{"as_of":"2026-08-15T01:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7aff089124312669705623a5a1aaf9e2edcd7f38148f4da67023bbb2e75801c4","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":41,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:24:31.533325Z","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":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2405.17428","last_updated":"2025-02-25T00:35:18Z","snapshot_observed_at":"2026-08-14T08:11:36.232487Z","submitted_at":"2024-05-27T17:59:45Z","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-14T21:15:16.112918Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2405.17428"},"observation_digest":"sha256:c321df62dd8271d18779222f8702d613ca64af99c4a1a7a2015b84ae2bb97265","observation_id":"81a80292-58e2-41d4-a1eb-661b21b93488","resolution":{"observed_at":"2026-05-14T21:15:16.272036Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-12T17:24:31.533325Z","title":"Gabriel de Souza P Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12644","last_updated":"2025-08-08T04:35:26Z","snapshot_observed_at":"2026-08-14T07:15:24.440419Z","submitted_at":"2024-11-19T16:54:45Z","title":"CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:31.533325Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2411.12644"},"observation_digest":"sha256:647d2ac41c0b42cf72834379609e0d786b36a266d665505a741f0b3f10ef2b4c","observation_id":"8ae72314-0402-4410-beca-8afc89daffd4","resolution":{"observed_at":"2026-08-12T17:24:31.533325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-12T04:49:20.663007Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01007","last_updated":"2025-03-04T00:36:44Z","snapshot_observed_at":"2026-08-14T12:40:39.050297Z","submitted_at":"2024-12-01T23:54:12Z","title":"CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T04:49:20.663007Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2412.01007"},"observation_digest":"sha256:21f4e33d71dabd3f825fbbb1736295bd7b5f469d9305c38031e9bc731c2cc121","observation_id":"389093d5-9ce0-4eff-be99-87006eb64d34","resolution":{"observed_at":"2026-08-12T04:49:20.663007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-11T23:21:42.034842Z","title":"Nv- retriever: Improving text embedding models with effective hard-negative mining","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02592","last_updated":"2025-08-30T07:10:08Z","snapshot_observed_at":"2026-08-14T08:41:28.723012Z","submitted_at":"2024-12-03T17:23:47Z","title":"OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T23:21:42.034842Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2412.02592"},"observation_digest":"sha256:e2cf7a37913b3d2b18363feb1943df2e7c6a0e8dcfcda82f36cd728f4ce9d59a","observation_id":"06973556-c6f3-4e3f-b2fe-e17605bd06bc","resolution":{"observed_at":"2026-08-11T23:21:42.034842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-11T23:04:04.202870Z","title":"Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04506","last_updated":"2024-12-14T00:13:09Z","snapshot_observed_at":"2026-08-14T20:53:36.835268Z","submitted_at":"2024-12-03T22:59:36Z","title":"Arctic-Embed 2.0: Multilingual Retrieval Without Compromise","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T23:04:04.202870Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2412.04506"},"observation_digest":"sha256:ec3ca34ef57b377ea2e70df0778d47d5ac9bfa5efc3dbdfe0427bc0d01e8756c","observation_id":"19f94ed8-0294-41a5-89c4-e71eac7312aa","resolution":{"observed_at":"2026-08-11T23:04:04.202870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-11T13:59:01.798576Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.12591","last_updated":"2025-07-25T09:22:04Z","snapshot_observed_at":"2026-08-14T13:47:37.509193Z","submitted_at":"2024-12-17T06:48:24Z","title":"LLMs are Also Effective Embedding Models: An In-depth Overview","version":2},"reference_index":116,"source":"pdf_text","source_observed_at":"2026-08-11T13:59:01.798576Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2412.12591"},"observation_digest":"sha256:5f50a9d8b58156f56d5a9934c3f80a98254cbacdf5dd35214939ec247ed92b79","observation_id":"7cc42a85-7be8-416a-8b16-c6d911cb761a","resolution":{"observed_at":"2026-08-11T13:59:01.798576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-11T01:03:27.690479Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19048","last_updated":"2025-01-23T16:01:22Z","snapshot_observed_at":"2026-08-14T12:39:51.256172Z","submitted_at":"2024-12-26T04:05:28Z","title":"Jasper and Stella: distillation of SOTA embedding models","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T01:03:27.690479Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2412.19048"},"observation_digest":"sha256:60b33c567a0f56a0c392f4ed3da7021042d79167a45d0b1c66b72ac99717c7dd","observation_id":"e035c6c2-8154-4e0b-8539-d85b840f0aea","resolution":{"observed_at":"2026-08-11T01:03:27.690479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-10T22:49:13.438989Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00747","last_updated":"2025-01-01T06:33:45Z","snapshot_observed_at":"2026-08-13T20:39:38.935513Z","submitted_at":"2025-01-01T06:33:45Z","title":"DIVE: Diversified Iterative Self-Improvement","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:13.438989Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2501.00747"},"observation_digest":"sha256:f9f3c402acc4afe58cd1f0d98b30bfbb557859571270fae05fed88869c1822be","observation_id":"7969135d-66e8-4a9e-9995-4c7c43ed6abd","resolution":{"observed_at":"2026-08-10T22:49:13.438989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-09T20:48:29.934457Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.19264","last_updated":"2025-01-31T16:24:46Z","snapshot_observed_at":"2026-08-14T04:54:40.436024Z","submitted_at":"2025-01-31T16:24:46Z","title":"mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T20:48:29.934457Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2501.19264"},"observation_digest":"sha256:57f29b727294ddf9d7c541f65139cd989a5317564e9c9e630506c22a945ac0de","observation_id":"a2c2d423-fe1a-4447-a50b-f3dceafbe8c5","resolution":{"observed_at":"2026-08-09T20:48:29.934457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-08T22:58:25.578683Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04289","last_updated":"2025-02-07T19:23:50Z","snapshot_observed_at":"2026-08-13T20:32:18.434858Z","submitted_at":"2025-02-06T18:34:37Z","title":"Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T22:58:25.578683Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2502.04289"},"observation_digest":"sha256:c4b8c75524fa399baa08d5e0c13d26ab52f733863476ca2cb17a025fb1deb8ad","observation_id":"4c6dc59c-17f1-4e46-a810-55f12db8d282","resolution":{"observed_at":"2026-08-08T22:58:25.578683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T14:36:10.377760Z","title":"Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18366","last_updated":"2025-05-23T20:51:20Z","snapshot_observed_at":"2026-08-14T00:22:44.378122Z","submitted_at":"2025-05-23T20:51:20Z","title":"Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T14:36:10.377760Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2505.18366"},"observation_digest":"sha256:13dafc2a822b461f12c3c93ccf1e9b4495a15a553431ea7ea5488220c32126b5","observation_id":"ed8fe9bc-9d30-40c2-b4fa-54f7cef170e9","resolution":{"observed_at":"2026-08-07T14:36:10.377760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T14:23:40.517431Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.19274","last_updated":"2025-05-25T19:06:19Z","snapshot_observed_at":"2026-08-07T22:54:37.605449Z","submitted_at":"2025-05-25T19:06:19Z","title":"Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T14:23:40.517431Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2505.19274"},"observation_digest":"sha256:37e5e270a5a592097a0da365bafa598751d9680285a4f12024094260f20a6bbb","observation_id":"952bd28a-2c51-4d5d-a230-44436fcf8239","resolution":{"observed_at":"2026-08-07T14:23:40.517431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T14:18:34.892653Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19464","last_updated":"2025-05-26T03:37:17Z","snapshot_observed_at":"2026-08-10T21:23:26.423575Z","submitted_at":"2025-05-26T03:37:17Z","title":"LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T14:18:34.892653Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2505.19464"},"observation_digest":"sha256:386337b335f6390fced5aac84755b724119f520b988a0c9893a8da27bae59175","observation_id":"8d12d582-e9fe-45b2-9d56-99203e5a4c1f","resolution":{"observed_at":"2026-08-07T14:18:34.892653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T13:35:49.317115Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21439","last_updated":"2025-05-27T17:14:37Z","snapshot_observed_at":"2026-08-13T06:20:52.491989Z","submitted_at":"2025-05-27T17:14:37Z","title":"Towards Better Instruction Following Retrieval Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T13:35:49.317115Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2505.21439"},"observation_digest":"sha256:99ddec687f126aa7b471df53f0f029a98ab54f4a7b163446fc632b19e48f0ed4","observation_id":"5043b857-ed03-4b01-905a-2a2d63864142","resolution":{"observed_at":"2026-08-07T13:35:49.317115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T13:24:52.610720Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22019","last_updated":"2025-06-03T05:28:55Z","snapshot_observed_at":"2026-08-13T17:46:22.594743Z","submitted_at":"2025-05-28T06:30:51Z","title":"VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T13:24:52.610720Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2505.22019"},"observation_digest":"sha256:e05a4cacb3b9619abf642bc37b0468b067281de7e790d8607b060e404eef2dae","observation_id":"4c8c0d4e-b367-4663-93eb-1233a01ea3dd","resolution":{"observed_at":"2026-08-07T13:24:52.610720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T13:08:54.972013Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22584","last_updated":"2025-05-28T16:56:41Z","snapshot_observed_at":"2026-08-13T01:49:52.632752Z","submitted_at":"2025-05-28T16:56:41Z","title":"DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T13:08:54.972013Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2505.22584"},"observation_digest":"sha256:c380abd8a61f8aad719c5735ceb8a6161e5eea18ac382e2388150a23aab11867","observation_id":"f4e134b9-ead7-4762-9194-b70386c5198f","resolution":{"observed_at":"2026-08-07T13:08:54.972013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T11:49:10.489385Z","title":"Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","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":17,"source":"arxiv_source","source_observed_at":"2026-08-07T11:49:10.489385Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2506.01435"},"observation_digest":"sha256:05bbcba798c7b1b976bd95e713c26bdb447dc538261806106d63d667478463c3","observation_id":"f5999f1d-be40-4e91-92d1-7ab342cdcb7d","resolution":{"observed_at":"2026-08-07T11:49:10.489385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T05:40:14.613578Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining.arXiv preprint arXiv:2407.15831, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07438","last_updated":"2025-06-22T05:33:06Z","snapshot_observed_at":"2026-08-14T12:39:53.339494Z","submitted_at":"2025-06-09T05:30:35Z","title":"LGAI-EMBEDDING-Preview Technical Report","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:40:14.613578Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2506.07438"},"observation_digest":"sha256:320af47ebbc8d775eba6091849a248e0f9cca480ac8309895e525987d1446cbb","observation_id":"c8abb780-f81e-475e-8322-58fcb101fc94","resolution":{"observed_at":"2026-08-07T05:40:14.613578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-07T05:18:26.492186Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.08354","last_updated":"2026-05-28T08:13:47Z","snapshot_observed_at":"2026-08-10T06:36:57.892160Z","submitted_at":"2025-06-10T02:11:42Z","title":"Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T05:18:26.492186Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2506.08354"},"observation_digest":"sha256:2af7eb7a7a7dd2d074632a5e52a8aea5a3e571606edf4405f171e1bfaa8be463","observation_id":"3aa69b84-24ce-485e-ab82-5f8e226b508c","resolution":{"observed_at":"2026-08-07T05:18:26.492186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-06T19:30:13.857779Z","title":"Nv-retriever: Improving text embedding models with effective hard- negative mining.arXiv preprint arXiv:2407.15831, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05513","last_updated":"2025-07-07T22:20:04Z","snapshot_observed_at":"2026-08-09T00:55:25.952612Z","submitted_at":"2025-07-07T22:20:04Z","title":"Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:13.857779Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2507.05513"},"observation_digest":"sha256:4c23640010f8b2f7591f45ca6b53c47b037f653b6bf805acee0a160c557a4edc","observation_id":"d0078f67-5d0e-4c8b-b65b-9ceda893262f","resolution":{"observed_at":"2026-08-06T19:30:13.857779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2507.08480","last_updated":"2026-05-19T10:50:22Z","snapshot_observed_at":"2026-08-14T12:24:46.845465Z","submitted_at":"2025-07-11T10:44:09Z","title":"Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-22T00:39:24.748381Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2507.08480"},"observation_digest":"sha256:960502e7b493b0f61c3d123f79010c9c51b11ceb470381e59658bdeff7d0ec3e","observation_id":"836ffbc9-c16d-4d12-9079-5be9be52b12a","resolution":{"observed_at":"2026-05-22T00:40:50.988161Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","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":8,"source":"pdf_text","source_observed_at":"2026-05-19T00:44:07.893905Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2508.01959"},"observation_digest":"sha256:31644bcf734b0f5f464017a9fe5e29418f9853df26440a0d33e71c5e54c57e93","observation_id":"0566f061-6446-4f5f-b2fe-f0a90e6c6e94","resolution":{"observed_at":"2026-05-19T00:46:56.292651Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T22:36:59.852304Z","title":"Gabriel de Souza P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.06781","last_updated":"2025-08-09T02:15:17Z","snapshot_observed_at":"2026-08-14T10:49:17.413066Z","submitted_at":"2025-08-09T02:15:17Z","title":"BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-05T22:36:59.852304Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2508.06781"},"observation_digest":"sha256:4e7e72c3df814e6140e641546e686c4d01583c62c54c8fc6ebc885383b1b7f7d","observation_id":"981f691a-cd2b-4384-99b3-32be92be1f31","resolution":{"observed_at":"2026-08-05T22:36:59.852304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T14:11:09.770786Z","title":"”NV-Retriever: Improving text emb edding models with eﬀective hard-negative mining.” arXiv preprint arXiv:2407.15831 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21632","last_updated":"2025-08-29T13:47:22Z","snapshot_observed_at":"2026-08-06T10:11:40.323413Z","submitted_at":"2025-08-29T13:47:22Z","title":"QZhou-Embedding Technical Report","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T14:11:09.770786Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2508.21632"},"observation_digest":"sha256:0b9abff45a6a683c85a8b561e76a9820157ba7f0b1e88b62c06f5dc44c954285","observation_id":"17191518-9d7a-4ec5-b2ee-892c3d85825e","resolution":{"observed_at":"2026-08-05T14:11:09.770786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-04T19:07:21.385485Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09459","last_updated":"2025-09-11T13:42:50Z","snapshot_observed_at":"2026-08-06T03:21:44.837514Z","submitted_at":"2025-09-11T13:42:50Z","title":"Boosting Data Utilization for Multilingual Dense Retrieval","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-04T19:07:21.385485Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2509.09459"},"observation_digest":"sha256:528911210dc4cc1c4492dcd920c1846a5ca5cbbabf0671b5dc410e60bf8a60f4","observation_id":"53ed72a4-b2d7-4353-a500-e32a31bc84be","resolution":{"observed_at":"2026-08-04T19:07:21.385485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2509.20354","last_updated":"2025-11-01T23:38:27Z","snapshot_observed_at":"2026-08-14T12:38:29.847254Z","submitted_at":"2025-09-24T17:56:51Z","title":"EmbeddingGemma: Powerful and Lightweight Text Representations","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T12:07:20.946370Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2509.20354"},"observation_digest":"sha256:78d8779bb8a882332107fa1272349be4813c39e4acbc05b3b093d0459e5805d9","observation_id":"07d77da2-bc7f-4bbb-bebb-40536718098c","resolution":{"observed_at":"2026-05-15T12:07:20.981272Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2604.08649","last_updated":"2026-04-09T18:00:00Z","snapshot_observed_at":"2026-08-11T02:05:39.115735Z","submitted_at":"2026-04-09T18:00:00Z","title":"PRAGMA: Revolut Foundation Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T17:37:04.381074Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2604.08649"},"observation_digest":"sha256:c724824ec1f1d115e8a0995e055006e65f661e98af22e4d3eaadb1cb1af42b39","observation_id":"18b65a53-5e44-4c41-9285-2a563018490c","resolution":{"observed_at":"2026-05-11T06:30:59.057731Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2604.08649","last_updated":"2026-04-09T18:00:00Z","snapshot_observed_at":"2026-08-11T02:05:39.115735Z","submitted_at":"2026-04-09T18:00:00Z","title":"PRAGMA: Revolut Foundation Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T17:37:04.381074Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2604.08649"},"observation_digest":"sha256:825441fd9022f294abe810015101ee0a741523b50a011c3e08b6e1c3aec892b6","observation_id":"78e4f2f7-164c-4698-9a96-cd31b573a0a5","resolution":{"observed_at":"2026-05-10T17:40:40.602308Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2604.11092","last_updated":"2026-04-13T07:11:30Z","snapshot_observed_at":"2026-08-11T23:21:07.870602Z","submitted_at":"2026-04-13T07:11:30Z","title":"ARHN: Answer-Centric Relabeling of Hard Negatives with Open-Source LLMs for Dense Retrieval","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T16:10:43.392191Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2604.11092"},"observation_digest":"sha256:d18e38810c17a27d1b8453f3ce2f4feb1fbb5d25533e1592c6ef87440ba8fc3b","observation_id":"022da305-5637-4aaf-b3e6-09cd0080a01b","resolution":{"observed_at":"2026-05-11T09:11:12.415411Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2604.15484","last_updated":"2026-04-16T19:22:58Z","snapshot_observed_at":"2026-08-11T12:07:56.806696Z","submitted_at":"2026-04-16T19:22:58Z","title":"vstash: Local-First Hybrid Retrieval with Adaptive Fusion for LLM Agents","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T09:48:02.455490Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2604.15484"},"observation_digest":"sha256:da123af4c9253d5b40030a50ee6f7bb81feaf7d9e562de6d6721588358a54ea9","observation_id":"1e2f48b2-0454-4e23-9fd3-7b1cb4b0cf83","resolution":{"observed_at":"2026-05-10T09:48:47.573769Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2604.16576","last_updated":"2026-04-17T13:02:29Z","snapshot_observed_at":"2026-08-12T18:52:00.851639Z","submitted_at":"2026-04-17T13:02:29Z","title":"On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T07:52:12.824157Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2604.16576"},"observation_digest":"sha256:0a6c2efbff425f4132d1e1468a1e2b3a3999d534e06dfaeccc574d9be1a6ca97","observation_id":"e75e522d-30c3-4c02-8daf-7495de4a1533","resolution":{"observed_at":"2026-05-10T09:18:32.448339Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2605.00353","last_updated":"2026-05-01T02:32:02Z","snapshot_observed_at":"2026-08-14T01:21:32.067213Z","submitted_at":"2026-05-01T02:32:02Z","title":"Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T19:18:35.441181Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2605.00353"},"observation_digest":"sha256:5b6721355507d4e365f9f52dc61d51e01b751c701e8c6f008f871873c4f36726","observation_id":"6a340bdf-cf52-4796-b609-e816fcec53c1","resolution":{"observed_at":"2026-05-11T15:46:37.708360Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2605.07249","last_updated":"2026-05-08T05:10:05Z","snapshot_observed_at":"2026-08-12T21:27:33.939531Z","submitted_at":"2026-05-08T05:10:05Z","title":"MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-11T02:33:25.462269Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2605.07249"},"observation_digest":"sha256:c06edbe5c08b37e96eb5308bdde2c5da28f62ba5053c5568417a8a3911ee6e6e","observation_id":"63e89219-9978-4755-a8b5-31d151538a6e","resolution":{"observed_at":"2026-05-11T03:15:56.391131Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2606.00610","last_updated":"2026-05-30T08:18:53Z","snapshot_observed_at":"2026-08-14T14:55:36.035071Z","submitted_at":"2026-05-30T08:18:53Z","title":"MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T18:20:43.092561Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2606.00610"},"observation_digest":"sha256:c08dc4ddc3e06b6002eabc5a0c6088fa391c193118d7bb7d536de41b1e08cfdc","observation_id":"bb4b2c6a-3a05-4e48-a1c5-504ff4dcc0ee","resolution":{"observed_at":"2026-06-28T20:42:37.979181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2606.04300","last_updated":"2026-06-03T00:08:44Z","snapshot_observed_at":"2026-08-13T01:39:01.449795Z","submitted_at":"2026-06-03T00:08:44Z","title":"Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-06-28T04:55:04.299049Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2606.04300"},"observation_digest":"sha256:bb195de80029625f3e84db143a2f3f3929bede6b74e4847ca4b4e707933d7726","observation_id":"ec613387-82c1-4370-ad3c-eba66e6a79bd","resolution":{"observed_at":"2026-07-02T10:46:52.616225Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2606.20369","last_updated":"2026-06-18T15:32:14Z","snapshot_observed_at":"2026-07-30T09:53:42.916716Z","submitted_at":"2026-06-18T15:32:14Z","title":"CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-06-26T17:30:07.053955Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2606.20369"},"observation_digest":"sha256:6f19b2cce217169789a84ec443fd51e14b8b38a16618821604bf2f8709502f32","observation_id":"ab4db2e6-78f7-4e68-a1a7-d043af9a2a49","resolution":{"observed_at":"2026-07-04T03:59:32.645781Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":"2407.15831","doi":"10.48550/arxiv.2407.15831","metadata_source":"pith","pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nv-retriever: Improving text embedding models with effective hard-negative mining","venue":"cs.IR","work_id":"4191e8bf-2d4c-4abf-aba3-948e3a5a2e46","year":2024},"citing_paper":{"arxiv_id":"2607.06036","last_updated":"2026-07-07T09:12:46Z","snapshot_observed_at":"2026-08-06T19:58:30.651720Z","submitted_at":"2026-07-07T09:12:46Z","title":"Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-08T18:45:34.929157Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2607.06036"},"observation_digest":"sha256:a6ac6bfd5766d4113dff37f635c8a82996f63ee5db06cdac27ed5aa50759499d","observation_id":"6b9bdfa6-9a43-4fee-a453-a0e99124cfda","resolution":{"observed_at":"2026-07-08T18:55:28.224229Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-01T07:59:44.458007Z","title":"Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21274","last_updated":"2026-07-23T12:51:55Z","snapshot_observed_at":"2026-08-09T03:35:22.481408Z","submitted_at":"2026-07-23T12:51:55Z","title":"A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T07:59:44.458007Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2607.21274"},"observation_digest":"sha256:1ce98d7ca362e6a9c6aa78fc200d9165a6a5a4f1a96e2bcfae73fcc2f8033cc1","observation_id":"ec26faaf-9f77-4e32-8b97-3f59fb0b7a1c","resolution":{"observed_at":"2026-08-01T07:59:44.458007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-07-30T11:17:16.871040Z","title":"Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.27178","last_updated":"2026-07-31T14:35:24Z","snapshot_observed_at":"2026-08-05T23:14:53.018277Z","submitted_at":"2026-07-29T17:50:51Z","title":"DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search","version":1},"reference_index":128,"source":"arxiv_source","source_observed_at":"2026-07-30T11:17:16.871040Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2607.27178"},"observation_digest":"sha256:da9396b6206986089dbde04185730e895408b52f46039c9e716320e9e69e903f","observation_id":"308e440f-f577-4c79-918f-a7d451052064","resolution":{"observed_at":"2026-07-30T11:17:16.871040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-03T01:43:07.098063Z","title":"Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.27178","last_updated":"2026-07-31T14:35:24Z","snapshot_observed_at":"2026-08-05T23:14:53.018277Z","submitted_at":"2026-07-29T17:50:51Z","title":"DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-03T01:43:07.098063Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2607.27178"},"observation_digest":"sha256:41abe6221e337773a8b4bacdd1abda50ea8c612eae61048842d03a452cedfe65","observation_id":"8e7eaa0c-2550-44f2-8481-5cae4481c252","resolution":{"observed_at":"2026-08-03T01:43:07.098063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15831","snapshot_observed_at":"2026-08-06T00:17:09.248864Z","title":"arXiv preprint arXiv:2407.15831 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.01389","last_updated":"2026-08-02T17:11:09Z","snapshot_observed_at":"2026-08-13T06:43:53.466463Z","submitted_at":"2026-08-02T17:11:09Z","title":"KoVRE: Training an Efficient Embedding Model for Korean Visual Document Retrieval","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T00:17:09.248864Z"},"links":{"cited_paper":"/paper/2407.15831","citing_paper":"/paper/2608.01389"},"observation_digest":"sha256:abdeac47f9b61ae61970270e4510b0134a11bb190e019431e5087c7cf139f327","observation_id":"e65ee079-6b4e-491f-ab4c-e4a9b03001c6","resolution":{"observed_at":"2026-08-06T00:17:09.248864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.15831/citation-record","integrity":"/paper/2407.15831/integrity","json":"/paper/2407.15831/citation-record.json","paper":"/paper/2407.15831"},"outbound":[],"paper":{"arxiv_id":"2407.15831","last_updated":"2025-02-07T15:17:18Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-12T23:17:05.684103Z","submitted_at":"2024-07-22T17:50:31Z","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2407.15831."}