{"as_of":"2026-08-11T10:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f8874779b4db81f00d2f5f94bb9281ae2b3e7808df5ebe018d82a7df6026bf93","coverage":[{"denominator":142,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T14:56:19.122278Z","state":"measured"},{"denominator":173,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":173,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":73,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:54:42.664636Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":25,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2412.13663","last_updated":"2024-12-19T06:32:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-18T09:39:44Z","title":"Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference","version":2},"reference_index":170,"source":"arxiv_source","source_observed_at":"2026-05-20T17:46:46.845424Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2412.13663"},"observation_digest":"sha256:f32a1aa961a0425bb823bf3ef5533827e89f1a940a1b241b4db2616091e29f62","observation_id":"9a9c3cf0-7778-47a7-b3b4-133236a24d01","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-10T15:54:42.664636Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13567","last_updated":"2025-05-28T08:20:05Z","snapshot_observed_at":"2026-08-10T16:02:29.962483Z","submitted_at":"2025-01-23T11:14:21Z","title":"K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T15:54:42.664636Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2501.13567"},"observation_digest":"sha256:b65bd67c0032d63d0a187b65214a02e862c6929259f5fd23ea3049d9f44928a6","observation_id":"1a15b3fe-f19f-4e40-915b-f3f78f5da731","resolution":{"observed_at":"2026-08-10T15:54:42.664636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-10T00:12:04.997118Z","title":"Nomic embed: Training a reproducible long context text embedder","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18280","last_updated":"2025-05-17T04:37:43Z","snapshot_observed_at":"2026-08-10T03:17:04.804861Z","submitted_at":"2025-01-30T11:37:40Z","title":"Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T00:12:04.997118Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2501.18280"},"observation_digest":"sha256:13f8c3d23b23d0f7d1c880032fef08ebd0dcc631785b87e10bb243f1431c698f","observation_id":"7917cc13-f42e-4a6e-8dbc-96b287e21b68","resolution":{"observed_at":"2026-08-10T00:12:04.997118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-09T20:48:29.784749Z","title":"arXiv preprint arXiv:2402.01613 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.19264","last_updated":"2025-01-31T16:24:46Z","snapshot_observed_at":"2026-08-11T00:46:58.128990Z","submitted_at":"2025-01-31T16:24:46Z","title":"mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T20:48:29.784749Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2501.19264"},"observation_digest":"sha256:d70674f6fd73619d8b07a0ccbae5a1597973612cf36b659b97da3b811ea9f32e","observation_id":"25fa70d7-a7ba-49d8-8a75-69e75da61ddc","resolution":{"observed_at":"2026-08-09T20:48:29.784749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-09T00:40:42.558465Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03824","last_updated":"2025-02-14T01:05:29Z","snapshot_observed_at":"2026-08-09T19:08:19.552527Z","submitted_at":"2025-02-06T07:19:59Z","title":"Syntriever: How to Train Your Retriever with Synthetic Data from LLMs","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-09T00:40:42.558465Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2502.03824"},"observation_digest":"sha256:9fa3bf3811fe3cd64aee12e3567963a755f369c02520e8616a68176e87b88f50","observation_id":"56c5b6c3-a6c0-40c6-a8ab-5afd55b7b73b","resolution":{"observed_at":"2026-08-09T00:40:42.558465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-08T13:51:16.604059Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07096","last_updated":"2025-02-10T22:40:34Z","snapshot_observed_at":"2026-08-08T14:51:58.178404Z","submitted_at":"2025-02-10T22:40:34Z","title":"Lotus: Creating Short Videos From Long Videos With Abstractive and Extractive Summarization","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T13:51:16.604059Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2502.07096"},"observation_digest":"sha256:7f1f1744ec893912f57a333ae2cf595ef2bad8e56f778cab77bb6641bb700dfc","observation_id":"bd1b9581-f8c3-412d-bdab-2cefaf90c9bd","resolution":{"observed_at":"2026-08-08T13:51:16.604059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-08T11:20:23.667474Z","title":"Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.07972","last_updated":"2025-03-09T19:39:00Z","snapshot_observed_at":"2026-08-10T08:47:57.539365Z","submitted_at":"2025-02-11T21:36:31Z","title":"Training Sparse Mixture Of Experts Text Embedding Models","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T11:20:23.667474Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2502.07972"},"observation_digest":"sha256:8e4b37bcef77eba3172615a7909d68d8dc02496914393cb34adf79f0337dd752","observation_id":"b2fba75c-34fc-4247-8982-85c0cb330d0a","resolution":{"observed_at":"2026-08-08T11:20:23.667474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T21:10:57.457233Z","title":"arXiv:2402.01613 [cs.CL]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09532","last_updated":"2025-02-13T17:49:30Z","snapshot_observed_at":"2026-08-10T17:39:55.470568Z","submitted_at":"2025-02-13T17:49:30Z","title":"Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T21:10:57.457233Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2502.09532"},"observation_digest":"sha256:a9c20245078ef0940ef5afe03bbbfd80e141fd6c9e178107da2f8f475a763da4","observation_id":"b6a3657c-cd84-4a1f-af9f-e78f9762bc80","resolution":{"observed_at":"2026-08-07T21:10:57.457233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2502.09891","last_updated":"2026-05-11T03:00:07Z","snapshot_observed_at":"2026-07-06T20:36:26.822412Z","submitted_at":"2025-02-14T03:28:36Z","title":"ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation","version":4},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-23T03:28:13.313028Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2502.09891"},"observation_digest":"sha256:7d0d436a1502679780187af72d63342c9a406cd72617d4b247890b10447e1039","observation_id":"fa23f9b3-2de1-44d9-a99d-25b5e20e9e25","resolution":{"observed_at":"2026-05-23T03:32:28.547594Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T22:18:53.463113Z","title":"Nussbaum, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2503.16439","last_updated":"2025-02-13T12:29:55Z","snapshot_observed_at":"2026-08-09T18:59:57.485068Z","submitted_at":"2025-02-13T12:29:55Z","title":"DreamLLM-3D: Affective Dream Reliving using Large Language Model and 3D Generative AI","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T22:18:53.463113Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2503.16439"},"observation_digest":"sha256:98404125b80d9f4925af5f8aff0722e327702cf66e11669b1d3ef151ffaecad1","observation_id":"777a1c9a-8667-471e-ac17-487a3c6f4e26","resolution":{"observed_at":"2026-08-07T22:18:53.463113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2504.20018","last_updated":"2026-05-03T16:16:02Z","snapshot_observed_at":"2026-08-02T21:31:44.700892Z","submitted_at":"2025-04-28T17:36:06Z","title":"MINT: Multi-Vector Search Index Tuning","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-22T17:46:53.722294Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2504.20018"},"observation_digest":"sha256:1ba4bca704c112023b55e98f6af65ff1e82b771b29b68007035fdb47cdb0f25b","observation_id":"74a5e340-d0cd-4c0e-932a-9ab7b76ef537","resolution":{"observed_at":"2026-05-22T17:51:55.226794Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T15:32:26.935041Z","title":"Nomic embed: Training a reproducible long context text embedder","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14802","last_updated":"2025-05-20T18:12:19Z","snapshot_observed_at":"2026-08-09T20:18:07.794790Z","submitted_at":"2025-05-20T18:12:19Z","title":"Text embedding models can be great data engineers","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:26.935041Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2505.14802"},"observation_digest":"sha256:8eea85f263de3c0ddaf31b2e34240c0f80d0edf1821b1a1163354f2199d2f05d","observation_id":"9a6514c0-98bc-4723-a6f5-8d050042e49e","resolution":{"observed_at":"2026-08-07T15:32:26.935041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T14:36:12.495892Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18366","last_updated":"2025-05-23T20:51:20Z","snapshot_observed_at":"2026-08-10T02:19:26.849552Z","submitted_at":"2025-05-23T20:51:20Z","title":"Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T14:36:12.495892Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2505.18366"},"observation_digest":"sha256:f40385d0f25e0a178b685011363a292c4bc99eb81f874b51c66f6adccfa1743b","observation_id":"ae2e0c81-8095-4c5d-ad2c-c2bfa6667611","resolution":{"observed_at":"2026-08-07T14:36:12.495892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T13:21:16.818037Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22041","last_updated":"2025-05-28T07:03:46Z","snapshot_observed_at":"2026-08-08T14:51:09.824736Z","submitted_at":"2025-05-28T07:03:46Z","title":"Detecting Undesired Process Behavior by Means of Retrieval Augmented Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:21:16.818037Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2505.22041"},"observation_digest":"sha256:3f4160edf6f57ab661e40cc43a12f401173f4791216a53c40b6ff0f73851b400","observation_id":"d542fbb3-4732-4cfb-b652-4d0e3564ad04","resolution":{"observed_at":"2026-08-07T13:21:16.818037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T12:35:38.750679Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24782","last_updated":"2025-06-06T16:42:11Z","snapshot_observed_at":"2026-08-08T23:24:56.284825Z","submitted_at":"2025-05-30T16:43:28Z","title":"Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:38.750679Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2505.24782"},"observation_digest":"sha256:5f33adfe52924c65c2047476907db21111712a92dddc3d1bb559cace2b014b9e","observation_id":"6d46216c-ebb0-4f07-93c5-735c10480da5","resolution":{"observed_at":"2026-08-07T12:35:38.750679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2506.00077","last_updated":"2026-04-03T21:44:46Z","snapshot_observed_at":"2026-07-06T21:33:55.658995Z","submitted_at":"2025-05-29T23:39:24Z","title":"Gaussian mixture models as a proxy for interacting language models","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-19T11:53:28.278150Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.00077"},"observation_digest":"sha256:675799203be89ef8273d1a9835d74d41f59606ef3fd3bc0919e80c4353bfd9ad","observation_id":"100a8e2b-14e2-450b-a9df-7ad465347ef6","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T11:49:14.972297Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.01435","last_updated":"2025-06-02T08:50:38Z","snapshot_observed_at":"2026-08-10T16:03:16.432212Z","submitted_at":"2025-06-02T08:50:38Z","title":"Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T11:49:14.972297Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.01435"},"observation_digest":"sha256:31d6c7dfb6faa2c4228acc86da917acba13bf2b48382038ee2ae106bcb4a5215","observation_id":"75440c7a-21d1-4fa8-9aac-5548dc5f4af4","resolution":{"observed_at":"2026-08-07T11:49:14.972297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T11:25:46.029625Z","title":"Nomic embed: Training a reproducible long context text embedder","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02825","last_updated":"2025-07-02T19:19:46Z","snapshot_observed_at":"2026-08-10T01:37:52.353403Z","submitted_at":"2025-06-03T12:54:24Z","title":"Asymptotically perfect seeded graph matching without edge correlation (and applications to inference)","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:25:46.029625Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.02825"},"observation_digest":"sha256:73f6a66e83b5c6fee8009d58ab2623b209e7d39177547a09e1ed1402efc38cc2","observation_id":"17c5a633-67ae-4098-a140-ce5822d89682","resolution":{"observed_at":"2026-08-07T11:25:46.029625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T00:41:14.397251Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12895","last_updated":"2025-06-15T15:53:38Z","snapshot_observed_at":"2026-08-07T00:35:12.173860Z","submitted_at":"2025-06-15T15:53:38Z","title":"Assessing the Performance Gap Between Lexical and Semantic Models for Information Retrieval With Formulaic Legal Language","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T00:41:14.397251Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.12895"},"observation_digest":"sha256:b155baa27e218fa7c79b5f67e97a3d22b13ff3f8b6814b3268da9467de3afa1c","observation_id":"e5d91d9b-994e-47f4-a227-643305344e28","resolution":{"observed_at":"2026-08-07T00:41:14.397251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T13:59:48.708781Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15690","last_updated":"2025-07-24T05:08:02Z","snapshot_observed_at":"2026-08-10T17:43:02.051231Z","submitted_at":"2025-05-26T22:10:52Z","title":"LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T13:59:48.708781Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.15690"},"observation_digest":"sha256:fceda91746e144973ce6e9d1f41c34bcc6cd7636ee6112b4d807b97d3c9bac8a","observation_id":"8deeb560-0dcc-4ac8-a768-3e30b051621c","resolution":{"observed_at":"2026-08-07T13:59:48.708781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T23:28:03.180723Z","title":"arXiv preprint arXiv:2402.01613 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18036","last_updated":"2025-06-22T13:34:58Z","snapshot_observed_at":"2026-08-08T14:51:59.166265Z","submitted_at":"2025-06-22T13:34:58Z","title":"Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:28:03.180723Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.18036"},"observation_digest":"sha256:2e14315bf922636f2ffdaf0c8ee7e9806f040a3b23b973ff5319093dfb39ee4c","observation_id":"608edd58-e958-4472-9ee2-65f4004951c4","resolution":{"observed_at":"2026-08-06T23:28:03.180723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T22:33:28.865781Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21288","last_updated":"2025-06-26T14:09:41Z","snapshot_observed_at":"2026-08-10T09:12:52.784242Z","submitted_at":"2025-06-26T14:09:41Z","title":"Small Encoders Can Rival Large Decoders in Detecting Groundedness","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T22:33:28.865781Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2506.21288"},"observation_digest":"sha256:d86a24aed66b87ea0f5b1f90993f1ffa247ae3b5d32cd4b9b0c6a4c435d4b224","observation_id":"c60aa4ef-5507-4a7c-98ac-56b29b456de4","resolution":{"observed_at":"2026-08-06T22:33:28.865781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-07T04:07:57.307865Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.00014","last_updated":"2025-06-13T07:11:14Z","snapshot_observed_at":"2026-08-07T04:02:03.046922Z","submitted_at":"2025-06-13T07:11:14Z","title":"SWE-Bench-CL: Continual Learning for Coding Agents","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:57.307865Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.00014"},"observation_digest":"sha256:0e2e5e297b2b45f38d054049c6fe077671bfa7a8454d841c54e219ff62ec39f7","observation_id":"8faff816-88b5-4194-8efd-126bc925c51a","resolution":{"observed_at":"2026-08-07T04:07:57.307865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T22:05:31.315365Z","title":"arXiv preprint arXiv:2402.01613 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00066","last_updated":"2025-06-28T02:04:06Z","snapshot_observed_at":"2026-08-09T09:47:49.636184Z","submitted_at":"2025-06-28T02:04:06Z","title":"InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:05:31.315365Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.00066"},"observation_digest":"sha256:1ec4e773e262687c070fdfa292526deb25f17cd0c1462d6c965c577719f3d734","observation_id":"99bcf12a-e80c-4c68-9aea-a92edab2483c","resolution":{"observed_at":"2026-08-06T22:05:31.315365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T20:18:07.642297Z","title":"Nomic embed: Training a reproducible long context text embedder,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05279","last_updated":"2025-07-04T08:48:15Z","snapshot_observed_at":"2026-08-11T04:05:34.281797Z","submitted_at":"2025-07-04T08:48:15Z","title":"ReservoirChat: Interactive Documentation Enhanced with LLM and Knowledge Graph for ReservoirPy","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:18:07.642297Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.05279"},"observation_digest":"sha256:37252477cd39e2933105264e30792a30b21b37202318a085095946972c035b65","observation_id":"465a1058-44f2-4641-8530-78d9cbc7ae6c","resolution":{"observed_at":"2026-08-06T20:18:07.642297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T16:41:16.950593Z","title":"Nomic embed: Training a reproducible long context text embedder,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15874","last_updated":"2025-07-17T08:33:56Z","snapshot_observed_at":"2026-08-09T20:33:11.320048Z","submitted_at":"2025-07-17T08:33:56Z","title":"Why Braking? Scenario Extraction and Reasoning Utilizing LLM","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:41:16.950593Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.15874"},"observation_digest":"sha256:cfdb261ef9d78829f6d25427fe391580683dfba8de727c2f1cca2d54a3b1a577","observation_id":"0d050319-fedb-4d3e-9c4e-4cbe0845285f","resolution":{"observed_at":"2026-08-06T16:41:16.950593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T15:25:45.901388Z","title":"arXiv:2402.01613","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.16046","last_updated":"2025-07-21T20:27:43Z","snapshot_observed_at":"2026-08-06T19:56:51.454687Z","submitted_at":"2025-07-21T20:27:43Z","title":"Belief Alignment vs Opinion Leadership: Understanding Cross-linguistic Digital Activism in K-pop and BLM Communities","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T15:25:45.901388Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.16046"},"observation_digest":"sha256:8dcbfa545294f7501ebed12f57b1d775e5ac78e471ee456c51679e237c38ae20","observation_id":"744715f1-b240-48a3-a33f-0f0fcd3ab214","resolution":{"observed_at":"2026-08-06T15:25:45.901388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T14:25:56.602942Z","title":"Nomic embed: Training a reproducible long context text embedder","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.19407","last_updated":"2025-08-06T10:08:06Z","snapshot_observed_at":"2026-08-07T05:53:35.522864Z","submitted_at":"2025-07-25T16:15:00Z","title":"Towards Domain Specification of Embedding Models in Medicine","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:25:56.602942Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.19407"},"observation_digest":"sha256:673692777fe80c01c33c453cd2da7bd006acd8de1d8af786a9757547aad6cba0","observation_id":"bb9fa701-e959-4c5c-96ba-92cb31992c0a","resolution":{"observed_at":"2026-08-06T14:25:56.602942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T15:41:54.485030Z","title":"arXiv:2402.01613 [cs.CL]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.22917","last_updated":"2025-07-21T05:19:41Z","snapshot_observed_at":"2026-08-07T22:48:40.606711Z","submitted_at":"2025-07-21T05:19:41Z","title":"Reading Between the Timelines: RAG for Answering Diachronic Questions","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T15:41:54.485030Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.22917"},"observation_digest":"sha256:d5eb8f29b6852f55dd38a1f7f599b1c2bb601bb19bfa41ac2ac2155d5be24040","observation_id":"091a093c-5f54-419c-8e27-ab861bde3296","resolution":{"observed_at":"2026-08-06T15:41:54.485030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T12:07:15.065481Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22952","last_updated":"2025-08-01T20:14:11Z","snapshot_observed_at":"2026-08-09T07:23:04.872899Z","submitted_at":"2025-07-29T18:00:22Z","title":"Automated Label Placement on Maps via Large Language Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:07:15.065481Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2507.22952"},"observation_digest":"sha256:4d40cc79222cada3d0568cc27973a6d6ace67515060931a6087fb09de0da8b68","observation_id":"6f165230-ae71-4edc-baf2-52052bc52bbf","resolution":{"observed_at":"2026-08-06T12:07:15.065481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2508.01959","last_updated":"2026-04-21T12:01:19Z","snapshot_observed_at":"2026-08-08T22:33:09.827204Z","submitted_at":"2025-08-03T23:59:31Z","title":"SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-19T00:44:07.893905Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2508.01959"},"observation_digest":"sha256:5dfb092eeccc23ef21eefe3037445018aae192fd78bba5653e0fe322ae3b0371","observation_id":"1790172e-60bb-4871-a0e4-343cac014657","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-06T04:23:43.412903Z","title":"arXiv:2402.01613 [cs.CL]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03558","last_updated":"2025-08-05T15:28:13Z","snapshot_observed_at":"2026-08-09T12:50:31.320343Z","submitted_at":"2025-08-05T15:28:13Z","title":"SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T04:23:43.412903Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2508.03558"},"observation_digest":"sha256:8b3cb9ba83f08c9aa494d68f09b5f13eb78be914278b693a68ce382bc44ddadb","observation_id":"5acd201f-28e5-4911-a091-1aa705023ad5","resolution":{"observed_at":"2026-08-06T04:23:43.412903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-04T20:03:51.814137Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08907","last_updated":"2025-09-10T18:09:45Z","snapshot_observed_at":"2026-08-11T02:37:15.854746Z","submitted_at":"2025-09-10T18:09:45Z","title":"Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-04T20:03:51.814137Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2509.08907"},"observation_digest":"sha256:ad147866fa0eb2e78e119b57bd1dbf0bdf41a8cadfd81c05335d6b666707deb0","observation_id":"af216801-2ed6-44d1-a808-c4268252a8f4","resolution":{"observed_at":"2026-08-04T20:03:51.814137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2603.28816","last_updated":"2026-04-06T12:03:14Z","snapshot_observed_at":"2026-08-08T11:06:33.713465Z","submitted_at":"2026-03-28T17:09:17Z","title":"ASTRA: Mapping Art-Technology Institutions via Conceptual Axes, Text Embeddings, and Unsupervised Clustering","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-14T21:51:40.716196Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2603.28816"},"observation_digest":"sha256:ab638522b8eb0959b6690379abd4d9debe54ba697429b5719bb819df7f196415","observation_id":"facfed51-06e3-48b3-9279-c98631cd4b05","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.07079","last_updated":"2026-04-08T13:35:09Z","snapshot_observed_at":"2026-08-11T06:15:33.603064Z","submitted_at":"2026-04-08T13:35:09Z","title":"MARVEL: Multimodal Adaptive Reasoning-intensiVe Expand-rerank and retrievaL","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T17:51:16.675856Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.07079"},"observation_digest":"sha256:7283e432d4ae8e6c8e99e41df1a2d76edc92635175e0d68f38b49b7eb0ec67de","observation_id":"249ad6fb-12b0-4a48-b265-54e0b4a26bed","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.07201","last_updated":"2026-04-08T15:28:21Z","snapshot_observed_at":"2026-07-06T22:55:28.855291Z","submitted_at":"2026-04-08T15:28:21Z","title":"BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T17:28:59.838565Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.07201"},"observation_digest":"sha256:a74f151f04fcf30eb23c281790d13d443a0707ee853b0efdc3744921f46c5e34","observation_id":"7182ae18-fae2-482e-8526-3f7850adf23c","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.12133","last_updated":"2026-04-13T23:33:43Z","snapshot_observed_at":"2026-07-06T23:00:22.690154Z","submitted_at":"2026-04-13T23:33:43Z","title":"Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T15:05:34.908337Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.12133"},"observation_digest":"sha256:1bd4306a98c9976eeeaf675ce6b21d677bc5eae512a4912a865661a2691f14aa","observation_id":"caf70f9b-bdbb-43d4-91dd-905d6f1379bf","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.15597","last_updated":"2026-04-17T00:33:32Z","snapshot_observed_at":"2026-07-06T23:03:07.228701Z","submitted_at":"2026-04-17T00:33:32Z","title":"LLMs Corrupt Your Documents When You Delegate","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-10T09:47:21.966292Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.15597"},"observation_digest":"sha256:894e94720e21923523a023fb4d6e593f5c585ea48c1a802a6a941ab2c54a5b40","observation_id":"9963600b-c9d9-4075-813a-d21ac40daaf2","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.20711","last_updated":"2026-07-20T19:02:16Z","snapshot_observed_at":"2026-08-11T08:47:43.067015Z","submitted_at":"2026-04-22T15:54:16Z","title":"Participatory provenance as representational auditing for AI-mediated public consultation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-09T23:46:25.907511Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.20711"},"observation_digest":"sha256:bd82e45e26f57bc045049bc1d71b8b56e121ee95d9476756b02778f21855e3f9","observation_id":"84d89478-ee01-4c18-91bf-8570ee613716","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-02T15:47:03.663217Z","title":"Morris, Brandon Duderstadt, and Andriy Mulyar","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.20711","last_updated":"2026-07-20T19:02:16Z","snapshot_observed_at":"2026-08-11T08:47:43.067015Z","submitted_at":"2026-04-22T15:54:16Z","title":"Participatory provenance as representational auditing for AI-mediated public consultation","version":3},"reference_index":1971,"source":"pdf_text","source_observed_at":"2026-08-02T15:47:03.663217Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.20711"},"observation_digest":"sha256:4d323f1adfa677f0cfd3f7e8f30e8f6e806f287b6b2ae6419ebff24ea82b1cd8","observation_id":"a7b23d5a-5ad9-41a8-ad62-e084b7488f1b","resolution":{"observed_at":"2026-08-02T15:47:03.663217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.24623","last_updated":"2026-04-27T15:52:20Z","snapshot_observed_at":"2026-07-06T23:10:38.548416Z","submitted_at":"2026-04-27T15:52:20Z","title":"XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-08T03:26:18.733886Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.24623"},"observation_digest":"sha256:e27412109ceeffa10dc8a533874eb3785813c5e9c1368ed15d3eeca32eb0d5da","observation_id":"8e11f16b-1cb5-485b-9afe-bf8fc0cb8627","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2604.25605","last_updated":"2026-07-08T13:50:42Z","snapshot_observed_at":"2026-07-12T18:09:25.946617Z","submitted_at":"2026-04-28T13:09:48Z","title":"Health System Scale Semantic Search Across Unstructured Clinical Notes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-07T15:15:13.378488Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2604.25605"},"observation_digest":"sha256:102fd14b55fa389c4bce072fc3472697f6069cc6b477186686b8a89d45a30234","observation_id":"f3d588e7-2928-44c3-b814-82220f7e6503","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.00618","last_updated":"2026-05-01T12:41:00Z","snapshot_observed_at":"2026-08-02T08:06:50.883100Z","submitted_at":"2026-05-01T12:41:00Z","title":"Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-09T19:29:17.346870Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.00618"},"observation_digest":"sha256:aad8e102d43fe4acfc0ebf9192f22efb564be96e49e27068009607c2f2409869","observation_id":"87a161dd-94a3-42b5-8cee-ba159ec583f3","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.07249","last_updated":"2026-05-08T05:10:05Z","snapshot_observed_at":"2026-07-31T07:41:30.428455Z","submitted_at":"2026-05-08T05:10:05Z","title":"MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-11T02:33:25.462269Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.07249"},"observation_digest":"sha256:e39e0fcad9c32b53fd63b3ae002913ef5ddd0f1afa261fdebf4ea0fd1a58604c","observation_id":"a7f0ee18-f18c-4f2b-95fc-9fcb113b9b57","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.07878","last_updated":"2026-05-08T15:32:35Z","snapshot_observed_at":"2026-07-06T23:20:11.042952Z","submitted_at":"2026-05-08T15:32:35Z","title":"Black-box model classification under the discriminative factorization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-11T02:24:37.402352Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.07878"},"observation_digest":"sha256:8c7af062269e19c32452f5ead133910d820ddc4f2120b8b83c2fecb9e59e09cc","observation_id":"f4e8faad-8410-4730-b245-f32dc07237a3","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.09321","last_updated":"2026-05-14T08:54:52Z","snapshot_observed_at":"2026-07-06T23:21:26.017420Z","submitted_at":"2026-05-10T04:46:58Z","title":"OpenIIR: An Open Simulation Platform for Information Retrieval Research","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T02:32:03.241694Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.09321"},"observation_digest":"sha256:c957fdda4cdfe8fb269c8cde00aaf2a14d62c3396edf19746290da770dbd8066","observation_id":"7abb5786-500f-4026-bfe0-25162da6183f","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.09321","last_updated":"2026-05-14T08:54:52Z","snapshot_observed_at":"2026-07-06T23:21:26.017420Z","submitted_at":"2026-05-10T04:46:58Z","title":"OpenIIR: An Open Simulation Platform for Information Retrieval Research","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T05:54:50.265950Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.09321"},"observation_digest":"sha256:0164af99b310adb41a1ab92e6f198a4d486d3e8bc4ae6e66786ee45c4c5d6c19","observation_id":"e07d7420-ee07-442d-86c2-0add025edcde","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.10782","last_updated":"2026-05-11T16:17:13Z","snapshot_observed_at":"2026-07-06T23:22:42.512039Z","submitted_at":"2026-05-11T16:17:13Z","title":"TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-12T04:49:03.891368Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.10782"},"observation_digest":"sha256:866d156defcf7ab3ac6aa505bb9d0006aed1f6b5fa47998f80652c8892aa176c","observation_id":"bd0ee1f5-b3c8-4827-93cf-a2b7e962656a","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.11135","last_updated":"2026-05-11T18:42:12Z","snapshot_observed_at":"2026-07-06T23:23:02.025664Z","submitted_at":"2026-05-11T18:42:12Z","title":"Control Charts for Multi-agent Systems","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-13T01:04:30.602210Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.11135"},"observation_digest":"sha256:2e05b3630fc0c876bf4938ba1028070fa5270a06fcf4b84111093effcc2a8ad0","observation_id":"eef3defa-3273-4ec1-b7ea-37e37d83e7d1","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.12714","last_updated":"2026-05-12T20:22:45Z","snapshot_observed_at":"2026-08-01T21:01:43.481918Z","submitted_at":"2026-05-12T20:22:45Z","title":"Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-14T21:50:10.564922Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.12714"},"observation_digest":"sha256:0462084996e0aa501f4fbf46abd76520995e08bd9f33efecb660a351dd11d5f1","observation_id":"468d79b3-ce64-4d32-89b1-28a65621d1a4","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.17524","last_updated":"2026-05-29T01:11:16Z","snapshot_observed_at":"2026-07-06T23:28:30.942078Z","submitted_at":"2026-05-17T16:15:21Z","title":"Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-20T14:22:30.531895Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.17524"},"observation_digest":"sha256:6b2e4452be244044ccd33eaea57a37a5780727c99fcdc5a5ac7d31eed56966bf","observation_id":"1a3bb879-6412-4830-8864-847094a18e09","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.17524","last_updated":"2026-05-29T01:11:16Z","snapshot_observed_at":"2026-07-06T23:28:30.942078Z","submitted_at":"2026-05-17T16:15:21Z","title":"Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T18:55:01.432602Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.17524"},"observation_digest":"sha256:fcdf9640dfc4ca82e34ea43b2c435e3820126edf6b85fc2d696305bdbc69bba3","observation_id":"d823c52e-ef81-4034-a0d5-1952a6b4bb59","resolution":{"observed_at":"2026-06-30T19:15:01.273953Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.20815","last_updated":"2026-05-20T07:09:53Z","snapshot_observed_at":"2026-07-06T23:31:23.407780Z","submitted_at":"2026-05-20T07:09:53Z","title":"GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-21T05:02:21.191699Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.20815"},"observation_digest":"sha256:755033baa96ab6630837c0f8f968aa26523bcf2f1cb7b6d6089d2a61111ac316","observation_id":"b5ec3356-390c-4927-bc8e-114f42fd3d68","resolution":{"observed_at":"2026-05-21T14:56:19.527257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.24297","last_updated":"2026-05-26T04:08:36Z","snapshot_observed_at":"2026-08-08T17:43:25.866604Z","submitted_at":"2026-05-22T23:51:13Z","title":"Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clustering","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T14:06:57.864207Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.24297"},"observation_digest":"sha256:2183fc90a0696a20d448b7b5a8f38be0472e32d050a6d59891ffecf3e14b4815","observation_id":"6a418b73-3495-4d96-a9ed-e90e6eee750e","resolution":{"observed_at":"2026-06-30T14:14:45.812991Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2605.26409","last_updated":"2026-05-26T00:36:42Z","snapshot_observed_at":"2026-08-02T10:55:21.817292Z","submitted_at":"2026-05-26T00:36:42Z","title":"Jailbreak susceptibility prediction and mitigation via the behavioral geometry of models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T17:43:47.849960Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2605.26409"},"observation_digest":"sha256:c194fc648270f824cd4f688c6e066ca0095604096e6d23944804e520e1b4d51b","observation_id":"06fc51e2-8089-4760-91c0-f4199eea8fd7","resolution":{"observed_at":"2026-06-29T17:53:47.699732Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.01561","last_updated":"2026-06-01T02:06:58Z","snapshot_observed_at":"2026-08-01T20:22:50.507895Z","submitted_at":"2026-06-01T02:06:58Z","title":"S-SPPO: Semantic-Calibrated Self-Play Preference Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T14:58:33.167891Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.01561"},"observation_digest":"sha256:b8956506a62e2eb9f94d3fa93a9e6cc423563a1751d5dc92381244c165da0810","observation_id":"2c3597ac-aa5f-461c-85c6-b59b49bbbe02","resolution":{"observed_at":"2026-07-01T22:46:20.321866Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.06106","last_updated":"2026-06-04T12:50:11Z","snapshot_observed_at":"2026-08-06T14:09:14.661601Z","submitted_at":"2026-06-04T12:50:11Z","title":"WebKnoGraph: GNN-Powered Internal Linking","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T23:30:43.663094Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.06106"},"observation_digest":"sha256:cf55b2ae0c90eda4c1afda696ee26c055e79358ca66a88b56171a91f49d0f14f","observation_id":"dec27869-193a-4c15-aead-28023d2eeb89","resolution":{"observed_at":"2026-07-02T15:47:06.272000Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.07252","last_updated":"2026-06-08T12:27:36Z","snapshot_observed_at":"2026-08-07T20:31:20.871126Z","submitted_at":"2026-06-05T13:24:46Z","title":"Constrained Dominant Sets for Multimodal Document Question Answering","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-27T20:45:12.783718Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.07252"},"observation_digest":"sha256:ba1f332089ec3537adaa91dcf44f5d2afd45529e7966c44a2a59a2bfb2e8c3bd","observation_id":"2389c1c5-91ed-4fc1-a9b6-399dbafbbef3","resolution":{"observed_at":"2026-07-02T20:17:21.734108Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.07850","last_updated":"2026-08-04T09:33:41Z","snapshot_observed_at":"2026-08-07T23:09:29.070033Z","submitted_at":"2026-06-05T21:21:23Z","title":"PDE-Agents: An LLM-Orchestrated Multi-Agent Framework for Automated Finite Element Simulations with Knowledge Graph-Augmented Reasoning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-27T19:58:16.955786Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.07850"},"observation_digest":"sha256:9080fb2657ddb13450b36c4688bf844beeb83f9785e1ae07df26a1e5ef7c6021","observation_id":"90bdfa64-1aca-44ea-b3e5-2e22ed24c2fc","resolution":{"observed_at":"2026-07-02T21:07:23.561012Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.18103","last_updated":"2026-06-16T16:03:37Z","snapshot_observed_at":"2026-07-31T18:22:23.981249Z","submitted_at":"2026-06-16T16:03:37Z","title":"HistoRAG: Embedding Historical Methodology in Retrieval-Augmented Generation Through Critical Technical Practice","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-06-27T00:26:05.175925Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.18103"},"observation_digest":"sha256:79d67450cf68a0c23bc13a7fcb95db9bcb9ef1cfdba0781439272185dc00a15c","observation_id":"9d2e1a17-5beb-412a-b14e-be8dfc63038c","resolution":{"observed_at":"2026-06-27T00:30:16.188992Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.20047","last_updated":"2026-06-18T10:22:57Z","snapshot_observed_at":"2026-08-03T05:07:33.291267Z","submitted_at":"2026-06-18T10:22:57Z","title":"PACMS: Submodular Context Selection as a Pluggable Engine for LLM Agents","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T15:47:29.381248Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.20047"},"observation_digest":"sha256:1e22e39ed6bce1abb85726f12253fe78e1dbbfa0ccbbaaab2b946cbddc068d88","observation_id":"a914f26f-17f9-45bb-9b37-7d6f601c9c04","resolution":{"observed_at":"2026-07-04T05:39:40.192808Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.21123","last_updated":"2026-06-19T05:51:03Z","snapshot_observed_at":"2026-08-08T09:54:07.537692Z","submitted_at":"2026-06-19T05:51:03Z","title":"A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T14:10:16.632153Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.21123"},"observation_digest":"sha256:f9c5258f8ce7aa8d3d544286f0485db7fc11c385a34a75a0d2bf8aa9a79f31c9","observation_id":"6ae5e6e2-ad0a-4848-8081-e0e417f6d4ec","resolution":{"observed_at":"2026-07-04T06:49:38.001854Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.22778","last_updated":"2026-06-22T02:42:06Z","snapshot_observed_at":"2026-08-08T12:33:48.957919Z","submitted_at":"2026-06-22T02:42:06Z","title":"HAKARI-Bench: A Lightweight Benchmark for Comparing Retrieval Architectures and Efficiency Settings under Unified Conditions","version":1},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-06-26T07:22:34.547816Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.22778"},"observation_digest":"sha256:884b12c452fdae0425fea0022db584c39baaa4e4e91ff343ff2094643290de96","observation_id":"d071a522-3f8c-4550-a0eb-7a73f3c85b58","resolution":{"observed_at":"2026-07-04T11:59:51.214152Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.23642","last_updated":"2026-06-22T17:31:02Z","snapshot_observed_at":"2026-08-06T22:42:54.447414Z","submitted_at":"2026-06-22T17:31:02Z","title":"Improving Long-Context Retrieval with Multi-Prefix Embedding","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-06-26T06:28:03.449379Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.23642"},"observation_digest":"sha256:9eae9e11275c0d8d9ac60cbb31068b9b643698c9c01e8141bb9d7215d2fa49ea","observation_id":"781156f7-d4d4-4fe1-8021-e34e9447f1e0","resolution":{"observed_at":"2026-07-04T12:39:49.196993Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.25343","last_updated":"2026-06-26T04:35:35Z","snapshot_observed_at":"2026-08-06T22:43:48.099674Z","submitted_at":"2026-06-24T03:17:30Z","title":"Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-25T21:15:12.242955Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.25343"},"observation_digest":"sha256:7183e0293f9937c2d07fac1c9abe0e6f1acc0615ba70d8b08ef59f96220e4031","observation_id":"8efa74e2-49ac-4343-a12c-acbb9a5315e1","resolution":{"observed_at":"2026-07-04T19:30:07.788661Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.25343","last_updated":"2026-06-26T04:35:35Z","snapshot_observed_at":"2026-08-06T22:43:48.099674Z","submitted_at":"2026-06-24T03:17:30Z","title":"Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-29T05:07:28.537679Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.25343"},"observation_digest":"sha256:49f1df10e78f24cfb4bdcde271a561c9500c7eee9a91b8130dc9f95086d438a7","observation_id":"f1d01f86-f3fe-405e-987b-89a83cbd8fde","resolution":{"observed_at":"2026-06-29T18:23:51.230462Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.28359","last_updated":"2026-06-09T19:34:18Z","snapshot_observed_at":"2026-08-10T20:43:53.178274Z","submitted_at":"2026-06-09T19:34:18Z","title":"The Voronoi Bottleneck: Capacity-Aware Dense Retrieval for Product Search","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T10:36:35.781906Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.28359"},"observation_digest":"sha256:7c1c6794eb76341f225153880213b9f86528e08600f90486a00af0ee5301c032","observation_id":"b80f39cf-6c4d-4762-a20d-9aad499e080d","resolution":{"observed_at":"2026-06-30T10:44:36.918413Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.29069","last_updated":"2026-06-27T20:12:30Z","snapshot_observed_at":"2026-08-02T06:50:47.012266Z","submitted_at":"2026-06-27T20:12:30Z","title":"Low-cost concept-based localized explanations: How far can we get with training-free approaches?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T09:15:55.371784Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.29069"},"observation_digest":"sha256:15cfb81f3619d1fad93a98309a7d4a59956c13269c0d4b7e83fe3a7be9ad09fd","observation_id":"adf5c2eb-b2b6-4ba4-a4ff-af1b7b41b409","resolution":{"observed_at":"2026-06-30T09:24:32.642236Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":"2402.01613","doi":"10.48550/arxiv.2402.01613","metadata_source":"pith","pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","venue":"cs.CL","work_id":"7344ef1a-710c-4656-bae0-234752f16b85","year":2024},"citing_paper":{"arxiv_id":"2606.30029","last_updated":"2026-06-29T09:28:29Z","snapshot_observed_at":"2026-08-02T08:29:57.562616Z","submitted_at":"2026-06-29T09:28:29Z","title":"ESOFinder: an LLM-powered tool to help users navigate ESO documentation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-30T04:08:19.993584Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2606.30029"},"observation_digest":"sha256:1ab5cff4d6031fa84d23c2d2c09601aece6fa885162e8acaf2faad3ed35b596c","observation_id":"6205c17c-4443-4a3e-b29d-9a698234d8ea","resolution":{"observed_at":"2026-06-30T04:14:18.734759Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-07-11T22:20:52.422988Z","title":"arXiv preprint arXiv:2402.01613 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04011","last_updated":"2026-07-04T20:13:38Z","snapshot_observed_at":"2026-08-06T03:55:35.624754Z","submitted_at":"2026-07-04T20:13:38Z","title":"Separating Representation from Reconstruction Enables Scalable Text Encoders","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-07-11T22:20:52.422988Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2607.04011"},"observation_digest":"sha256:13590fec4edef7ed7b6944f904ea50b7267ccbb195465ac42a9237b2e55c6a1c","observation_id":"c267ec25-95f7-444e-9d2a-9b7912ed6ec3","resolution":{"observed_at":"2026-07-11T22:20:52.422988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-01T22:43:58.006066Z","title":"Nomic embed: Training a reproducible long context text embedder.arXiv preprint arXiv:2402.01613, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15657","last_updated":"2026-07-17T06:05:17Z","snapshot_observed_at":"2026-08-08T02:14:12.766320Z","submitted_at":"2026-07-17T06:05:17Z","title":"Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:58.006066Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2607.15657"},"observation_digest":"sha256:3c4fafcf3857d4f48b8ad2901a0ac6b5a61ef07b0ef863257ff2435a6ed639f3","observation_id":"260e40b1-0fa8-40f2-8c85-a14c77ec1f8f","resolution":{"observed_at":"2026-08-01T22:43:58.006066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-08-01T11:00:31.398212Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20581","last_updated":"2026-07-22T11:52:43Z","snapshot_observed_at":"2026-08-08T03:58:49.424156Z","submitted_at":"2026-07-22T11:52:43Z","title":"Geometric Configurations of Perturbed Jailbreak Prompts","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T11:00:31.398212Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2607.20581"},"observation_digest":"sha256:6e9b1e1d59efe56abf98caec995640a48637693b17cf0b0d72a4b22482a1ce96","observation_id":"9ba42c45-3816-4a4b-ad16-16269e684956","resolution":{"observed_at":"2026-08-01T11:00:31.398212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01613","snapshot_observed_at":"2026-07-30T20:41:37.169734Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23507","last_updated":"2026-07-26T07:13:33Z","snapshot_observed_at":"2026-08-08T22:42:54.413805Z","submitted_at":"2026-07-26T07:13:33Z","title":"Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-30T20:41:37.169734Z"},"links":{"cited_paper":"/paper/2402.01613","citing_paper":"/paper/2607.23507"},"observation_digest":"sha256:cd28eeb7bde9b642ec65838c962e90a887e16b2b8da75e74f8fd2943ae33fd60","observation_id":"0ddd7bba-4c6b-47f2-8a62-e276303a5817","resolution":{"observed_at":"2026-07-30T20:41:37.169734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.01613/citation-record","integrity":"/paper/2402.01613/integrity","json":"/paper/2402.01613/citation-record.json","paper":"/paper/2402.01613"},"outbound":[{"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":"Ms marco: A human generated machine reading comprehension dataset","venue":null,"work_id":"f939f0bd-4234-4d76-881f-097b7db15b5c","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:e532fbed67428b3c43d714a1c0eb1b607b18c68a86befc343b0ecf26d94d7471","observation_id":"a9e7a1ed-ac33-4509-a0d1-802bd40aea58","resolution":{"observed_at":"2026-05-21T14:56:19.489740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation","venue":null,"work_id":"ff20bed8-d79b-400f-aa08-e0fcb0ad77ac","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:bfb2300a594776033695a8a7c7e24f4ad4f3eff8517ee8b87cce204f459c0846","observation_id":"314c151e-d643-4be7-82e4-fe623400d338","resolution":{"observed_at":"2026-05-21T14:56:19.496213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Bowman, Gabor Angeli, Christopher Potts, and Christopher D","venue":null,"work_id":"a5f4bfa5-4546-4074-a84c-b93208f16e8c","year":2015},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:ffc684a398b2c22f4ec4ad03fd54074e97af8682a144dab49242a9a9c675e90d","observation_id":"54e40ddc-0715-462a-b23b-e6df5cad91f1","resolution":{"observed_at":"2026-05-21T14:56:19.498828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15595","last_updated":"2023-06-28T04:26:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-27T16:26:26Z","title":"Extending Context Window of Large Language Models via Positional Interpolation","version":2},"cited_work":{"arxiv_id":"2306.15595","doi":"10.48550/arxiv.2306.15595","metadata_source":"pith","pith_arxiv_id":"2306.15595","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Extending Context Window of Large Language Models via Positional Interpolation","venue":"cs.CL","work_id":"c8b6df85-e7da-4bd8-90a4-d309cc2a0f60","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"cited_paper":"/paper/2306.15595","citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1ada5d8bb99fe4401abbdd62fbcd22393770c152e6cddc7b0e6677e8bfac9e29","observation_id":"39436d80-4a01-4ef1-b90a-e09f38f14dbf","resolution":{"observed_at":"2026-05-21T14:56:19.208883Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-01T10:38:12.283235+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T10:38:12.283235+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+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":"S imple E nglish W ikipedia: A new text simplification task","venue":null,"work_id":"2fc8b26a-04f6-473d-aff7-4d7e1146ffc1","year":2011},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:5f97c2d034963a1feaab5f27a9fd497564b13f24b2a7782635a1ec757e47bf22","observation_id":"8d162375-3b84-457e-811c-f2c5fa282557","resolution":{"observed_at":"2026-05-21T14:56:19.501295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Fu, Stefano Ermon, Atri Rudra, and Christopher Ré","venue":null,"work_id":"f04d9946-9017-498a-b872-477dace690b7","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:879b176e96829689843fb7469782e3935c16e8df7d1da6032416f0845b7a0114","observation_id":"285b6235-9b7a-4ddc-be1c-4506d96f07cf","resolution":{"observed_at":"2026-05-21T14:56:19.504023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Smith, and Matt Gardner","venue":null,"work_id":"162c71ff-6bcb-47b3-9a23-5a3cd0684b91","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:2b46f666d781f67b00ae9952e3b002249e18ef3f73c2ba04b5a4bc7dd988db55","observation_id":"9c24d374-2b3d-4371-be2f-20c11fdaffe7","resolution":{"observed_at":"2026-05-21T14:56:19.506801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":"a23dc4bf-e5af-45a4-bc2b-3f6add5e0a4a","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:32873ce6f6b5aa5c1b224e72d4e44c1308f8298c6c2738958d08925d57785b08","observation_id":"419e18bb-f66c-4ea1-b0fb-57d60ac7f3eb","resolution":{"observed_at":"2026-05-21T14:56:19.509448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Dynamically scaled rope further increases performance of long context llama with zero fine-tuning","venue":null,"work_id":"fa36ef25-0ba3-478b-a723-c20149bcae19","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:7180b3c8ec867402bdbf9f6e129df6a23823bfe8171ae38f6f177ab6b03b74e3","observation_id":"53b19985-3c9e-479f-9017-c0e2edb20363","resolution":{"observed_at":"2026-05-21T14:56:19.512473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Open Question Answering Over Curated and Extracted Knowledge Bases","venue":null,"work_id":"7dc9341b-338e-4ed2-b52a-e96929c4cd0a","year":2014},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:0c87019c12d05b99c326b2b33d84fff8fcbb644500fc9d9339cf55ff74fa0ee8","observation_id":"85788b59-cd24-406a-9e5a-69df958910d1","resolution":{"observed_at":"2026-05-21T14:56:19.515116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/p19-1346","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ELI5: long form question answering","venue":null,"work_id":"a84a09c9-d0b0-440d-ada6-19ad2403cef9","year":2013},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:81ed4e10c96673c4f1aee20edc96ae8db0e80b45c8d89ffd28093ad456171741","observation_id":"b8094a3c-b956-4b50-b903-b604b576c29b","resolution":{"observed_at":"2026-05-21T14:56:19.194728Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Overcoming the lack of parallel data in sentence compression","venue":null,"work_id":"1648cee5-4f08-47a9-90ce-78d2d3268e97","year":2013},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:15abc0b971f68b19736453caa0065ff8f400d7675c63c88be61e74c91cfe76fd","observation_id":"1f09c932-de58-4aab-8940-e9c5f22442be","resolution":{"observed_at":"2026-05-21T14:56:19.517945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-11T03:37:47.647154Z","title":"Wikimedia downloads","venue":null,"work_id":"6426049a-0c99-4fd7-b8ea-b2fa2c9585ff","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:81879003a5c41beb45d78d7d96f1a021f6bb341c2dfa871ff2971c4f9da9b843","observation_id":"0572fa77-ddab-4e9d-8649-8ce520de351b","resolution":{"observed_at":"2026-05-21T14:56:19.520456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Condenser: a pre-training architecture for dense retrieval","venue":null,"work_id":"3f4eddf5-d0de-4c0b-a103-02cf1132202b","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:a508bace5926f94ee3697762749055d8cd56263f8dfd687a5a8a4ab824ff0a98","observation_id":"b0805e6c-5e63-41b8-a3aa-0713b421fa40","resolution":{"observed_at":"2026-05-21T14:56:19.523431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Simcse: Simple contrastive learning of sentence embeddings","venue":null,"work_id":"379fa47f-3d76-43f1-aad2-71317a9b3ce6","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:740d3ffc43d334ff25ba17da293ee9d119d12b71cc6d6fbd00e8910b148a18ac","observation_id":"e359ca93-aafa-4a85-9aae-69828a38a431","resolution":{"observed_at":"2026-05-21T14:56:19.526182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Cramming: Training a language model on a single gpu in one day","venue":null,"work_id":"6fdd1639-6489-4d60-87e6-da7fdd480612","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:aab7ffd269d428ec9b1a0d93f07fc289e94f1ab52d8b06dc29bd78165954ea1f","observation_id":"9840d2a4-8d9c-4057-baea-3ef6abe9848f","resolution":{"observed_at":"2026-05-21T14:56:19.219180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Amazonqa: A review-based question answering task","venue":null,"work_id":"d615550a-a77b-4bda-babb-759b875533a2","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:7db9a1e3b93312e742827956ae90350adf1767ecde572daa65db93c253dbc4bc","observation_id":"6da2ca3d-5c77-4c9e-b24c-a0658ac15223","resolution":{"observed_at":"2026-05-21T14:56:19.221832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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 embeddings: A novel set of high-performance sentence embedding models","venue":null,"work_id":"af32ad86-9049-4fd0-b1ae-3285f43fecdf","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:633c36f72e8a0c1877f236ed6317ca7934929160f6f0ea0a7cd544c458eddb69","observation_id":"39de8126-3ed9-4690-9361-a81839719b56","resolution":{"observed_at":"2026-05-21T14:56:19.224514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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 embeddings 2: 8192-token general-purpose text embeddings for long documents","venue":null,"work_id":"bd1a3f48-2b8d-4c00-8620-838229299699","year":2024},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:50fa7061fd180334ef3d9c928d49463445fe4f6fab4e48093b9ac59979ea8dea","observation_id":"3913f70a-83cf-4002-b50d-35586c03d5f5","resolution":{"observed_at":"2026-05-21T14:56:19.226938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.09436","last_updated":"2020-06-08T09:09:28Z","snapshot_observed_at":"2026-08-06T10:54:14.530969Z","submitted_at":"2019-09-20T11:52:45Z","title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","version":3},"cited_work":{"arxiv_id":"1909.09436","doi":"10.48550/arxiv.1909.09436","metadata_source":"pith","pith_arxiv_id":"1909.09436","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","venue":"cs.LG","work_id":"c800e7c9-4d30-4cea-a30c-2a54a64f9229","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"cited_paper":"/paper/1909.09436","citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:b19bfbb61ec842103c2966f81c4f91e61ea6eb81cd57e77e5cf8ab99777e2eff","observation_id":"b02eee10-6482-4944-a7db-f0d3f869285c","resolution":{"observed_at":"2026-05-21T14:56:19.216423Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-05-21T21:53:08.457832+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T21:53:08.457832+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Unsupervised dense information retrieval with contrastive learning, 2022 a","venue":null,"work_id":"e4cca4a3-5252-4c27-8d31-d227363e689c","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:830426126d488bae6de6a845280b00604f1f9564bc3189759bbfcef38805b0ae","observation_id":"faf76da3-bb44-4b6d-9782-244277514364","resolution":{"observed_at":"2026-05-21T14:56:19.229800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Atlas: Few-shot learning with retrieval augmented language models, 2022 b","venue":null,"work_id":"dc216b40-3f7e-425e-89d0-e9efbb6abd3f","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:16afcce975a4695311ee90d6f7d21683d3b888973ca8e0c33187c5dd27faf844","observation_id":"c1ca809d-1724-4c05-b314-8ec8cac9fe58","resolution":{"observed_at":"2026-05-21T14:56:19.232225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Things I'm learning while training superhot","venue":null,"work_id":"23d9217f-2e7c-4028-91d0-cd3551384bb5","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:d0ac2c54a5327ac24ea006deae03d41a7a97f5ac3ad31c2ad2e05577f6c206af","observation_id":"c11a1f15-179a-4380-ab7d-b5af3868be0e","resolution":{"observed_at":"2026-05-21T14:56:19.234827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Dense passage retrieval for open-domain question answering","venue":null,"work_id":"06e8b66a-bbc6-463b-b6b4-5d74c4b5bfbe","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:e398d8c17c718324496fab2d5e452395dcbe817e17fd1061864942de0dbd58ed","observation_id":"9b64af63-3a4e-478a-9593-96f3ecda7fa7","resolution":{"observed_at":"2026-05-21T14:56:19.237429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Gooaq: Open question answering with diverse answer types","venue":null,"work_id":"f80dcf13-c8d6-40b6-94e5-62b0a0b47779","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:f31549f83747958152f1ece4f6e33ce7ab60cb1eae879d3c28b86fc08ca29850","observation_id":"7fb84582-a55c-45ae-87d4-0d34c06acec5","resolution":{"observed_at":"2026-05-21T14:56:19.240051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Wikihow: A large scale text summarization dataset","venue":null,"work_id":"a7d4e2a9-b224-4a12-9c12-c35c22f57ff4","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:3f991fd4cd07d8a2409acfbbd826b6333dc17d77dd95941947a4cba70f0361d7","observation_id":"eb85c2f3-a020-4298-b9f1-1a1f8c4a928c","resolution":{"observed_at":"2026-05-21T14:56:19.242636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Retrieval-augmented generation for knowledge-intensive nlp tasks, 2021 a","venue":null,"work_id":"97601f5a-b13a-4043-879b-1d9c036cc0e0","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:a821c2f330a55e08cf1f2fd41f888078c4d832a56ba35b1afa438c0401b47140","observation_id":"3e33403a-8b87-4209-b081-9ab1186a644c","resolution":{"observed_at":"2026-05-21T14:56:19.244979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Paq: 65 million probably-asked questions and what you can do with them, 2021 b","venue":null,"work_id":"07fa534b-fa3e-44df-bf02-3bec5da400ec","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:bceb7754c4f253f5397fb32aac11682c64b69fdc0b34385ca8fde411aa79eca5","observation_id":"d2315505-6824-4096-b7a9-f1f558e63e51","resolution":{"observed_at":"2026-05-21T14:56:19.247543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-09T00:15:48.160937Z","title":"Towards general text embeddings with multi-stage contrastive learning","venue":null,"work_id":"66cde5af-a785-47a3-821d-800035d4b7ec","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:936fb4f027244a1f24a99e957b1c59e1b7c6550bb86e3ff922dcd5d27005f450","observation_id":"0738004f-8d85-4ffe-847d-04cdc336f0b9","resolution":{"observed_at":"2026-05-21T14:56:19.249874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Roberta: A robustly optimized bert pretraining approach","venue":null,"work_id":"681a238e-f94c-4b16-a3d7-8c9da755aecf","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:86d3e25036eced8873af285425915eacdffd39cb59659a463e63213a46988ff4","observation_id":"052a44c9-f65d-45d4-ab99-8f98239ccf4d","resolution":{"observed_at":"2026-05-21T14:56:19.252244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":null,"venue":null,"work_id":"b24bf84c-f427-4d76-ab24-a90334cb1148","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:b926aeba6379c206bc9fa5ff09c413142457afdad50727405831152e9a71fba6","observation_id":"47abd8b2-34de-4838-8d60-8c930d2614c3","resolution":{"observed_at":"2026-05-21T14:56:19.254658Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-06T12:42:26.502375Z","title":"Decoupled weight decay regularization","venue":null,"work_id":"32b6dc59-5f3c-43d3-beca-d7f40277af3d","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1dfb28049d3ea3fa2f5337981b671119e1dd3cb76fc36e8db9f1bdeffae6a57d","observation_id":"bc432108-ef0f-4c6b-97cd-646fa1359e10","resolution":{"observed_at":"2026-05-21T14:56:19.257085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Scaling deep contrastive learning batch size under memory limited setup","venue":null,"work_id":"6c9bc464-6b1c-4574-89ad-f52c937a9cd4","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:729a69a7f0436877b532dfe9dc9a6f24348d7323d18b75aa5ccd943fac0d5489","observation_id":"dac1e74d-dd67-4a51-91de-33c49dad4e75","resolution":{"observed_at":"2026-05-21T14:56:19.259293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Mixed precision training","venue":null,"work_id":"a313d3f4-09f2-4406-87aa-13518d1b437d","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:8470e1ba554c28554f9c2893356e2c4012e77ef60c4fd6dd87f6e56d27be4faa","observation_id":"b5f0cbc6-442b-4318-8b2e-50af0d4ca3fa","resolution":{"observed_at":"2026-05-21T14:56:19.261740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Sgpt: Gpt sentence embeddings for semantic search","venue":null,"work_id":"c7d4fcd7-d4ff-45be-b835-7c5179b9ae2e","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:595ec519fc480494f9bc6b96d4013a8971bd20166dfc24b476e598f9bb63beee","observation_id":"8ed5c440-91fe-41f8-8f26-5e91b8bb56f9","resolution":{"observed_at":"2026-05-21T14:56:19.264037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Mteb: Massive text embedding benchmark","venue":null,"work_id":"b8b37f3e-9200-4f62-94c5-7e8ab0e67948","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:6c22ee83a1d282f7cc3885a902e996a17964e28cc754c933fc5987c6b1754aa7","observation_id":"4ff9283c-29da-47cb-a622-6383858e3397","resolution":{"observed_at":"2026-05-21T14:56:19.266765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Text and code embeddings by contrastive pre-training","venue":null,"work_id":"a35955fa-1d8c-4279-a044-dd10924dd148","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:da83e6ee9beaeaa4be3408beebcea3afa8f6dfc8f78228bc533eff19fa6b02c5","observation_id":"e72f69d0-afeb-4cf6-98c2-b816264f102b","resolution":{"observed_at":"2026-05-21T14:56:19.268969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Zhao, Yi Luan, Keith B","venue":null,"work_id":"8a1656ff-1000-4709-86c9-437a43c15f0e","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:442f5c5bef1aa4ab51a94c974f7866c6bf04357a6b30466c13beb31835d72266","observation_id":"8bb65fe3-8031-4af1-b7fe-0345cda5e950","resolution":{"observed_at":"2026-05-21T14:56:19.271057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Hall, Daniel Cer, and Yinfei Yang","venue":null,"work_id":"e4b08ab1-c47e-407f-b7c0-da8aae31cade","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:979ff220825529a575af434917a13d713f8e417c7d591c10649db63e76bea162","observation_id":"30298010-1f10-49a4-b944-0ae25dbaa279","resolution":{"observed_at":"2026-05-21T14:56:19.273225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Yarn: Efficient context window extension of large language models","venue":null,"work_id":"bfae8d9d-3d97-4680-ba31-fc023f1b4561","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:3114f3f249a9adfc871df184985e8d2d93022963645ea7f2787564c76ed39f32","observation_id":"315a114c-0355-4dbc-8a09-e48305294f15","resolution":{"observed_at":"2026-05-21T14:56:19.276938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Mosaicbert: A bidirectional encoder optimized for fast pretraining","venue":null,"work_id":"16e1d323-91d9-4ccb-a96a-00f6d9aabf0a","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1dd50ea1737ca4c89ab1376ced07ad06edf30257141de607acdc8c8976a2acf7","observation_id":"74307b26-9ec3-4593-9e02-9a9655653332","resolution":{"observed_at":"2026-05-21T14:56:19.279172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":null,"venue":null,"work_id":"ea5b6c6a-7a2d-40ad-891d-62f5940a81c1","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:8e35e31f2be95c81ea75c678069a19652af28e9a3f7977c5b0d2c336387c3252","observation_id":"4b4f6f1b-3393-4867-8167-4326b4dd4bb2","resolution":{"observed_at":"2026-05-21T14:56:19.281229Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Zero: Memory optimizations toward training trillion parameter models","venue":null,"work_id":"94d9c518-8f1a-40c9-8a86-400f2db2c0dd","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:ee395403dca1ca7954cb18fed6b674a67773c0aa1d187ee66f0399fe397df4b9","observation_id":"7bb165a0-f9c1-4d8c-ad77-10e8b7faa324","resolution":{"observed_at":"2026-05-21T14:56:19.283427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05250","last_updated":"2016-10-11T02:42:36Z","snapshot_observed_at":"2026-08-08T11:05:45.903773Z","submitted_at":"2016-06-16T16:36:00Z","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","version":3},"cited_work":{"arxiv_id":"1606.05250","doi":"10.48550/arxiv.1606.05250","metadata_source":"pith","pith_arxiv_id":"1606.05250","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","venue":"cs.CL","work_id":"0492dd16-26e8-48d9-874c-3dd90cae7b85","year":2016},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"cited_paper":"/paper/1606.05250","citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:5842e9fda0da1130442cc1f4105b1a71f9ebdec32e71ad903a535f34d3952968","observation_id":"afe77bf6-9c14-47a6-9156-55af48e378d3","resolution":{"observed_at":"2026-05-21T14:56:19.200047Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"In-context retrieval-augmented language models","venue":null,"work_id":"9b9b89c1-b07c-46ce-8753-20c454c6d45b","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:79203b6673efbc0385437e6b74079eb9b5928254c957224bc7739d273d2acaad","observation_id":"a2e56cbd-8123-48fe-8459-22e5d665837f","resolution":{"observed_at":"2026-05-21T14:56:19.285596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Sentence-bert: Sentence embeddings using siamese bert-networks","venue":null,"work_id":"f7d1c575-f31f-435b-a1b5-f4d4bb20678a","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:3e49920761d6a3e95407746b2d9f45f5602165fa3907d83903a2bd21a3b01bc3","observation_id":"d638c389-a214-4e3a-9058-648b13e08f5d","resolution":{"observed_at":"2026-05-21T14:56:19.288006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Introducing embed v3, Nov 2023","venue":null,"work_id":"844012e8-d0e5-4dde-aeb5-53ef4406912e","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:00c71cb954be3edc7f046b3fffdcdd81fed1da21c4279d342642a2c33cda250c","observation_id":"21f5ce5e-c312-4f5d-a02f-cc0898efb536","resolution":{"observed_at":"2026-05-21T14:56:19.289983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Long-context retrieval models with monarch mixer, Jan 2024","venue":null,"work_id":"9e8ac251-77e8-4d6d-bcf1-2e02a6e295bb","year":2024},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:6b5fd0744014d06489959854b8a77e8570120d2a8733cf18a529e6400c152196","observation_id":"78fb5274-983d-4833-bc3a-189fe627f68c","resolution":{"observed_at":"2026-05-21T14:56:19.292138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"SCROLLS : Standardized C ompa R ison over long language sequences","venue":null,"work_id":"7669ba50-1665-44a3-b54f-28147dd5b004","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:770b272b4ec05cda388a808dbfda75ffe231d8f6b3823ecfa4c4f8070ba8a63a","observation_id":"f8ec7f5d-61fe-434d-a0a7-d8ffe882877c","resolution":{"observed_at":"2026-05-21T14:56:19.294341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Glu variants improve transformer","venue":null,"work_id":"04bd62e3-292b-4fc7-8058-c6e5d1d5481e","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:058fa89acb65fab51d3aa236ba3fe936d5f1f8b06182a04203a10fd48b6ed9f4","observation_id":"d6cb0181-101b-4b0c-8a96-4adc1d982d67","resolution":{"observed_at":"2026-05-21T14:56:19.297662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-10T17:07:26.483530Z","title":"Megatron-lm: Training multi-billion parameter language models using model parallelism","venue":null,"work_id":"23281401-4f0f-4944-a9aa-59b314d8a477","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1582cdc3cf06fb406453197c78505093e5bca1ceb0c3a5f4d6739d84d3715665","observation_id":"315201d2-7d74-4e79-9361-a568dc869d01","resolution":{"observed_at":"2026-05-21T14:56:19.300146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Smith, Luke Zettlemoyer, and Tao Yu","venue":null,"work_id":"ce5d2897-8fb0-4ee2-9dfa-ca79f5a761ad","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1120fb6d2012133dac39a4064e85617822fba9e891574d1e10fb013ec3fe05f3","observation_id":"c5d6d168-7178-483f-ae99-13fcbb3aa4ed","resolution":{"observed_at":"2026-05-21T14:56:19.302158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Roformer: Enhanced transformer with rotary position embedding, 2023 b","venue":null,"work_id":"85b89db6-4472-412f-9f7b-8bce15b5aeda","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:3d1f32103b4d22282dffc13b59bf1e1aa26afd1d5251cd962cc009e0424413f9","observation_id":"096ea8b0-b0d0-45ab-834c-f2116ca2dfb3","resolution":{"observed_at":"2026-05-21T14:56:19.304217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10739","last_updated":"2020-06-18T17:59:11Z","snapshot_observed_at":"2026-07-06T09:30:32.320227Z","submitted_at":"2020-06-18T17:59:11Z","title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains","version":1},"cited_work":{"arxiv_id":"2006.10739","doi":"10.48550/arxiv.2006.10739","metadata_source":"pith","pith_arxiv_id":"2006.10739","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T","venue":"cs.CV","work_id":"f5db05ce-abd9-4879-b024-3a5b70071370","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"cited_paper":"/paper/2006.10739","citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1ce848aead282f8fe419b69123c39ae3072cff9254d160b2ef7f5ef8c989403e","observation_id":"8638acfd-0ce9-4798-a0d6-ab8f042a0f69","resolution":{"observed_at":"2026-05-21T14:56:19.204582Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-08T07:38:12.861044+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-08T07:38:12.861044+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models","venue":null,"work_id":"937e2948-e93a-4da3-b1d3-12ae3d2618cc","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:0c5839e3ffc6bbb68b269368d39a4bb1550e2f282e4ea6742c1457add162256b","observation_id":"808b42ff-3395-4f8d-bf95-f6dc383fa879","resolution":{"observed_at":"2026-05-21T14:56:19.306404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"FEVER : a large-scale dataset for fact extraction and VERification","venue":null,"work_id":"41bf57e1-795d-4b7f-8a53-7267135f64f4","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:b3c7e282ee3d4bbb456e12c7403675c30bc9135ad640d61c6275f87c6d1ef2c6","observation_id":"f024f835-5ac5-4220-b40f-1e5d413c90e0","resolution":{"observed_at":"2026-05-21T14:56:19.308588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Representation learning with contrastive predictive coding","venue":null,"work_id":"0eccf922-a995-4d0e-b587-98412b139397","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:17305ca50a87d9f8c6178ea2e3e9a20ef89b5612fc82ce8851a01f8bdab767db","observation_id":"96b00c7a-b6b9-4249-a5a1-c2d3bffd5a6a","resolution":{"observed_at":"2026-05-21T14:56:19.311157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Excited to announce voyage embeddings!, Nov 2023","venue":null,"work_id":"0ce5d950-e90d-4e8d-ad49-36571481010b","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:c40b804c83b0e27693e4801ee866f061e31f260b92db3fc6d82fc59f28ffb326","observation_id":"959e4e03-b0ed-4cd9-8116-90e148d26012","resolution":{"observed_at":"2026-05-21T14:56:19.313460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":null,"venue":null,"work_id":"61034232-93a1-4d18-ad46-4daef9d9d225","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:5b13f860179e85b26c8b6d52503cb91dbcb97e8a6ff92bf4026f9ad2ce53a3c6","observation_id":"6b546737-7976-427a-aa16-5c24c0ce4aaf","resolution":{"observed_at":"2026-05-21T14:56:19.315544Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Text embeddings by weakly-supervised contrastive pre-training","venue":null,"work_id":"a47b28f5-51d4-4ca1-8ccc-b5baf36bb67c","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:daab37b80106df98a4a075637c62b16d4a60792def89cc184bdf6374bc803d09","observation_id":"3c155578-5fca-42aa-b02b-aa4c5853e17a","resolution":{"observed_at":"2026-05-21T14:56:19.317777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Simlm: Pre-training with representation bottleneck for dense passage retrieval, 2023 a","venue":null,"work_id":"b388cbb9-1a32-472a-a9c7-a7789ab5efaa","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:09d670249694e0a8bd12536bb5fb7a9ee740f44d93104cbe7fe1a6af28a8dbbb","observation_id":"dea942ea-773c-4dee-9a14-136b068e2f69","resolution":{"observed_at":"2026-05-21T14:56:19.320193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Improving text embeddings with large language models, 2023 b","venue":null,"work_id":"b4ea1f05-304f-4801-a9d7-dc4f06410655","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:579637ed0dc3188b604d31848ec008cfd87b887b1146904a50973058ea5e365b","observation_id":"25033bea-d0bf-4dc4-b411-8ba3076f2fe6","resolution":{"observed_at":"2026-05-21T14:56:19.322834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"C-pack: Packaged resources to advance general chinese embedding","venue":null,"work_id":"9e363785-0e48-4009-add6-c9456a6e50fd","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:5199d1dcfe41e7baa53d5bc5f9e59e11a1be77aabefbff6cf5890b1c2bd390e7","observation_id":"cd8cf3a1-4aa1-4ce3-9eca-c30d139de9ef","resolution":{"observed_at":"2026-05-21T14:56:19.325323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Cohen, Ruslan Salakhutdinov, and Christopher D","venue":null,"work_id":"91f606a1-bc64-4e2e-9dc2-511fab119120","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:adf4ca456649f7489887fb9bbe10470bfe1c7a41daa63da24cdfb3b911fba7d9","observation_id":"1d15e5e9-c818-4baf-a695-3584cb660d23","resolution":{"observed_at":"2026-05-21T14:56:19.327835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Character-level convolutional networks for text classification","venue":null,"work_id":"20151d9d-c333-414b-8d95-e7df3f1dabde","year":2016},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:248234785541d2be09a6b82dce68ab72f94529b97a88813a43d6d9f77d310c79","observation_id":"fb8ae399-4ed2-46dd-a8d1-51828a19640f","resolution":{"observed_at":"2026-05-21T14:56:19.330065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Aligning books and movies: Towards story-like visual explanations by watching movies and reading books","venue":null,"work_id":"19085e0a-699a-48a4-96e1-b1072986c33c","year":2015},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1b7c3b2b3e24fbbc67749dbaae9356bc45197475054e78726d7c8f2c5eddfe14","observation_id":"a06a5517-f65e-4f83-b264-b76a9beb5efd","resolution":{"observed_at":"2026-05-21T14:56:19.332421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-06T02:11:52.025598Z","title":"2024 , eprint=","venue":null,"work_id":"53f6577a-ca84-4b2e-9382-34b48cbe2b9c","year":2024},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:2e6bf7435d617d265f90932eb423722483afa876d635814def7f9303bacafe82","observation_id":"8be7df5f-21e6-44be-bc14-7bc3b151c801","resolution":{"observed_at":"2026-05-21T14:56:19.334794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Liu , title =","venue":null,"work_id":"b1c599ae-f455-460b-862b-e50ab90de235","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:444c20f114156d0c32b042ea8eaf03a08e8ea4da3a57176057f831ccac312dd0","observation_id":"00e41d2c-839f-4154-bcc2-f379a617307d","resolution":{"observed_at":"2026-05-21T14:56:19.337355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2018 , eprint =","venue":null,"work_id":"02d306cb-4c4f-4f33-ac53-cae3ce7134ec","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:26b39b798a2eb36c8e58eba50b576b37e17d95da706e985180299e0c9228646e","observation_id":"44d75290-b2a1-4328-995b-000f15ddf72b","resolution":{"observed_at":"2026-05-21T14:56:19.339438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2022 , eprint =","venue":null,"work_id":"ebdc5c2f-db53-44b7-ba7e-a4d7f3e658f3","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:7c585a98266e965fec84a55e6bfb4a2c4c608a814f0ce7b4b4f8525fb4c57bb9","observation_id":"d4a17e9b-b62d-487d-a6de-52016a0b5b79","resolution":{"observed_at":"2026-05-21T14:56:19.341796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2019 , eprint =","venue":null,"work_id":"4043d5de-c11d-4991-8ff4-e103389f95d7","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:0ff08d5cdf1d4deaeb58685bb68039c76a2daf6e8d560ee7e2a9da2e21d9a089","observation_id":"bee53397-3f56-432c-bb6b-8584b3ab58db","resolution":{"observed_at":"2026-05-21T14:56:19.343922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Dynamically Scaled RoPE further increases performance of long context LLaMA with zero fine-tuning , author =","venue":null,"work_id":"8ba3025b-90b0-4fbe-9990-89273bc4c9eb","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:72ce1027a3032843765c1233d901afe3a9f8c67d1e61d6651c1c48eb9cfc1c2e","observation_id":"74b8b246-0a11-476f-bc4d-93ab9a4a1111","resolution":{"observed_at":"2026-05-21T14:56:19.346185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2021 , eprint =","venue":null,"work_id":"93fb98ed-5fc8-49ae-a705-d60f42b3abca","year":2021},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:a16f8d41712d653b10490c590b1af59a3a0a30c0ec369dbe22c427feafa0cdd1","observation_id":"80c25d5c-df38-4c4f-9116-db23210731b2","resolution":{"observed_at":"2026-05-21T14:56:19.348157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2022 , eprint =","venue":null,"work_id":"c1e6c284-123e-4347-a7ad-473419ce3943","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:fddcbcb90696560cf5bfe8f29b72181791c49b47b83c750c9992df52b16eb303","observation_id":"576f9ffb-5f4a-42a8-9602-32b2bd121704","resolution":{"observed_at":"2026-05-21T14:56:19.350676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2022 , eprint =","venue":null,"work_id":"f656ac53-2f67-4d31-9efb-761357a37ad8","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:048f844f40faa91aed0befd52d64d3c7b06dbfe1ed4b95ad0a8bcfbf418e14b0","observation_id":"6ede32f3-6d0d-4bf7-93de-c4107795d9c4","resolution":{"observed_at":"2026-05-21T14:56:19.352599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"4617bb42-df96-454c-a500-7e18b273668e","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:040af657bebe9ea14e37f56b6e7e4079db2164f197beed408f94dae3eda93b3d","observation_id":"5c087fc8-fc04-40ac-96c0-383cecc1f7f6","resolution":{"observed_at":"2026-05-21T14:56:19.355010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2024 , eprint =","venue":null,"work_id":"b242639d-97e9-44eb-ad48-2fb319beef76","year":2024},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:afa61ed43276087b8915d84f669cd8f59c96ecfd829d43ed08514e73d80649f3","observation_id":"1e8fbea1-a8e5-4f52-a995-c5c173d95c7b","resolution":{"observed_at":"2026-05-21T14:56:19.357100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2020 , eprint =","venue":null,"work_id":"dc9aec14-7281-4239-b1bb-4381ba2d4ab0","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:f0c40afd83c08a16fdc60d9c0d3eb76d1718b660e95e5a6c3b475dcb3991c584","observation_id":"d0f132ae-32ad-4410-a0de-fafce731da69","resolution":{"observed_at":"2026-05-21T14:56:19.359366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"b3ab6bdc-9c3a-4071-b62c-3a7251ca3a86","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:70f2b91e2e8ab69722835656d5631ce769c1a3cba33c5d1d0d03f6a533df0ce5","observation_id":"cf89cad7-6e8d-46e4-b81a-e24ed4a8c89d","resolution":{"observed_at":"2026-05-21T14:56:19.361779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2019 , eprint =","venue":null,"work_id":"8fab2932-a51f-45d3-a1f6-3587f7a0b070","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:a0ed23986fad768532131103cab11204d1d12fdcfcf9c30cf49a78ec196882c4","observation_id":"5337cfc9-373a-40dc-86d3-0bb51e463490","resolution":{"observed_at":"2026-05-21T14:56:19.364134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2019 , eprint =","venue":null,"work_id":"6a28e81a-2831-44d2-88a6-5e2355322dbd","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:e0af9709dd2d361b7d59638ba9bc19f2f3686b766ff7db23268da62bb0517c51","observation_id":"bf2a0a31-d14f-4bb4-9487-f93c5749e2fc","resolution":{"observed_at":"2026-05-21T14:56:19.366041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.findings-acl.146","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hall, Daniel Cer, and Yinfei Yang","venue":"Findings of the Association for Computational Linguistics: ACL 2022","work_id":"c0499b0c-2d17-4e0b-80e9-7ee4ea48e6c2","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:7be843d60c3a8a9d9172003633cbb73ae8177100ca9a7f90672ca0b02b58fb4c","observation_id":"4462f3f1-0218-44c4-85f7-e2cfb0814ceb","resolution":{"observed_at":"2026-05-21T14:56:19.170214Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-05-23T23:53:53.213718+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T23:53:53.213718+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"5f061a5d-80e6-484e-8db4-4dc7ac64a48e","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:77d0d411b028cefecfc3f9635d63d06191733d65b00a62bc57b325b79cd58d52","observation_id":"7e55990d-b68a-4f57-a181-01668b4acc79","resolution":{"observed_at":"2026-05-21T14:56:19.367854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"ec7af864-b398-4282-9dac-79847e494eb4","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:1da62e8d660e8ea29907e42fb7c66ab886d0b99f837aa61fd6447d63dcbe4b5f","observation_id":"9d740b4b-4da6-4453-9055-8437410e4d03","resolution":{"observed_at":"2026-05-21T14:56:19.369662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2020 , eprint =","venue":null,"work_id":"2877edc5-a86e-452e-aafb-6bbf64943fc5","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:eaa95eda7f251594ee34574af8a63520184d558d4c880165ef4f96e83d968c23","observation_id":"fd3c7dff-c690-475c-a270-27962a9d7534","resolution":{"observed_at":"2026-05-21T14:56:19.371459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2020 , eprint =","venue":null,"work_id":"9efa7fc1-9c1b-4cae-bed2-82746328b46e","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:ceb83f5f600402191688ef3fd1d9c60556c329e90d9ae85e15ac9c509a8fea0c","observation_id":"9131a4b4-879d-4c26-b44f-bf8b5be44941","resolution":{"observed_at":"2026-05-21T14:56:19.373634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2020 , eprint =","venue":null,"work_id":"fe045f19-88e3-4f2f-94d1-0ba1c7458931","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:9ff61029e0ca00f5d2a99d1637c0810107044648a59c96e9e6c97c764e348a66","observation_id":"bf2fba00-89ab-457d-8a57-5edd52d263ff","resolution":{"observed_at":"2026-05-21T14:56:19.375751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"1ef97948-1457-4bfc-ad0c-9fc45b38faf4","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:d7d7a8af43f56f76faba5ababd19a39b548038753ab4ba7aff6d61e1138a48e2","observation_id":"8eb8221b-c4cd-4faa-b8ac-c5808d371a04","resolution":{"observed_at":"2026-05-21T14:56:19.377713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"NAACL-HLT , year =","venue":null,"work_id":"fb4dab76-97c5-44f8-bd35-3dd365a93942","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:c002f02836dbd453f11ef10a1142f3221fbd7f0f6cc2806d7d729402a09bcfc8","observation_id":"b88f8d57-cb18-4915-8131-d22c0de3d4bc","resolution":{"observed_at":"2026-05-21T14:56:19.379644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"note =","venue":null,"work_id":"36803940-8ac2-4567-a2ca-7f95ea70d778","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:ddb67a71d4748eb9269bed791615db5b28d8cbc3212d97c7cde16267983a1b71","observation_id":"21fb59de-05ce-4e9f-8fac-6d0efbf7601c","resolution":{"observed_at":"2026-05-21T14:56:19.381529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2022 , eprint =","venue":null,"work_id":"841420fc-3b44-44b6-b931-296adcd1a77e","year":2022},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:f97455c078c19b3e7de309589751f8cd2d3f7dbc58cdc2f93afea8bb5784c209","observation_id":"ea2328ed-4133-44bc-afb3-218f45f0a0c8","resolution":{"observed_at":"2026-05-21T14:56:19.383346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"8f28e34e-f3b8-4e35-8e0a-5a750ae31de8","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:982d398a33c51c31047592190f2beb18246701d9cd1899c83f7168f0f33aa613","observation_id":"66cd2485-bfe4-4f19-bba2-9ccd15595d2a","resolution":{"observed_at":"2026-05-21T14:56:19.385336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2023 , eprint =","venue":null,"work_id":"b2fb6564-d4db-45cc-89ad-7b1697431516","year":2023},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:bb67c706a2aaaae67be1bd92410607ae7ddfadcf1871c5ccbe28af1d2036c0f0","observation_id":"eb62158c-a9db-4e88-b3b7-0f2eee2df476","resolution":{"observed_at":"2026-05-21T14:56:19.387098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"and Salakhutdinov, Ruslan and Manning, Christopher D","venue":null,"work_id":"da6b3a9f-0abc-49e9-9eb7-92c50959027d","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:f6d097a6020e3309d89e7164bcae9a22e9cff403c63d27a4f0ab1f3fa8567ac9","observation_id":"cf470dec-20dd-44cb-98d8-70153c7edb4a","resolution":{"observed_at":"2026-05-21T14:56:19.389633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2015 , eprint =","venue":null,"work_id":"d4baa965-c4cf-4ecf-8245-32e5a31cc9be","year":2015},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:59eae40cff69feb2164daea7908f95f5fba1c15639849bfdb6348395ca820fcd","observation_id":"32ecde0f-2ff0-48f7-af82-5432adb611fd","resolution":{"observed_at":"2026-05-21T14:56:19.391644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2020 , eprint=","venue":null,"work_id":"5f59e22a-45c3-41e2-9b5d-21966176cfd4","year":2020},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":104,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:602ab3c92830ff0a3995a426fa712c50cbd4c78897d812e7389913c0e49e7bf3","observation_id":"76ed3d08-9ae6-42c3-a08c-10e0e0678bcb","resolution":{"observed_at":"2026-05-21T14:56:19.393506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-07-09T10:16:09.900185Z","title":"2019 , eprint=","venue":null,"work_id":"3633bd7d-023e-430a-9558-beb21fdcd38b","year":2019},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:54df203c3d16337489cc05e191a8603beaeb8cc4dcb17e1104d1b25d25e70d4a","observation_id":"9bf429c2-f5e3-48ef-9095-7c133e976c60","resolution":{"observed_at":"2026-05-21T14:56:19.395415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Proceedings of the 6th Workshop on Representation Learning for NLP , year=","venue":null,"work_id":"83a5ddb1-7def-478e-b1f9-58f0f6941953","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:a69e6d3ac0594567ef14db13d4c311af7dd59111b96a6d263f0fd6e1512fa262","observation_id":"01026641-fc33-42da-8294-e3c213ba4c24","resolution":{"observed_at":"2026-05-21T14:56:19.397790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2018 , eprint=","venue":null,"work_id":"d0b71a8b-7931-4535-ad3f-b7ccd9a1d9ed","year":2018},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":107,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:ee68e5955b6f1e9a9a1fb9df8c0891463adfd49ab79660ea77aee88d86f3d8db","observation_id":"1ace12d1-3011-4793-8548-df4713c1fa2b","resolution":{"observed_at":"2026-05-21T14:56:19.399760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"Introducing embed V3 , url=","venue":null,"work_id":"a875f9d2-7cfd-4307-b1e7-0d83b0c22582","year":null},"citing_paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","version":2},"reference_index":108,"source":"arxiv_source","source_observed_at":"2026-05-21T14:56:19.122278Z"},"links":{"citing_paper":"/paper/2402.01613"},"observation_digest":"sha256:25de1951f5da54738750d87628dddd70cbdf5401c928843988bda5f2cd915156","observation_id":"c4a3de01-2107-4cd6-9a58-dfb2e9ae134e","resolution":{"observed_at":"2026-05-21T14:56:19.401716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2402.01613","last_updated":"2025-02-03T22:26:56Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T18:16:47.501613Z","submitted_at":"2024-02-02T18:23:18Z","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":5,"verified_fuzzy":91},"total_outbound_references":142},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 100 of 142 outbound references and 73 inbound Pith citation observations for arXiv:2402.01613."}