76","observation_id":"c2414183-3759-45d5-815d-45618d236c86","resolution":{"observed_at":"2026-08-16T01:09:10.161070Z","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":"2026-08-16T01:09:10.927536Z","title":"Gradient-based learning algorithms for recurrent networks and their computational complexity","venue":null,"work_id":"689b074e-cd5e-4ce6-b620-6bb16ff09e94","year":2013},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.166104Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:b2e8ce134a5704e09ee253457a7f713bfa51002b7050303b18a26a8e35488daa","observation_id":"96585d4b-67e1-480b-a0b6-2bb3d88a5b64","resolution":{"observed_at":"2026-08-16T01:09:10.931565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.00163","last_updated":"2024-01-18T02:10:01Z","snapshot_observed_at":"2026-08-16T15:36:50.328478Z","submitted_at":"2023-04-29T03:59:36Z","title":"Enhancing Video Super-Resolution via Implicit Resampling-based Alignment","version":2},"cited_work":{"arxiv_id":"2305.00163","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.00163","snapshot_observed_at":"2026-08-16T01:09:10.238001Z","title":"Enhancing Video Super-Resolution via Implicit Resampling-based Alignment","venue":"cs.CV","work_id":"f450593f-5d5e-4e16-ab65-562b238fc94c","year":2023},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.170206Z"},"links":{"cited_paper":"/paper/2305.00163","citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:a729c4293d9d5657fd66992195ce86ebca3773afe46ea2ddca3016819748033a","observation_id":"6ef542c7-4d59-40e6-8d48-74e8fb40fa8b","resolution":{"observed_at":"2026-08-16T01:09:10.244828Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.912043Z","title":"Video enhancement with task-oriented flow","venue":null,"work_id":"4e76b6a7-31a8-4756-9698-7c29d143684f","year":2019},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.175159Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:d6cf5b70b0d951858edf75f0f94e29a99c6f6561aad90e686e71b04f7ecee13a","observation_id":"9f2cfd6a-377c-43d8-b134-457e36cb1f92","resolution":{"observed_at":"2026-08-16T01:09:10.917216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.899360Z","title":"Video super-resolution trans- former with masked inter&intra-frame attention","venue":null,"work_id":"0a0d7414-c6fa-45c8-a922-6c6317f06f2b","year":2024},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.178591Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:8a23621da5934f08fbd682260107233374dcca3f5e23132f75f8c20637059dad","observation_id":"d9a4edc6-9e70-4876-9123-7f47ba6384f1","resolution":{"observed_at":"2026-08-16T01:09:10.903582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.883889Z","title":null,"venue":null,"work_id":"6d107927-3dfe-40b0-893a-ff2a2ee5b642","year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.182436Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:59f0fe7d3596b9f797409eabe944aace827ba0deb96f968ed83da4eaa7977d59","observation_id":"ff3accb7-5bc2-4b74-b183-dc7d756e0b72","resolution":{"observed_at":"2026-08-16T01:09:10.889189Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.870179Z","title":"It is obvious in the Fig","venue":null,"work_id":"9f693814-0c17-48a1-a4a1-82356fe2147a","year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.187150Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:2eaac80f34bddaaa400705c9a73254267ef3c828e4dcc69f577003ed2031df34","observation_id":"5e24d05d-5b17-4e72-8456-e144321c937a","resolution":{"observed_at":"2026-08-16T01:09:10.874443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.856135Z","title":"We calculate PSNR and SSIM on the RGB channel for these datatsets","venue":null,"work_id":"11ea0026-854a-441d-938b-d6d5836a656a","year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.191768Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:a0988660e11f98e64ecec29b50a6c92832329fc298154ed377786b950fd8eb48","observation_id":"52b21d61-9829-44ef-91c1-0ef1c73255b3","resolution":{"observed_at":"2026-08-16T01:09:10.861074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T01:09:10.121667Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T01:09:10.121667Z"},"links":{"citing_paper":"/paper/2505.02159"},"observation_digest":"sha256:715d049a00427412b95d6b608b58e970f614437c51bd6c09956e4329491efd58","observation_id":"9119e2d2-6041-43e5-be66-1db572910e00","resolution":{"observed_at":"2026-08-16T01:09:10.121667Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.02159","last_updated":"2025-05-04T15:46:34Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T23:08:24.711217Z","submitted_at":"2025-05-04T15:46:34Z","title":"Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":17,"verified_exact":2,"verified_fuzzy":30},"total_outbound_references":50},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2505.02159."}