{"as_of":"2026-08-04T09:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34603d30ef46d0ce45a59773f87d5c2e048bb099e24765011885e67ca0cd2837","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T10:30:26.342174Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T03:01:20.101304Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.22180","snapshot_observed_at":"2026-08-01T03:01:20.101304Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25268","last_updated":"2026-07-28T04:13:47Z","snapshot_observed_at":"2026-08-02T15:04:26.867615Z","submitted_at":"2026-07-28T04:13:47Z","title":"Structure-aware Relative Policy Optimization for Ranking","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T03:01:20.101304Z"},"links":{"cited_paper":"/paper/2604.22180","citing_paper":"/paper/2607.25268"},"observation_digest":"sha256:3869e9985f34a3ac77997f2539f537e8f7fa212cc4000ca7f159f6d55be2be40","observation_id":"111423ad-ba3a-4609-92a6-eb46f48c20dd","resolution":{"observed_at":"2026-08-01T03:01:20.101304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2604.22180/citation-record","integrity":"/paper/2604.22180/integrity","json":"/paper/2604.22180/citation-record.json","paper":"/paper/2604.22180"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2308.07107","doi":"10.48550/arxiv.2308.07107","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Large language models for information retrieval: A survey","venue":null,"work_id":"ff471f2d-dc3e-459c-81a9-9a40d29e96f8","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:b18c865d49b6b1b646631644b0507a5f3ac144448f336b0a3c12ffe3f9b31c20","observation_id":"80e148b4-ae1a-49dd-a576-668af239dd46","resolution":{"observed_at":"2026-05-11T20:01:10.854837Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-01T13:38:15.875295+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T13:38:15.875295+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09542","last_updated":"2024-12-28T06:20:54Z","snapshot_observed_at":"2026-07-06T15:17:25.246628Z","submitted_at":"2023-04-19T10:16:03Z","title":"Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents","version":3},"cited_work":{"arxiv_id":"2304.09542","doi":"10.48550/arxiv.2304.09542","metadata_source":"pith","pith_arxiv_id":"2304.09542","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"[Online; accessed 2025-07-26]","venue":"cs.CL","work_id":"f641f433-ce28-4c6b-bce6-4f77e0d21705","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2304.09542","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:9f184b071aa88403ae5fa318dcea8358fccd4a5a24870bf1682b7cbc24be8c09","observation_id":"c6beaeae-e01d-4de1-834f-79234aa2a499","resolution":{"observed_at":"2026-05-11T20:01:10.868081Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15088","last_updated":"2023-09-26T17:31:57Z","snapshot_observed_at":"2026-07-06T16:23:57.143116Z","submitted_at":"2023-09-26T17:31:57Z","title":"RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models","version":1},"cited_work":{"arxiv_id":"2309.15088","doi":"10.48550/arxiv.2309.15088","metadata_source":"pith","pith_arxiv_id":"2309.15088","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Rankvicuna: Zero-shot listwise doc- ument reranking with open-source large language models","venue":"cs.IR","work_id":"6196b66d-d87d-460e-bd36-fa58f800ca9b","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2309.15088","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:4c8870779e8d8900f7ae19ae7aa1ae47582f4ad4dbb090e6fcd0ab3e122f74a6","observation_id":"c331bf65-2146-4cbd-88f8-aa79cee6250b","resolution":{"observed_at":"2026-05-11T20:01:10.884174Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Lost in the middle: How language models use long contexts","venue":null,"work_id":"bd32eb38-34ef-40ef-a703-1844610338aa","year":2024},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:1b8c28cb7bedec8240c1bee15ba82bda3814250d6c787a73c42508b380b4e7ab","observation_id":"f680b3c2-6f4d-4a57-aa85-177d0e7e2a07","resolution":{"observed_at":"2026-05-26T15:07:52.314659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.14574","last_updated":"2024-12-19T06:44:59Z","snapshot_observed_at":"2026-07-06T20:09:47.427018Z","submitted_at":"2024-12-19T06:44:59Z","title":"Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models","version":1},"cited_work":{"arxiv_id":"2412.14574","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.14574","snapshot_observed_at":"2026-06-29T10:33:18.842267Z","title":"Sliding windows are not the end: Exploring full rank- ing with long-context large language models","venue":null,"work_id":"0865d1aa-9d73-4449-b2b8-6b7460651097","year":2024},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2412.14574","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:c44660410f04c706345448dc2f95a315cc55fd5ca55253b792e65e1932140529","observation_id":"b06685a4-647a-4f1f-8a59-6cd1385faff6","resolution":{"observed_at":"2026-05-11T20:01:10.889588Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Leveraging passage embeddings for efficient listwise reranking with large language models","venue":null,"work_id":"9b5a74da-7edf-4a2a-9d95-a1573a372dc3","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:e9adae0b5f295acd16240971823b1ed9635aaa13d3d6d0e4f5cb311a7f36cb81","observation_id":"f85d3fc7-d2e1-44eb-b352-571ed7c0ce48","resolution":{"observed_at":"2026-05-26T15:07:52.307113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Visual instruction tuning","venue":null,"work_id":"0bc24149-ab5d-44c5-a51b-1f5b1de973a8","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:57fb035fb11297eae269bf39944b3c592e34eabde71acef40c0a0225607b2175","observation_id":"197a7639-ea11-42b0-abcb-99b7314d0e40","resolution":{"observed_at":"2026-05-26T15:07:52.309719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Compress-then-Rank:Fasterandbetterlistwisererankingwith large language models via ranking-aware passage compression","venue":null,"work_id":"5fd39e96-3311-4194-ace9-49991dbef055","year":2026},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:0027c4bcaaf0a54a897a4af9ea1cd4da58945aa1dee6a0b076916d0b020e8774","observation_id":"32d51cfe-f3e1-4a6f-964e-052717b91dc5","resolution":{"observed_at":"2026-05-26T15:07:52.304112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.22733","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"E2Rank: Your text embedding can also be an effective and efficient listwise reranker","venue":null,"work_id":"9071e345-36e9-4258-b9a2-31faf0577abe","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:1abde70c6e898a129dc42f6cf03a024113de828ed92759219af8db0e92cb2b3c","observation_id":"8d93fc7b-d7b4-448b-9824-799d58c85c6a","resolution":{"observed_at":"2026-05-11T20:01:10.874471Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Generatingdiversecriteriaon-the-flytoimprove pointwise LLM rankers","venue":null,"work_id":"c25e8323-382b-4307-b635-3b0da9c0d968","year":2024},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:5c42ebd2cfdf7feb66add1a782eed0878d0a1ce04e781e687d618d5e87095f28","observation_id":"d216e89d-f2be-46dc-a2ec-e3f1db2c0093","resolution":{"observed_at":"2026-05-26T15:07:52.312121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.17563","last_updated":"2024-03-28T13:59:09Z","snapshot_observed_at":"2026-07-06T15:48:44.267376Z","submitted_at":"2023-06-30T11:32:25Z","title":"Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting","version":2},"cited_work":{"arxiv_id":"2306.17563","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.17563","snapshot_observed_at":"2026-07-08T20:05:34.193004Z","title":"Large language models are effective text rankers with pairwise ranking prompting.arXiv preprint arXiv:2306.17563","venue":"cs.IR","work_id":"c16860aa-2f43-4a94-9a6a-974cd8bc64f6","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2306.17563","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:903a561a9098108779dc00fb7fd9c88ca31a3debe995b914a6fa53326a07e197","observation_id":"1a68bf68-c87c-4616-b321-eacd25c0a33a","resolution":{"observed_at":"2026-05-11T20:01:10.897920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.14424","last_updated":"2019-10-31T12:45:40Z","snapshot_observed_at":"2026-07-06T08:33:43.198398Z","submitted_at":"2019-10-31T12:45:40Z","title":"Multi-Stage Document Ranking with BERT","version":1},"cited_work":{"arxiv_id":"1910.14424","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.14424","snapshot_observed_at":"2026-07-08T20:05:34.190658Z","title":"Multi-stage document ranking with bert","venue":"cs.IR","work_id":"59ce8c86-2bcd-46a9-a49e-402c4efef412","year":2019},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/1910.14424","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:c168d63e5a92e6a2c53d1c235f29b6e633f8fb1988354cf4d6aa5d6cb15919b5","observation_id":"b6fca413-a255-4d93-b4da-b5336d5e21f1","resolution":{"observed_at":"2026-05-11T20:01:10.849655Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.06713","last_updated":"2020-03-14T22:29:50Z","snapshot_observed_at":"2026-08-04T07:59:15.888451Z","submitted_at":"2020-03-14T22:29:50Z","title":"Document Ranking with a Pretrained Sequence-to-Sequence Model","version":1},"cited_work":{"arxiv_id":"2003.06713","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.06713","snapshot_observed_at":"2026-07-02T02:46:29.381896Z","title":"Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin","venue":null,"work_id":"c1fe8c8d-c400-470d-8075-998a546cf680","year":2003},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2003.06713","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:065af8f50c36cd5e08ab059162558d4d3e9b6d775aab69239859002e151fea85","observation_id":"a5f69255-79f4-4e16-9a87-c5d04387f4cc","resolution":{"observed_at":"2026-05-11T20:01:10.862560Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02724","last_updated":"2023-12-05T12:39:00Z","snapshot_observed_at":"2026-07-06T16:57:09.664139Z","submitted_at":"2023-12-05T12:39:00Z","title":"RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!","version":1},"cited_work":{"arxiv_id":"2312.02724","doi":"10.1609/aaai.v37i8.26128","metadata_source":"pith","pith_arxiv_id":"2312.02724","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!","venue":"cs.IR","work_id":"0d9b3ad1-b405-412f-81ee-fd6f941d2367","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2312.02724","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:626a1951340d3d208d00aee2442800719ab1975c8bf4bf5e1ed93ea27cbf57be","observation_id":"b2368ce5-2e98-4c8b-819f-9992b825deb6","resolution":{"observed_at":"2026-05-15T23:40:11.276075Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"ListT5: Listwise reranking with fusion-in-decoder improves zero-shot retrieval","venue":null,"work_id":"51601b74-d191-4bee-9bc2-93836d348446","year":2024},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:acd3180cb5d782adc529bca29c1990b0e0ae54670b175cb7f18d9c37c9a76485","observation_id":"a8563273-580c-4db9-add4-f8a2c30d8daa","resolution":{"observed_at":"2026-05-26T15:07:52.290943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"TourRank:Utilizinglargelanguagemodels for document ranking with a tournament-inspired strategy","venue":null,"work_id":"6905a7f9-2a9d-44fc-85b3-9c76422c9029","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:e25c0f01c74b89a17268badcf86ea147fb73bdf5e10492fa4ea029f2492ca736","observation_id":"b0a0a303-3404-47b6-8924-08e4f6ac8859","resolution":{"observed_at":"2026-05-26T15:07:52.293244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"DiffuRank: Effective document reranking with diffusion language models","venue":null,"work_id":"aa42cd03-4faa-4040-8c71-f795c31e2a6d","year":2026},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:df0ee29882af55c4e72227de9d3bb38e19e1a76b0608f8dc83871d9206d38a18","observation_id":"d91b5cf6-6246-48c4-ba58-8943f5ea17ea","resolution":{"observed_at":"2026-05-26T15:07:52.298134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"jina-reranker-v3: Last but not late interaction for listwise document reranking","venue":null,"work_id":"0c3961d3-f63d-41c7-9cd7-c11afc6526de","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:a3052696019f1b0ec69c5dc622cf6c57f3bca148481097ea8cc3de42b336020a","observation_id":"db559cc5-16d4-488c-a518-077234f5b232","resolution":{"observed_at":"2026-05-26T15:07:52.279653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.12819","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Com- pLLM: Compression for long context Q&A","venue":null,"work_id":"bf721238-dc09-49b3-ba7b-20a8fbce4538","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:c633653c6148f4b47dafae50af03a894b6e13d4f763f6c642a693587b6064f79","observation_id":"cb2f225e-562b-43b2-94dc-d14954e224f1","resolution":{"observed_at":"2026-05-11T20:01:10.845242Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14587","last_updated":"2024-01-02T07:22:04Z","snapshot_observed_at":"2026-08-03T08:34:52.414842Z","submitted_at":"2023-10-23T05:52:09Z","title":"Large Search Model: Redefining Search Stack in the Era of LLMs","version":2},"cited_work":{"arxiv_id":"2310.14587","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.14587","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Large search model: Redefining search stack in the era of LLMs","venue":null,"work_id":"bb7eaba4-647d-4e99-aa37-85a45e90a31f","year":2023},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2310.14587","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:9a4dc5b24ca082391df1f6297a6a12b0fa3fc47a76bbe18104f2f46ad2785677","observation_id":"3f7313e1-ce51-455d-a59a-7075b3891f23","resolution":{"observed_at":"2026-05-11T20:01:10.840611Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18965","last_updated":"2025-02-26T09:25:10Z","snapshot_observed_at":"2026-07-06T20:42:55.911327Z","submitted_at":"2025-02-26T09:25:10Z","title":"OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment","version":1},"cited_work":{"arxiv_id":"2502.18965","doi":"10.48550/arxiv.2502.18965","metadata_source":"pith","pith_arxiv_id":"2502.18965","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment","venue":"cs.IR","work_id":"d1a07d92-e045-4af2-a79f-c7b0112cf824","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2502.18965","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:7cb93b78d7e66e0ae1493448d13ab2d0d3c4a76b892ceba904bdfb7f4d097ef3","observation_id":"1690c5ee-4c9c-4bf7-bcb9-3f7117f65db0","resolution":{"observed_at":"2026-05-12T18:30:36.120082Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-25T20:53:23.201732+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T20:53:23.201732+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.07860","last_updated":"2025-09-09T15:40:23Z","snapshot_observed_at":"2026-07-06T22:27:10.808353Z","submitted_at":"2025-09-09T15:40:23Z","title":"KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis","version":1},"cited_work":{"arxiv_id":"2509.07860","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.07860","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"UniSearch: Rethinking search system with a unified generative architecture","venue":null,"work_id":"3e9cf76d-f0f6-401a-8030-d5ddd656b1a1","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2509.07860","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:2471894eb6b2d759d334a5751f0d3289b10dcd0c949db7ba7fa906dea9c8856c","observation_id":"64e45d81-a188-4efa-a9c2-3593406bf4f0","resolution":{"observed_at":"2026-05-11T20:01:10.813939Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12740","last_updated":"2024-09-19T13:03:07Z","snapshot_observed_at":"2026-07-06T19:18:08.790424Z","submitted_at":"2024-09-19T13:03:07Z","title":"HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling","version":1},"cited_work":{"arxiv_id":"2409.12740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12740","snapshot_observed_at":"2026-07-01T17:25:51.255113Z","title":"Hllm: Enhancing sequential recom- mendations via hierarchical large language models for item and user modeling","venue":null,"work_id":"499540e4-4ba3-49f5-a144-6443fb4dbc1c","year":2024},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2409.12740","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:f08a65a62a2d8a293d2bac82ff2eff1f2690b5ff6c2b7db14e4ba515f43c3fa7","observation_id":"aab7f71f-d7c5-4081-98dd-d977959f4699","resolution":{"observed_at":"2026-05-11T20:01:10.834350Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.07367","last_updated":"2023-04-23T16:56:52Z","snapshot_observed_at":"2026-07-06T11:57:52.155266Z","submitted_at":"2021-10-14T13:52:55Z","title":"RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking","version":2},"cited_work":{"arxiv_id":"2110.07367","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.07367","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2110.07367 , year=","venue":null,"work_id":"6c069fe5-a6d2-456f-b127-50a82198ded6","year":2021},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2110.07367","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:eb8b28874677633f0c4021b4cdbef4f18d54a08280a1f2d6fbc44d8d8965cbcb","observation_id":"8d4eb112-70a1-46cc-9a9e-0d7a1a540a63","resolution":{"observed_at":"2026-05-11T20:01:10.775550Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.07820","last_updated":"2020-03-18T16:56:56Z","snapshot_observed_at":"2026-07-06T09:05:23.015957Z","submitted_at":"2020-03-17T17:12:36Z","title":"Overview of the TREC 2019 deep learning track","version":2},"cited_work":{"arxiv_id":"2003.07820","doi":"10.48550/arxiv.2003.07820","metadata_source":"arxiv_reference","pith_arxiv_id":"2003.07820","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Voorhees","venue":null,"work_id":"083b288a-95a1-4846-959c-e69b87d8885c","year":2019},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2003.07820","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:8ef0d3ddbf97c3e810173bddc698184bb3320dbb33b2a617f098aee6aacc3a12","observation_id":"8583e36c-a54a-4ee9-b0aa-9a2280c2332c","resolution":{"observed_at":"2026-05-11T20:01:10.756970Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.07662","last_updated":"2021-02-15T16:47:00Z","snapshot_observed_at":"2026-07-06T10:41:31.563193Z","submitted_at":"2021-02-15T16:47:00Z","title":"Overview of the TREC 2020 deep learning track","version":1},"cited_work":{"arxiv_id":"2102.07662","doi":"10.48550/arxiv.2102.07662","metadata_source":"arxiv_reference","pith_arxiv_id":"2102.07662","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M","venue":null,"work_id":"08c56b45-a87e-4374-9a23-6404c928b6ea","year":2021},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2102.07662","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:d2f6272f6c01e8b57072c3143a52b27e33f8d7dca1f603f1e9dda48700a14e64","observation_id":"e8179422-b234-4979-a60c-aebfd0ee5a60","resolution":{"observed_at":"2026-05-11T20:01:10.782839Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08663","last_updated":"2021-10-21T01:18:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-04-17T23:29:55Z","title":"BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models","version":4},"cited_work":{"arxiv_id":"2104.08663","doi":"10.48550/arxiv.2104.08663","metadata_source":"pith","pith_arxiv_id":"2104.08663","snapshot_observed_at":"2026-07-11T00:47:43.432590Z","title":"BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models","venue":"cs.IR","work_id":"c5f7f027-ac36-4b07-b824-0eca2f310641","year":2021},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2104.08663","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:129893cc4ced73356aa1d2d9bf5bb4a7f37962636210149734e413d778c1b900","observation_id":"bd71a659-9951-406b-bd8a-e266a69340dc","resolution":{"observed_at":"2026-05-12T14:42:11.637914Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-06-02T00:57:45.853409+00:00","source":"crossref_status_cache"},{"observed_at":"2026-06-02T00:57:45.853409+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":"2505.09388","doi":"10.1016/j.aiopen.2022.12","metadata_source":"pith","pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Technical Report","venue":"cs.CL","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:779cfb6ab2e96d2a5fbe6577d90948daf88db387e065d4459d337fb52de0090d","observation_id":"6364fd23-4256-48e9-abca-07ec9c29da68","resolution":{"observed_at":"2026-05-11T20:01:10.797136Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.05176","last_updated":"2025-06-11T02:54:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-05T15:49:48Z","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","version":3},"cited_work":{"arxiv_id":"2506.05176","doi":"10.1016/j.displa.2025.103255","metadata_source":"pith","pith_arxiv_id":"2506.05176","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","venue":"cs.CL","work_id":"bab684a8-d933-426c-a19e-2c855a0d1f59","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2506.05176","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:22de9b60ec73c01a12344890dcee3c59b6cc50f50458cbca0960966448fb660a","observation_id":"379931ca-55d9-470c-a432-a166065e6ae2","resolution":{"observed_at":"2026-05-11T20:01:10.770476Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"FlashAttention: Fast and memory-efficient exact attention with IO-awareness","venue":null,"work_id":"e70d6b77-3041-463c-87e8-bf6383cb7474","year":2022},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:7048960b9848bb9d4a61ce2de1ab6e511fec07a8278d7fb9539b527ed2bf273a","observation_id":"3c1d32a8-4050-4683-a1b6-2c4e73d3b96a","resolution":{"observed_at":"2026-05-26T15:07:52.288263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters","venue":null,"work_id":"5200ccde-0f72-41e7-8e19-ebf44d1788e6","year":2020},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:57277cc0658ce33e42fe568c8f1b3ffa95c70a6e524c7f5eeb40eea1b5231398","observation_id":"dac0f600-4d4b-4ee3-a336-741de4f5367c","resolution":{"observed_at":"2026-05-26T15:07:52.300988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Reciprocal rank fusion outperforms condorcet and individual rank learning methods","venue":null,"work_id":"d7493c37-57a0-4527-93e3-845c81e9884b","year":2009},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:12d60690fcc7b2c2a689751162a5ff918af898741e545333b36b87925731d631","observation_id":"ebb931a8-2fab-4bdd-a2f0-b743cf7f22f9","resolution":{"observed_at":"2026-05-26T15:07:52.282448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.00968","last_updated":"2026-06-28T13:51:33Z","snapshot_observed_at":"2026-08-04T00:37:22.994758Z","submitted_at":"2025-11-02T15:16:21Z","title":"The adiabatic theorem for non-Hermitian quantum systems with real eigenvalues and the complex geometric phase","version":3},"cited_work":{"arxiv_id":"2511.00968","doi":null,"metadata_source":"pith","pith_arxiv_id":"2511.00968","snapshot_observed_at":"2026-07-10T17:47:25.379535Z","title":"Optimizing generative ranking relevance via reinforcement learning in Xiaohongshu search","venue":"quant-ph","work_id":"7b57fc0d-6420-429b-b686-03c4a2f6e200","year":2025},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"cited_paper":"/paper/2511.00968","citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:dcb32be026d60d7ae9178b0cd7a5cf9d434921e65bbb79492628d20e96342762","observation_id":"42445113-571f-4195-bbfe-eb021abef6be","resolution":{"observed_at":"2026-06-30T02:16:10.847702Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Learning to rank using gra- dient descent","venue":null,"work_id":"91acfde9-ddd1-425e-9dc3-005ba7968a52","year":2005},"citing_paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T10:30:26.342174Z"},"links":{"citing_paper":"/paper/2604.22180"},"observation_digest":"sha256:394ccdff4f2033fc1aabdf43709ad1436a86819f08e2e62ea8a62ce9b29516a7","observation_id":"c35d5b56-fb94-469d-b3c6-23a56abaf654","resolution":{"observed_at":"2026-05-26T15:07:52.285424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.22180","last_updated":"2026-04-24T03:11:51Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-02T10:18:37.084375Z","submitted_at":"2026-04-24T03:11:51Z","title":"ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":0,"verified_exact":16,"verified_fuzzy":13},"total_outbound_references":34},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2604.22180."}