{"as_of":"2026-08-11T14:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d3e9ef8e53946ddf035a473f75a4405e3cb540c8664639a5738edf27127675d7","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:09:47.514938Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2501.06121/citation-record","integrity":"/paper/2501.06121/integrity","json":"/paper/2501.06121/citation-record.json","paper":"/paper/2501.06121"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:48.061859Z","title":"Information Systems 87 (02 2019), https://github.com/erikbern/ann-benchmarks","venue":null,"work_id":"018efb56-75d2-4848-be18-4155431e8d4e","year":2019},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.411926Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:cd6198e1c0008c4d85ff3700ad3ea702fc8455e49e721833a1a8841e479a5123","observation_id":"9605066d-9cbf-4042-a1e3-82c670226c8f","resolution":{"observed_at":"2026-08-10T21:09:48.068059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:48.044359Z","title":"In: Brisaboa, N.R., Pedreira, O., Zezula, P","venue":null,"work_id":"9bda0a39-2532-4769-bf58-1bcb51330e6f","year":2013},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.420274Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:4c540dd5f579dc5e8caac3182e01bc5f80c2d3b1695d9489911b3c6cc0b890e9","observation_id":"04078b9e-afcc-4476-a155-02ed8dac4a94","resolution":{"observed_at":"2026-08-10T21:09:48.049629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:48.027187Z","title":"In: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2024), https://api.semanticscholar.org/CorpusID:269449081","venue":null,"work_id":"67ac257d-aa6b-4bcf-9724-bfba1409fb54","year":2024},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.431704Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:75502020750b13eef5aaee9ab2a83d6eef952515b5dc7dadb2f66d6aa976b0be","observation_id":"d319e9a6-7c51-438f-a9f5-5e355de8e661","resolution":{"observed_at":"2026-08-10T21:09:48.032049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08281","last_updated":"2025-10-23T09:36:08Z","snapshot_observed_at":"2026-07-31T05:45:37.385210Z","submitted_at":"2024-01-16T11:12:36Z","title":"The Faiss library","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08281","snapshot_observed_at":"2026-08-10T21:09:47.436965Z","title":"arXiv preprint arXiv:2401.08281 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.436965Z"},"links":{"cited_paper":"/paper/2401.08281","citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:c27eb1ee4c43af055879f60411a992741507c775580018193acbb54cd5ff87c8","observation_id":"d9f182a9-fa3a-49d9-a639-a6df71210d10","resolution":{"observed_at":"2026-08-10T21:09:47.436965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.442303Z","title":"In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.442303Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:19cee1efba088b849f0d2e67cf81b8ecc6cd6de3569a8f6e43a295b6b8acb210","observation_id":"6ab3b930-bf2c-4451-9975-44fa4ddb84ab","resolution":{"observed_at":"2026-08-10T21:09:47.442303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:48.010278Z","title":"https://github.com/Leslie-Chung/GrassRMA","venue":null,"work_id":"59764a97-a526-4aac-bd35-c53fac17183d","year":null},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.448811Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:88de5bad40ea7fa488dd36f52c051f4a56e35778869ada41bd7202f12d7f72d8","observation_id":"7d363e19-65e3-421f-b391-e9f36deed869","resolution":{"observed_at":"2026-08-10T21:09:48.014904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10396","last_updated":"2020-12-04T21:29:31Z","snapshot_observed_at":"2026-08-10T18:49:56.141270Z","submitted_at":"2019-08-27T18:27:17Z","title":"Accelerating Large-Scale Inference with Anisotropic Vector Quantization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10396","snapshot_observed_at":"2026-08-10T21:09:47.453644Z","title":"In: International Conference on Machine Learning (2020),https://arxiv.org/abs/1908.10396","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.453644Z"},"links":{"cited_paper":"/paper/1908.10396","citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:c5388383e7fdac02d3ff7e1494c2fbb138ad9e20654b4c38b1c7316822028647","observation_id":"03640390-7a1a-48f8-a380-ecbde7ba3e72","resolution":{"observed_at":"2026-08-10T21:09:47.453644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.460069Z","title":"IEEE transactions on pattern analysis and machine intelligence33, 117–28 (01 2011)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.460069Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:50f2dbe9b3e44c195263a269b02259ba5777b403e2ced8fbb1c334572b6d797b","observation_id":"c4e8b592-49e2-4856-8e4d-96af103b94a2","resolution":{"observed_at":"2026-08-10T21:09:47.460069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07452","last_updated":"2023-02-15T03:53:26Z","snapshot_observed_at":"2026-08-05T15:37:43.888534Z","submitted_at":"2023-02-15T03:53:26Z","title":"How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07452","snapshot_observed_at":"2026-08-10T21:09:47.465569Z","title":"CoRR abs/2302.07452 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.465569Z"},"links":{"cited_paper":"/paper/2302.07452","citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:4a450ea6f042cd2b3ce603732345cdde1b3cae0159a56f0493a0bef915419185","observation_id":"80ac47dd-dd59-4164-9e9b-759fc6937fea","resolution":{"observed_at":"2026-08-10T21:09:47.465569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.470767Z","title":"In: Proceedings of the Seventh IEEE International Conference on Computer Vision","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.470767Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:d3ec9814e4bf601e8d2592d5ec4200f00efdf50a37a82cb92669bc2fa47b6a4a","observation_id":"5f4c4b20-473b-41b8-87f0-14df3d9a64ba","resolution":{"observed_at":"2026-08-10T21:09:47.470767Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.475675Z","title":"IEEE Trans","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.475675Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:5ef5f1fdc09145c6c20d8b394597227c07c94e9b9dbe2b9f51c52bb4464e1338","observation_id":"55ab93dd-85d1-4c97-b184-2e8b52948b52","resolution":{"observed_at":"2026-08-10T21:09:47.475675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.992631Z","title":"https://github.com/kakao/n2","venue":null,"work_id":"e2ca1ff0-a2d4-4319-807b-e40991018c95","year":null},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.480463Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:f073a4828d174bfb75e9b6dfa3ddc3510fe56a41230536a6baa9f6a4f85c0917","observation_id":"5249dd34-3ee7-4609-b7d0-0fa29f0eee6b","resolution":{"observed_at":"2026-08-10T21:09:47.998062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.976548Z","title":"In: Besold, T.R., Bordes, A., d’Avila Garcez, A.S., Wayne, G","venue":null,"work_id":"0afcd79b-8666-4f5d-ae78-ebbfd3bb1259","year":2016},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.485469Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:487707df204b69d6db5cffa6c0f698137be7d6f0984d8568809cb25324e20050","observation_id":"4a7563e4-a5d3-44ae-a59b-579455c99587","resolution":{"observed_at":"2026-08-10T21:09:47.981514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.942409Z","title":"https://github.com/veaaaab/pyanns","venue":null,"work_id":"23c70330-c5ec-4119-989c-9d062e849a3c","year":null},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.495445Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:9352883691c4a5107c3f917973cd5e6979eb94287e099e8d8ba465c9b3e333c1","observation_id":"5d39e404-5c8d-4b25-939a-b80f56ac3cb4","resolution":{"observed_at":"2026-08-10T21:09:47.949245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.925662Z","title":null,"venue":null,"work_id":"99429dc1-a9f6-4b5a-9262-efdb056c8772","year":2024},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.500602Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:955c3bd12ea433210de66acceb179cf9c1a6f555a042f12f140d19b297e56ae7","observation_id":"26370663-7354-4192-a4fa-9460b2b1ae96","resolution":{"observed_at":"2026-08-10T21:09:47.930732Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.505647Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.505647Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:ae283cd96c98b3ae955c7f8016c37722f2dbba5f6ba0a162769bc40ed008078d","observation_id":"c86342a9-5253-4f47-a85d-2aa2ec71eecf","resolution":{"observed_at":"2026-08-10T21:09:47.505647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00774","last_updated":"2024-03-31T19:09:09Z","snapshot_observed_at":"2026-08-11T03:30:08.235301Z","submitted_at":"2024-03-31T19:09:09Z","title":"SOAR: Improved Indexing for Approximate Nearest Neighbor Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00774","snapshot_observed_at":"2026-08-10T21:09:47.510238Z","title":"In: Neural Information Processing Systems (2023), https://arxiv.org/abs/2404.00774","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.510238Z"},"links":{"cited_paper":"/paper/2404.00774","citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:6b38d34aa2458f28e33971be6f970a43b7729808944f4d1e0b1bf4d3c00440b5","observation_id":"2b6e8e22-5858-477f-ba79-f48bd68141a4","resolution":{"observed_at":"2026-08-10T21:09:47.510238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.514938Z","title":"In: Proceedings of the 44th Inter- national ACM SIGIR Conference on Research and Development in Informa- tion Retrieval","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.514938Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:36cc1264f0697b1ee98b49eb8312a040fe0beac8a8a5fcdd00db100f6fbbc01f","observation_id":"bed7e76e-446c-46ab-80d1-3f0d6466e603","resolution":{"observed_at":"2026-08-10T21:09:47.514938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-642-41062-8_28","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.587535Z","title":null,"venue":null,"work_id":"f12ab633-6424-484e-b746-905a3882dbeb","year":2013},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":293,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.425691Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:c30809b1ab294b041fd35f7a7f8a0e0938e74289ae1f7e7d11f9ef3735bb5262","observation_id":"c2e8a171-f518-4928-921c-c23eb2c88cd1","resolution":{"observed_at":"2026-08-10T21:09:47.592995Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:09:47.960524Z","title":null,"venue":null,"work_id":"1b0d570e-0340-4337-86ae-55517b009ae0","year":2016},"citing_paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-10T21:09:47.490256Z"},"links":{"citing_paper":"/paper/2501.06121"},"observation_digest":"sha256:9fd4ed0c0f4a072155121a1beea9e212e025639f67efc578d1c325543e6c671b","observation_id":"1ca4c758-f9c9-42bc-be12-68284b16cd39","resolution":{"observed_at":"2026-08-10T21:09:47.965554Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.06121","last_updated":"2026-07-01T14:57:14Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-11T02:32:53.065744Z","submitted_at":"2025-01-10T17:19:59Z","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":1,"verified_fuzzy":7},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2501.06121."}