{"as_of":"2026-08-05T15:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38c5f2f78ccc150b7b2558a8543dfdfa83881960c1ca855d0e4467beda828caa","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T21:46:02.002132Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2604.21889/citation-record","integrity":"/paper/2604.21889/integrity","json":"/paper/2604.21889/citation-record.json","paper":"/paper/2604.21889"},"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":"2025 , url =","venue":null,"work_id":"b8a6966b-626f-4ae0-a5cb-d46d1dd9876f","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:e0432357d602ee1ff36a9bcdc6168933f2d0f2e15c3027a0ee74601a9a199855","observation_id":"03f5c014-97ff-4d8b-8f0f-060ecc612e79","resolution":{"observed_at":"2026-05-23T15:35:41.857124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2025 , url =","venue":null,"work_id":"50b17b6c-5072-4727-bcbe-62de5f25553c","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:76a2579e6c758f5023a200854d5b94cb5e1ca95f6a55b219c7ef286a8f8f44e4","observation_id":"713274b3-16bd-434c-9bd1-73e20e1a38d8","resolution":{"observed_at":"2026-05-23T15:35:41.860688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/1.9781611972764.29","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Density-Based Clustering over an Evolving Data Stream with Noise , booktitle =","venue":null,"work_id":"4069472c-bb1b-437c-9990-ba3174849c26","year":2006},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:50841dab9efd9e69c60c33c7f5b2f3f644f5ee9900717572df95ee87131f6081","observation_id":"a1b09305-b670-4a23-83a5-fe4ef2122ab5","resolution":{"observed_at":"2026-05-09T21:48:36.579856Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/b978-012722442-8/50016-1","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Aggarwal, Philip S","venue":"Elsevier eBooks","work_id":"474b1d84-0adb-4ce1-b1f4-f9538638b365","year":2003},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:28a05a5e04dc2da3feafd5531935f26b0b4fb5e567a68ad9c03d7d3bab6c559e","observation_id":"49db730f-0e44-4cfe-9783-279d442f223b","resolution":{"observed_at":"2026-05-09T21:48:36.563585Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2500.333068","doi":"10.1145/3292500.3330680","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Available: https://doi.org/10.1145/3292500.3330680","venue":null,"work_id":"bb4de18d-e712-416f-aa25-6765e314ebe9","year":2019},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:110eeb4b9ec7b03c288d960d0e5543e474735b065688290517435150123010a8","observation_id":"3ae6fc7f-fc82-4df1-8b4c-6270f1800c61","resolution":{"observed_at":"2026-05-09T21:48:36.587589Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10994-021-05988-7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Embed2Detect: temporally clustered embedded words for event detection in social media , journal =","venue":"Machine Learning","work_id":"e07cb3ef-22ca-4103-bfd2-805c8a118470","year":2022},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:4ba813e9d077ce5eccc47d6af5f8e05aabb8dbf5f928b62499e0ac21b70c51ba","observation_id":"17c08b84-5f89-45b5-bbb6-fc798129d0e3","resolution":{"observed_at":"2026-05-09T21:48:36.570995Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2021.eacl-main.198","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"McKeown , editor =","venue":null,"work_id":"c16a0cd2-ea66-4a34-9bcd-0c2412e64ea8","year":2021},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:5c11c5ef9bad3b70abb54df0c61fdd373dc264dc7ee1e2817c708f16bff511cb","observation_id":"e0d0dfeb-3043-4134-939b-c7952f88abd5","resolution":{"observed_at":"2026-05-09T21:48:36.551026Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03172","last_updated":"2023-11-20T23:09:34Z","snapshot_observed_at":"2026-07-06T15:51:12.179086Z","submitted_at":"2023-07-06T17:54:11Z","title":"Lost in the Middle: How Language Models Use Long Contexts","version":3},"cited_work":{"arxiv_id":"2307.03172","doi":"10.1162/tacl","metadata_source":"pith","pith_arxiv_id":"2307.03172","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lost in the Middle: How Language Models Use Long Contexts","venue":"cs.CL","work_id":"37c05e13-4a24-44f8-a1c4-da1bbe7223aa","year":2023},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"cited_paper":"/paper/2307.03172","citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:b35078798986a21a3c31f2f8c981cae336c7646b0f1b10c22aa9f83b029ca1fc","observation_id":"eb8a72c7-3b15-4f2c-8651-53b6a5d58501","resolution":{"observed_at":"2026-05-09T21:48:36.568490Z","resolver_source":"doi","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ijcce.2024.11.004","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Matos-Carvalho and Nuno Fachada , keywords =","venue":"International Journal of Cognitive Computing in Engineering","work_id":"b1cac6b9-711e-4bef-be97-16e3ba9c0bd5","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:ab94e004d9486673b8c592cfd71f26eb1ea49992c8298b7d37a1f495412f5ab8","observation_id":"e506d055-b85d-44c1-905c-46e426892679","resolution":{"observed_at":"2026-05-09T21:48:36.577240Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-23T03:53:04.959819+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T03:53:04.959819+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7695.376951","doi":"10.1145/3767695.3769519","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region , pages =","venue":null,"work_id":"000899b4-9fd8-4f15-abd3-bb6a7d77e41d","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:035e7be7a3d15879f6e0e2f882be267301f75c4c072fdf530ee647186e40175a","observation_id":"2f5af798-7605-4051-a65a-a616ce1a211a","resolution":{"observed_at":"2026-05-09T21:48:36.574773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-21T08:52:41.606831+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T08:52:41.606831+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00988","last_updated":"2024-05-02T03:50:31Z","snapshot_observed_at":"2026-07-06T18:08:35.173140Z","submitted_at":"2024-05-02T03:50:31Z","title":"Context-Aware Clustering using Large Language Models","version":1},"cited_work":{"arxiv_id":"2405.00988","doi":"10.48550/arxiv.2405.00988","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.00988","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ioannidis and Changhe Yuan and Chandan K","venue":"arXiv (Cornell University)","work_id":"ceb11166-1ea7-4735-b95e-8e7f90ae713a","year":2024},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"cited_paper":"/paper/2405.00988","citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:38d1a68bb0e04f419dac2a10f60c0f0b96cbb751c31ba2e11e007a14efb39cc9","observation_id":"f311f3a3-cccd-4b2e-80d7-a3c507387065","resolution":{"observed_at":"2026-05-09T21:48:36.561116Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.08466","doi":"10.48550/arxiv.2510.08466","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CoRR , volume =","venue":"ArXiv.org","work_id":"a3069b52-61ec-4d9d-a6b7-fd78f0d39e8b","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:54c05bc0f0afff36d9c4ab077e1b128d7738e485ed7d1c0346c67c95489c0ec0","observation_id":"8b23d43b-bcf4-4574-ab7c-d130987a66ab","resolution":{"observed_at":"2026-05-09T21:48:36.583444Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1155/2009/837601","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ghorbani","venue":"EURASIP Journal on Advances in Signal Processing","work_id":"da9b45e0-0779-44f8-91ab-a28c0f21cca7","year":2009},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:90e75f2541d9ddb26c319fe04ef2c9a00b6cc72919fdedaeb725049ab10aa434","observation_id":"a64a68fb-5794-43f0-a859-d353b151b6b2","resolution":{"observed_at":"2026-05-09T21:48:36.590039Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-642-04394-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rokne , editor =","venue":"Lecture notes in computer science","work_id":"197994b9-55bb-42fc-aa2a-55ca26a0381d","year":2009},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:fbe32dbfeb05eb918e14399a4989db29e20935253425712291ab77e3259ad9c9","observation_id":"96d0d6e4-7376-4bb7-9543-ab0de658b01e","resolution":{"observed_at":"2026-05-09T21:48:36.548959Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2500.333068","doi":"10.1145/3292500.3330680","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Available: https://doi.org/10.1145/3292500.3330680","venue":null,"work_id":"bb4de18d-e712-416f-aa25-6765e314ebe9","year":2019},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:584ce561fbee927089bb2c7208c2c24ae3bfbc9baae14ac74f78f275464ce8f6","observation_id":"d79ba294-4a8e-4772-80e2-84dd4a8edbdd","resolution":{"observed_at":"2026-05-09T21:48:36.557286Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1115/1.4048960","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"title =","venue":"Journal of Mechanical Design","work_id":"e1c86c5d-1b25-418f-b469-7e5cbb1bbf60","year":2021},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:6ed0faf110599208e55571934de6e2436d01059c4a38a43c619c2f50a90ed330","observation_id":"7f299596-6631-44ce-871e-6c8a015c4365","resolution":{"observed_at":"2026-05-09T21:48:36.565784Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2020.emnlp-main.550","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T20:05:33.949387Z","title":"Dense Passage Retrieval for Open-Domain Question Answering","venue":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","work_id":"083391f8-812d-430f-8d08-89a03031ce6c","year":2020},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:1869e18846984289bf3fb91c177658f71a64048037e17c9bbdf55d9c02b2d501","observation_id":"1ab32ca6-0fce-4769-a6cd-4e593e810bbf","resolution":{"observed_at":"2026-05-09T21:48:36.553361Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-01T13:38:13.70004+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T13:38:13.70004+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7983.309801","doi":"10.1145/3097983.3098011","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proceedings of the 23rd","venue":null,"work_id":"35dadaf0-58a6-4a7a-82b3-b397abaad18b","year":2017},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:7e367804c24801cfd0f8cfcee49c57a0accf12f95d80c6ba24e62cd5ca05238f","observation_id":"0de85334-c982-46f8-8b7d-41d887b28ce6","resolution":{"observed_at":"2026-05-09T21:48:36.599969Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.02097","last_updated":"2025-06-25T07:18:47Z","snapshot_observed_at":"2026-07-06T21:35:17.779740Z","submitted_at":"2025-06-02T17:59:27Z","title":"Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation","version":2},"cited_work":{"arxiv_id":"2506.02097","doi":"10.48550/arxiv.2506.02097","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.02097","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"CoRR , volume =","venue":"ArXiv.org","work_id":"2d0a194b-9c98-4f8d-9ab7-0d736392b1c5","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"cited_paper":"/paper/2506.02097","citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:3b897640407ca6340a8a36c0c1d678c2984f359d140674805c36832f6f321b58","observation_id":"a2e2c3c8-3819-4620-bb27-bea4987c2633","resolution":{"observed_at":"2026-05-09T21:48:36.596222Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":"2505.09388","doi":"10.1016/j.aiopen.2022.12","metadata_source":"pith","pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Technical Report","venue":"cs.CL","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:ec576e52cb82f9078bd7ef40cd756604578be2956e1e6536c56d39983a5b5e6d","observation_id":"91acdf87-e253-4dc0-b998-a8048bb63799","resolution":{"observed_at":"2026-05-09T21:48:36.606739Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.findings-acl.137","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T19:17:17.447107Z","title":"M 3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation","venue":"Findings of the Association for Computational Linguistics ACL 2024","work_id":"6f249b44-4b0a-47dd-b5f4-1877fd9269ad","year":2024},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:94d667eb7f7888c0e9edc07790098949613475c9f258cd3e40e58a85c8ff4471","observation_id":"1df9062f-e9e4-43f0-b92e-bfd7dcf0adf1","resolution":{"observed_at":"2026-05-09T21:48:36.602858Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-12T15:19:19.048621+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T15:19:19.048621+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.20534","last_updated":"2026-02-03T04:57:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-28T05:35:43Z","title":"Kimi K2: Open Agentic Intelligence","version":2},"cited_work":{"arxiv_id":"2507.20534","doi":"10.1145/3448609","metadata_source":"pith","pith_arxiv_id":"2507.20534","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kimi K2: Open Agentic Intelligence","venue":"cs.LG","work_id":"7f18284c-12d3-4137-bea1-1da97e8cf3c1","year":2025},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"cited_paper":"/paper/2507.20534","citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:5913bc8644552e44bd87a8e30d0242bb876e3e48059e62f60281edabb522e96b","observation_id":"00956387-e982-48c3-abc8-7eb6b4e761da","resolution":{"observed_at":"2026-05-10T17:49:28.234076Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-25T01:23:16.170083+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T01:23:16.170083+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.acl-long.791","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"D 2 LLM : Decomposed and Distilled Large Language Models for Semantic Search","venue":null,"work_id":"0a4edebb-69d0-4ea3-9b58-d23fd0b32e3a","year":2024},"citing_paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-09T21:46:02.002132Z"},"links":{"citing_paper":"/paper/2604.21889"},"observation_digest":"sha256:9b988fe16d3406a81b616fc6bca7a2fd974e02b2f6363c723a1fa58f7681be81","observation_id":"809d65cb-ae5f-47a3-8b4b-3eb6ac343acf","resolution":{"observed_at":"2026-05-09T21:48:36.592551Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.21889","last_updated":"2026-04-23T17:40:45Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T23:08:23.939574Z","submitted_at":"2026-04-23T17:40:45Z","title":"TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":1,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":0,"verified_exact":16,"verified_fuzzy":2},"total_outbound_references":23},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2604.21889."}