{"as_of":"2026-08-08T05:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:93809d6907757ad66476a86e7508a4983a618f1d907a5088fd98d54308164df4","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:29.668279Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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":"2026-08-07T05:18:27.053502Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T05:18:38.588554Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"cited_work":{"arxiv_id":"2505.24581","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.24581","snapshot_observed_at":"2026-08-07T05:18:38.588554Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","venue":"cs.CL","work_id":"7f20e48e-3a12-40ba-bfaf-31ca6374a946","year":2025},"citing_paper":{"arxiv_id":"2506.08354","last_updated":"2026-05-28T08:13:47Z","snapshot_observed_at":"2026-08-07T05:10:51.150588Z","submitted_at":"2025-06-10T02:11:42Z","title":"Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T05:18:27.053502Z"},"links":{"cited_paper":"/paper/2505.24581","citing_paper":"/paper/2506.08354"},"observation_digest":"sha256:40cefed5e33805d79ee11b4a1ff185c3fbab28d47161093d8042fcabf17cdb3d","observation_id":"f18f8b50-dae1-41a5-90ae-e7ab7f4eaeb7","resolution":{"observed_at":"2026-08-07T05:18:38.695960Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.24581/citation-record","integrity":"/paper/2505.24581/integrity","json":"/paper/2505.24581/citation-record.json","paper":"/paper/2505.24581"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:25.520888Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.520888Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:db79dab83570ad22a3b52d8cf3defd0174924e7df1c7bd730221df1fe10968bb","observation_id":"b63ba18b-4f44-4871-aed8-7a3f72c52ce0","resolution":{"observed_at":"2026-08-07T12:35:25.520888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:25.623757Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.623757Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:90c13c8158ed2d04502ea5a7f9f2fe01662e4f5c4efb2182dd9738c82a9e57a8","observation_id":"6dbe8c49-6771-48df-8d8f-041f49015874","resolution":{"observed_at":"2026-08-07T12:35:25.623757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.01785","last_updated":"2021-06-07T20:39:46Z","snapshot_observed_at":"2026-08-07T22:25:23.463110Z","submitted_at":"2020-12-27T06:32:55Z","title":"ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.01785","snapshot_observed_at":"2026-08-07T12:35:25.759074Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.759074Z"},"links":{"cited_paper":"/paper/2101.01785","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:90c6945f1157c6b6adc27fa8d29955f0ba910c1bbd932f5ff834836a579090b5","observation_id":"d386f626-d37c-45a1-b908-83f141b0ed3a","resolution":{"observed_at":"2026-08-07T12:35:25.759074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.00104","last_updated":"2021-03-07T13:37:01Z","snapshot_observed_at":"2026-07-06T09:01:07.016780Z","submitted_at":"2020-02-28T22:59:24Z","title":"AraBERT: Transformer-based Model for Arabic Language Understanding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.00104","snapshot_observed_at":"2026-08-07T12:35:25.879467Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.879467Z"},"links":{"cited_paper":"/paper/2003.00104","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:9486034ac0a5704373ab85210cb21eaf5283f8996ed1d02fa4c2da96f7ccb4b1","observation_id":"45adb5d9-7dcd-4793-bab4-ff662cb10ae1","resolution":{"observed_at":"2026-08-07T12:35:25.879467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:25.983764Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.983764Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:4940a86c08c334ae63c85269e73f44be14a55e15b80bfca1748165c99a7aa821","observation_id":"e29f2276-18d2-4503-8405-f435c7837671","resolution":{"observed_at":"2026-08-07T12:35:25.983764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:32.511322Z","title":null,"venue":null,"work_id":"5b57643a-6e6f-494d-9a4f-27331196ab62","year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.080352Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:ec70a66821bd68b22638190dfa5ea13105e86ae75698100a21a1909e87f19122","observation_id":"d84f0ab6-2eaa-462e-8361-7a6c6732baae","resolution":{"observed_at":"2026-08-07T12:35:32.567841Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1508.05326","last_updated":"2015-08-21T16:17:01Z","snapshot_observed_at":"2026-08-05T22:08:24.004344Z","submitted_at":"2015-08-21T16:17:01Z","title":"A large annotated corpus for learning natural language inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.05326","snapshot_observed_at":"2026-08-07T12:35:26.170504Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.170504Z"},"links":{"cited_paper":"/paper/1508.05326","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:9ff628f5e7b484f8e50b153f416aebc8ecd93726aa36a216391f3cf808cbbca8","observation_id":"a2f9c59a-3418-4370-943e-d7247610f38a","resolution":{"observed_at":"2026-08-07T12:35:26.170504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00055","last_updated":"2017-07-31T20:12:06Z","snapshot_observed_at":"2026-08-04T03:50:51.013049Z","submitted_at":"2017-07-31T20:12:06Z","title":"SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00055","snapshot_observed_at":"2026-08-07T12:35:26.274555Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.274555Z"},"links":{"cited_paper":"/paper/1708.00055","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:8714150bf14959bd060fdc6b66806140cd43648aa4ef6f5b9c80c9af2009acd3","observation_id":"bbd9458d-3bc5-48bd-bb00-750de693d94d","resolution":{"observed_at":"2026-08-07T12:35:26.274555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.01852","last_updated":"2022-03-08T05:10:16Z","snapshot_observed_at":"2026-07-06T09:35:14.132350Z","submitted_at":"2020-07-03T17:58:42Z","title":"Language-agnostic BERT Sentence Embedding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.01852","snapshot_observed_at":"2026-08-07T12:35:26.380704Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.380704Z"},"links":{"cited_paper":"/paper/2007.01852","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:b335cb1a00eded2d33d52b870a6a5110b728f17054ed7016297b4cb08285b7b7","observation_id":"9050cce2-9f16-4f08-b7bf-59826f498d1c","resolution":{"observed_at":"2026-08-07T12:35:26.380704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08821","last_updated":"2022-05-18T12:29:49Z","snapshot_observed_at":"2026-07-06T11:01:05.577957Z","submitted_at":"2021-04-18T11:27:08Z","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08821","snapshot_observed_at":"2026-08-07T12:35:26.480075Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.480075Z"},"links":{"cited_paper":"/paper/2104.08821","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:ec482228e3eede70726b3e2cc4dc5a873370ea67a0d4cff0aeb7addabbc63b88","observation_id":"8a44c536-7efa-4833-bcde-755a66688863","resolution":{"observed_at":"2026-08-07T12:35:26.480075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-07T12:35:26.555688Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.555688Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:7518063656a52e49bb6b03d8dc66dc991ed2f3e383cee353781b1582df9d7eef","observation_id":"0b1d2c42-bb0b-4475-ba47-ab75f723d52f","resolution":{"observed_at":"2026-08-07T12:35:26.555688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:26.639613Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.639613Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:8575f89de68f6456cf1bd98328ca55c7bee4483b8fb896c529714d2dc4ff4ed2","observation_id":"b0e0277d-8e59-4467-a662-626319991e2b","resolution":{"observed_at":"2026-08-07T12:35:26.639613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:26.775688Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.775688Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:54c780ff4acde7e0b8ff0d9cd5566ac14f2f9870af2c5eb7a55839f5c647a7d2","observation_id":"d9fa46bb-ce73-4277-8b45-cd201b05110d","resolution":{"observed_at":"2026-08-07T12:35:26.775688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06932","last_updated":"2024-05-11T06:32:08Z","snapshot_observed_at":"2026-07-06T18:12:55.888082Z","submitted_at":"2024-05-11T06:32:08Z","title":"Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06932","snapshot_observed_at":"2026-08-07T12:35:26.913944Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.913944Z"},"links":{"cited_paper":"/paper/2405.06932","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:4d1ea8c877ead3840bed477a9f6b3a3db812349f30622905d4dae2404f5f9d25","observation_id":"59831827-e4c3-4963-80fb-fac5d475dbdd","resolution":{"observed_at":"2026-08-07T12:35:26.913944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:32.306766Z","title":null,"venue":null,"work_id":"955da10e-7212-4457-bc89-57b057bdf33e","year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.979784Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:a708894549b4faeb372c36a9868138a921e76e895c5be7e422dd1d3372a5234a","observation_id":"97b27d4f-421c-4e5d-a9f8-9122d80271f4","resolution":{"observed_at":"2026-08-07T12:35:32.385534Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:32.090326Z","title":null,"venue":null,"work_id":"60fa7cac-8d92-49b4-9085-5e325abe519a","year":2019},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.081430Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:2a4f66465b6cdfa2e2b8bb8dffeb3a704f4ce398204fadd28060a7746a16ea2a","observation_id":"8e254a33-d0a7-433f-844e-fc0344f710e3","resolution":{"observed_at":"2026-08-07T12:35:32.200693Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.02810","last_updated":"2017-03-06T15:54:27Z","snapshot_observed_at":"2026-08-07T04:25:02.147651Z","submitted_at":"2017-01-10T23:32:43Z","title":"OpenNMT: Open-Source Toolkit for Neural Machine Translation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.02810","snapshot_observed_at":"2026-08-07T12:35:27.191811Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.191811Z"},"links":{"cited_paper":"/paper/1701.02810","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:8c6633b3bd050d2321801f2a690fa84dcf854224fb8dd6d5e9dbab5cd02f4f70","observation_id":"4b189ae3-3f53-4c22-8a50-4c5d7ba1210a","resolution":{"observed_at":"2026-08-07T12:35:27.191811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:31.816924Z","title":null,"venue":null,"work_id":"4057efe6-ac15-433d-99f6-2ef894fa8300","year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.322905Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:3e52e04393e519ff744b3f8149f05490ab833e20187346bbaaa664b4e4723916","observation_id":"11ec7105-350a-486c-81c8-edde8cb19f14","resolution":{"observed_at":"2026-08-07T12:35:31.950862Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:27.402640Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.402640Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:dbc61856f22c80d4534d94ee3ca243fab0a90adc30967061d670fd19e99aa2fd","observation_id":"953ef8af-0fc9-4aac-8d68-499229f60beb","resolution":{"observed_at":"2026-08-07T12:35:27.402640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20327","last_updated":"2024-03-29T17:56:40Z","snapshot_observed_at":"2026-07-06T17:53:11.000124Z","submitted_at":"2024-03-29T17:56:40Z","title":"Gecko: Versatile Text Embeddings Distilled from Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.20327","snapshot_observed_at":"2026-08-07T12:35:27.507654Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.507654Z"},"links":{"cited_paper":"/paper/2403.20327","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:58b432b6a92d5dadcc4ed2fc31a175a7a6f68d6083c1d2d1ef04498da9802d5f","observation_id":"cec8c085-3b02-44dc-aa72-3acd42546df6","resolution":{"observed_at":"2026-08-07T12:35:27.507654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:31.641483Z","title":null,"venue":null,"work_id":"ec9e766f-89c4-455f-a59d-9cd88d9e46e1","year":2018},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.660000Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:3ab53e773bb5cff559b63850411163650d89dab1a237d1457ed83de052b6fdd0","observation_id":"82377bef-aa63-4978-8ee4-3ef9f4ac7e5c","resolution":{"observed_at":"2026-08-07T12:35:31.714769Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03281","last_updated":"2023-08-07T03:52:59Z","snapshot_observed_at":"2026-08-04T23:10:23.964516Z","submitted_at":"2023-08-07T03:52:59Z","title":"Towards General Text Embeddings with Multi-stage Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03281","snapshot_observed_at":"2026-08-07T12:35:27.765317Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.765317Z"},"links":{"cited_paper":"/paper/2308.03281","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:c43c2daf1c69839f3be69911ec8d4e9efb4d52dd15ba123526638f6ed0e96f87","observation_id":"8451957f-ce65-443c-85c0-6fc53fc9880a","resolution":{"observed_at":"2026-08-07T12:35:27.765317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:31.499735Z","title":null,"venue":null,"work_id":"560fe917-38e8-4adf-bfb4-239c54b6a695","year":2022},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.877061Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:df1320fd11a9057383045da9ed21014506cff2c8e72499bdaaac3f829812a579","observation_id":"69c49024-0354-48d1-84b9-5cc3e64d7135","resolution":{"observed_at":"2026-08-07T12:35:31.586109Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:31.357429Z","title":null,"venue":null,"work_id":"9a92a114-effd-4dcc-bd1f-992e83aaaf90","year":2008},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.992913Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:d4def0eb9da7ef6b572a80e172190cf876ca10098cbbb50e64dd20df36c3147e","observation_id":"2303df87-904d-4110-b6c9-d1cbe6a0c851","resolution":{"observed_at":"2026-08-07T12:35:31.416949Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07316","last_updated":"2023-03-19T13:37:01Z","snapshot_observed_at":"2026-07-06T14:04:58.943383Z","submitted_at":"2022-10-13T19:42:08Z","title":"MTEB: Massive Text Embedding Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07316","snapshot_observed_at":"2026-08-07T12:35:28.145836Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.145836Z"},"links":{"cited_paper":"/paper/2210.07316","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:4e5a266217c2213f40bf047ee1669deafedd7051ccdc84ff8dc52a5f3e111537","observation_id":"8fd32607-c994-42e7-8f91-7181600f40ee","resolution":{"observed_at":"2026-08-07T12:35:28.145836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21139","last_updated":"2024-08-01T12:24:01Z","snapshot_observed_at":"2026-08-03T12:06:26.529845Z","submitted_at":"2024-07-30T19:03:03Z","title":"Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning","version":2},"cited_work":{"arxiv_id":"2407.21139","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.21139","snapshot_observed_at":"2026-08-07T12:35:29.932012Z","title":"Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning","venue":"cs.CL","work_id":"fda601cc-c24c-4501-90ce-4214301de177","year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.262788Z"},"links":{"cited_paper":"/paper/2407.21139","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:09fa11442d4cef5d495e11511c53bc35f98e52940ef3531e48229c23303c1761","observation_id":"06f065e8-4ffc-452e-8530-5d13f669928f","resolution":{"observed_at":"2026-08-07T12:35:29.975498Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:31.186034Z","title":null,"venue":null,"work_id":"2b5adb81-c0d4-4c6e-a5ab-69344eb3bdef","year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.402703Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:861c91d033ca9ce87a6b667d80efb8f3b4a74c00d67cbbf4210bde0157d5137f","observation_id":"969fcc42-03c9-413b-bc73-9aa1c34e8bd7","resolution":{"observed_at":"2026-08-07T12:35:31.252865Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:31.026065Z","title":null,"venue":null,"work_id":"4bd809b2-4074-4cb4-82f8-4e1488ee5792","year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.494740Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:65e6771747eacbe87186f0738740ef468770ae7d0d73a8a442772b991a610c56","observation_id":"41d352ec-14bd-4f65-ae00-0e30699c52c6","resolution":{"observed_at":"2026-08-07T12:35:31.106034Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:30.854068Z","title":null,"venue":null,"work_id":"ac30cb70-18ef-4387-96ac-52b26a0bad8d","year":2019},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.600177Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:f26a71602598d531edc0ccad74799f8cf8c5645d890c80abb4ed8c47f1e98c61","observation_id":"dabe56c7-4b5d-4094-a1ff-1ae0e9937825","resolution":{"observed_at":"2026-08-07T12:35:30.934204Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:28.706206Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.706206Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:1d9fb70e051298f98c04234d4259f77b1ba5c3fe7a8498543d9ce610defd11a5","observation_id":"13dcbc48-1fa3-47d3-bacf-bf1d492a08cc","resolution":{"observed_at":"2026-08-07T12:35:28.706206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10084","last_updated":"2019-08-27T08:50:17Z","snapshot_observed_at":"2026-07-06T08:17:05.681370Z","submitted_at":"2019-08-27T08:50:17Z","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10084","snapshot_observed_at":"2026-08-07T12:35:28.896982Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.896982Z"},"links":{"cited_paper":"/paper/1908.10084","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:ad57a178cc32fae54534e46ffdf9643e26f72722be50e5c4831679b3b28a4ff7","observation_id":"b5efa2d6-496f-47cf-ae7a-9e7e3fdb3cb2","resolution":{"observed_at":"2026-08-07T12:35:28.896982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.03533","last_updated":"2024-02-22T06:21:51Z","snapshot_observed_at":"2026-07-06T14:27:46.217000Z","submitted_at":"2022-12-07T09:25:54Z","title":"Text Embeddings by Weakly-Supervised Contrastive Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.03533","snapshot_observed_at":"2026-08-07T12:35:28.984259Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.984259Z"},"links":{"cited_paper":"/paper/2212.03533","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:be79d786f19057daa469f0655e09424196f240b4c5394b8e36efb6103c742ad5","observation_id":"106f866e-05e1-4370-9e16-00e724a1f90e","resolution":{"observed_at":"2026-08-07T12:35:28.984259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00368","last_updated":"2024-05-31T07:22:01Z","snapshot_observed_at":"2026-08-06T17:51:59.068535Z","submitted_at":"2023-12-31T02:13:18Z","title":"Improving Text Embeddings with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00368","snapshot_observed_at":"2026-08-07T12:35:29.071102Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.071102Z"},"links":{"cited_paper":"/paper/2401.00368","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:297b4bd21bb06acca3ec9887d6b39ca28cc7261c82bf5aa2818266c29c278d12","observation_id":"8b16a745-153f-4fd5-8d85-1b8233d59019","resolution":{"observed_at":"2026-08-07T12:35:29.071102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05672","last_updated":"2024-02-08T13:47:50Z","snapshot_observed_at":"2026-07-30T09:46:51.062184Z","submitted_at":"2024-02-08T13:47:50Z","title":"Multilingual E5 Text Embeddings: A Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05672","snapshot_observed_at":"2026-08-07T12:35:29.182396Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.182396Z"},"links":{"cited_paper":"/paper/2402.05672","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:c7579a566cca73da6551c72d9ca1fd00461dc244dfac785e3e376998e5818399","observation_id":"af4e925b-d9ac-46d8-baca-2c7025403f16","resolution":{"observed_at":"2026-08-07T12:35:29.182396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:30.628278Z","title":null,"venue":null,"work_id":"c86779c6-8e9f-47cc-99e2-b6393b40025f","year":2021},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.260806Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:b2bfc91c91bce67c44ee9bfaed9774ef4caad6553beb74162d07e75758eb56da","observation_id":"68865602-5e7a-47b8-9207-1a2e1e14c800","resolution":{"observed_at":"2026-08-07T12:35:30.704768Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07597","last_updated":"2024-09-24T03:01:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-14T10:57:50Z","title":"C-Pack: Packed Resources For General Chinese Embeddings","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07597","snapshot_observed_at":"2026-08-07T12:35:29.423388Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.423388Z"},"links":{"cited_paper":"/paper/2309.07597","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:f32b448b684af3c5200b81873567a387ae0497b4ca9dc95ef83dbff6f749ef56","observation_id":"9538047d-332b-4769-84e9-5afade6f70f8","resolution":{"observed_at":"2026-08-07T12:35:29.423388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08232","last_updated":"2025-05-22T08:54:14Z","snapshot_observed_at":"2026-07-06T16:31:48.246401Z","submitted_at":"2023-10-12T11:25:46Z","title":"Language Models are Universal Embedders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08232","snapshot_observed_at":"2026-08-07T12:35:29.530957Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.530957Z"},"links":{"cited_paper":"/paper/2310.08232","citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:5a1d0f64e2b281473419ddbe0399d1091679d0a04b4dd74b5384043c9fdf0baf","observation_id":"0278ade8-7992-4456-aca6-2b4ac33e8f70","resolution":{"observed_at":"2026-08-07T12:35:29.530957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:30.433169Z","title":null,"venue":null,"work_id":"03b1ee6b-315c-4c16-9b0b-b73457929409","year":2024},"citing_paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.668279Z"},"links":{"citing_paper":"/paper/2505.24581"},"observation_digest":"sha256:6d54b80e4f19ce4557b7b592ef058da38faadbca60a5129b5c88a42e4a3d0196","observation_id":"b3475423-73dd-44b2-8c05-f247b6684927","resolution":{"observed_at":"2026-08-07T12:35:30.521856Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24581","last_updated":"2025-05-30T13:29:03Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T04:58:02.457770Z","submitted_at":"2025-05-30T13:29:03Z","title":"GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":37,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":38},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.24581."}