{"as_of":"2026-08-09T19:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bbe55a8f72e9ad020a2ffc90000ec2b084f9dc99b03e8f906369f94d2707010f","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:37:34.805525Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2506.04790/citation-record","integrity":"/paper/2506.04790/integrity","json":"/paper/2506.04790/citation-record.json","paper":"/paper/2506.04790"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:37:41.809535Z","title":"Optuna: A next-generation hy- perparameter optimization framework","venue":null,"work_id":"0e237a42-a19d-4424-b810-2da622786cd3","year":null},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.566738Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:4bf3a3eddd8a646b3bfbb7a6cf53800f1749e58dd53565ca37b978c1e9f11697","observation_id":"096e2b6f-7ad8-4ab4-ac88-c9027726812b","resolution":{"observed_at":"2026-08-07T10:37:41.812670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.800127Z","title":"Optuna: A next-generation hyperparameter optimization framework","venue":null,"work_id":"e07734fb-71bc-4aa6-a3c8-284f12ecc5e5","year":2019},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.595538Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:3a33471bf3bbd701ee42db4d712667c0ee6b4a11bf54ee4edc473e353c40ef5e","observation_id":"f52b5291-cf19-46ef-806c-355f79d1691a","resolution":{"observed_at":"2026-08-07T10:37:41.803469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.790752Z","title":"Diversity maximization in the presence of outliers","venue":null,"work_id":"57da4282-60bc-4dc6-9a19-c53912eebe91","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.657984Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:db8c7fbb96074bc8e16611116273ea554c1957b725ce12e91d9f65b0f860127a","observation_id":"2c9511ba-f0cb-44ab-b8c6-33056ab4d3ef","resolution":{"observed_at":"2026-08-07T10:37:41.794091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.780711Z","title":"Quicker adc: Unlocking the hidden potential of product quantization with simd","venue":null,"work_id":"8042b342-c0a2-4ca2-8363-ad683bb63e30","year":2021},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.738931Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:cdb93eccea58399ded2911a950710fa2ff776b88f033310ce01236e9e7fbea33","observation_id":"80e2c01a-1254-4705-9472-cb459dccb5e9","resolution":{"observed_at":"2026-08-07T10:37:41.784440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.770960Z","title":"Acl2023 tutorial on retrieval-based language models and ap- plications, 2023","venue":null,"work_id":"d7697f5d-3125-456b-afa8-5dae34ea1459","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.808248Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:8b7eb48da812134049e0672a1141e6743f817386701e3b5339ea978e81def171","observation_id":"a5cd5a33-f15c-4c5d-8b20-3f58f74c83f6","resolution":{"observed_at":"2026-08-07T10:37:41.774349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.09268","last_updated":"2018-10-31T14:46:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-11-28T18:14:11Z","title":"MS MARCO: A Human Generated MAchine Reading COmprehension Dataset","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.09268","snapshot_observed_at":"2026-08-07T10:37:30.889309Z","title":"Ms marco: A human generated machine reading comprehension dataset","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.889309Z"},"links":{"cited_paper":"/paper/1611.09268","citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:4fb6279391700ebc76fa0cfa5ffbe91de53572c8c02327b4369eabb96cc7eb33","observation_id":"7de72cfd-166b-48ed-85f5-d364ac319731","resolution":{"observed_at":"2026-08-07T10:37:30.889309Z","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":"2026-08-07T10:37:41.759734Z","title":"Re- visiting the inverted indices for billion-scale approximate nearest neighbors","venue":null,"work_id":"a4c42c6a-4686-49f4-b789-2989a44ce29e","year":2018},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:30.974453Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:d17df6748e7a1977f6dcd9da1f5a8e11814167a460dd22f74061d4a8b92d486d","observation_id":"89dbbaff-cf32-4fe5-8b6c-9a5daaf49c36","resolution":{"observed_at":"2026-08-07T10:37:41.764382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.750019Z","title":"Foundations of Vector Retrieval","venue":null,"work_id":"a269be8c-0717-47b5-a6ea-ab812182ec2b","year":null},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.042324Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:d88ace62a298e869b58988ceaa0d5ceec933e0a7c8785537ab3a5e5dc5d2e066","observation_id":"7f0e3ac3-e806-42f8-825f-402fe3383f76","resolution":{"observed_at":"2026-08-07T10:37:41.753369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.689526Z","title":"The use of mmr, diversity-based reranking for reordering documents and pro- ducing summaries","venue":null,"work_id":"f51cf0ed-de25-4a17-9e9c-f544506175ab","year":1998},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.119460Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:0ee7e8d0f7596299982e744643ab2ddef742b52a3e8a891a5c0b179be8ab6db0","observation_id":"a7fe7239-f781-491c-9a80-609b2043fa78","resolution":{"observed_at":"2026-08-07T10:37:41.742775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.446288Z","title":"Learned index with dy- namic ϵ","venue":null,"work_id":"5795c02c-dfe4-4f67-825d-ceda2fde3e47","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.207368Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:b22005b07f6e655bfa532e2304a4277cbe34eaf150ba5638fe89d485c9ba7cdf","observation_id":"80b53865-7e63-4569-b95d-b733ea100ed2","resolution":{"observed_at":"2026-08-07T10:37:41.530869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:41.025336Z","title":"Bert: Pre-training of deep bidirectional trans- formers for language understanding","venue":null,"work_id":"7bc7f4d8-db28-44f9-bdb4-699cc5f2cd46","year":null},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.297695Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:a955191b0c8570cecff418196f8e221ddbcb0dfd1674f865f26c36763152b9cb","observation_id":"d7b63c4f-dd53-47dc-b109-5210ce420c21","resolution":{"observed_at":"2026-08-07T10:37:41.229608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:40.789057Z","title":"Tsunami: A learned multi-dimensional index for correlated data and skewed workloads","venue":null,"work_id":"b10ccecf-f2bb-43ef-98e0-710a4240566f","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.386182Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:5ada34802d601d49f52e1493be107e79e7445e5fcc9594386be05b7c1ef70f78","observation_id":"afe7d5fa-255c-45bc-aa5e-22686d8be89f","resolution":{"observed_at":"2026-08-07T10:37:40.880555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:40.557873Z","title":"Link and code: Fast indexing with graphs and compact re- gression codes","venue":null,"work_id":"2ca793db-1362-4e6a-b1ea-f654654421f6","year":2018},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.470173Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:7b7618f88a5aca81d163a8161920c11627e2d74d1f1ed0ed9547a1181562f582","observation_id":"db41db40-04f0-4591-9b54-8f8a241ef793","resolution":{"observed_at":"2026-08-07T10:37:40.682634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:31.541091Z","title":"The faiss library","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.541091Z"},"links":{"cited_paper":"/paper/2401.08281","citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:8be9cc57351b8b63d4a4be416c492e67cc77ac41c785c13dc84f542bc5b50a01","observation_id":"f5cadd4d-56f1-4f5e-a0cc-f8f4d52e96a1","resolution":{"observed_at":"2026-08-07T10:37:31.541091Z","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":"2026-08-07T10:37:40.412794Z","title":"Search result diversi- fication","venue":null,"work_id":"be1d7f8e-cb66-4040-9d2b-513eae9f0c6f","year":2010},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.591741Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:7cd25b14b15b95f709921dc673f982b6cc71e4ffe11bed7efaee140106abe953","observation_id":"47169e97-656f-4010-8e10-370eacf33a89","resolution":{"observed_at":"2026-08-07T10:37:40.481668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:40.173043Z","title":"Disc diversity: Result diversification based on dissimilarity and coverage","venue":null,"work_id":"bbf13387-40a0-4e34-ae13-0170cb5af4a7","year":2012},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.669668Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:413645ac58c8cd888f39caa5b53a38ea76bc50ab17cb18e935498ed26829ae49","observation_id":"35ef443c-cca7-4b54-ab4a-30e76ea8da71","resolution":{"observed_at":"2026-08-07T10:37:40.312764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:39.979550Z","title":"Learned Data Structures","venue":null,"work_id":"42625a07-f4fc-40e6-a7d3-67bbb12e18ea","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.743134Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:fc8fad4c7ddef092f45a3035a3afb6ac6829d11d061829e12a174c842e5e6345","observation_id":"e4c91106-75f4-405f-9e47-1799dc235d1a","resolution":{"observed_at":"2026-08-07T10:37:40.085821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:39.786640Z","title":"The pgmindex: a fully dynamic compressed learned index with provable worst-case bounds","venue":null,"work_id":"b7b07b9f-9007-49ef-abb9-033e4cfe60bd","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.817102Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:ef7f812abde788a143dc43dad5f3eb48fef0c824b11e88ae192eeefd7bcf75be","observation_id":"14e33dff-cb65-4e28-8043-f2fbace15749","resolution":{"observed_at":"2026-08-07T10:37:39.864557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:39.518247Z","title":"Why are learned indexes so effective? In Proc","venue":null,"work_id":"a78abb98-87d0-4e6f-9787-fdf70cbdedba","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.896190Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:e53ae7ae258bace122104f2dd07db3ff47118e51a46ed7e04a78e65a8457050b","observation_id":"c3ea6766-b1a7-4cfc-a1dc-c9ad530bcd2e","resolution":{"observed_at":"2026-08-07T10:37:39.642906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:39.367837Z","title":"Fast approximate nearest neighbor search with the navigating spreading-out graph","venue":null,"work_id":"06644cb2-8cf1-4cfc-b117-ed0ff77478a3","year":2019},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:31.969327Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:52403e6e6e2e7bd9b1a7981778fa29ae21283925c52cf2126d13a75b58cd8338","observation_id":"fb6b7150-339e-4f13-8afb-b72cbd6d2705","resolution":{"observed_at":"2026-08-07T10:37:39.414380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:39.163902Z","title":"Flexflood: Efficiently up- datable learned multi-dimensional index","venue":null,"work_id":"89602014-fe8e-45fd-a003-802d874520f9","year":2024},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.038484Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:68b14e8eedaae15903020e4083fe2e9dca5d66d82c723a47f707a28a08aaf85a","observation_id":"cdaef10e-2be3-4185-a045-53ae61882646","resolution":{"observed_at":"2026-08-07T10:37:39.280149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.989653Z","title":"Solving diversity-aware maximum inner product search efficiently and effectively","venue":null,"work_id":"de591cf5-75f9-4562-98bd-a269dcf17c6f","year":2022},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.092604Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:a5b30212dcf65028a277c6b9a261b3f75a2c666f0c23c7ac1d1ee4fb96c9f073","observation_id":"55dd7bdc-5ace-4217-bda5-ce3ccb130141","resolution":{"observed_at":"2026-08-07T10:37:39.063419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.852966Z","title":"nanobind: tiny and efficient c++/python bind- ings, 2022","venue":null,"work_id":"03c7b5e1-9700-4cc0-a3fb-936ee18a3afd","year":2022},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.192912Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:e30731fb1663253eaa4008d59cd9690d3d962a3342b311acbc33be91c365b933","observation_id":"90c38d58-b4b2-4ed1-bd81-ac125df40e77","resolution":{"observed_at":"2026-08-07T10:37:38.914438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.726393Z","title":"Prod- uct quantization for nearest neighbor search","venue":null,"work_id":"82fa0aa3-7115-4bce-adfc-e62e6276997f","year":2011},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.294889Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:e4bbc3e4c076e70205ddceea5508503765efb5460d855f4ddeb8c6d55d831169","observation_id":"3530c3ab-bac3-4fb1-bb9d-b0b5d6206317","resolution":{"observed_at":"2026-08-07T10:37:38.792768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.625712Z","title":"Kochenderfer and Tim A","venue":null,"work_id":"8b619ada-95f6-4bde-9a2b-2e79739ec124","year":2019},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.405816Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:89c18510f7ceb5d19ba2f218ebb90a4df41ebad95e9e6848291de5dfea105357","observation_id":"a13eb572-e005-49c4-8e32-d97d17e7aa81","resolution":{"observed_at":"2026-08-07T10:37:38.673276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.508391Z","title":"Chi, Jeffrey Dean, and Neoklis Polyzotis","venue":null,"work_id":"efc4a8f0-8a24-4d9a-9b42-101006f0ab95","year":2018},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.480018Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:1a8f9d3958145bfa24983454c16540b3b87357968410ecb61bb46579cac45640","observation_id":"493f5823-d722-43cf-9aaf-d608c12bd56a","resolution":{"observed_at":"2026-08-07T10:37:38.567288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.430441Z","title":"Stable learned bloom filters for data streams","venue":null,"work_id":"18332050-b305-4433-8e1c-97ab2f4eb0bf","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.566132Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:0721e3a8d9c64772e13be6e51f65191a03984ae1d2a27f90c7afb5f32ca17685","observation_id":"bc9c9471-a465-4818-8ba7-1bbd946443d7","resolution":{"observed_at":"2026-08-07T10:37:38.470577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.307148Z","title":"Malkov and Dmitry A","venue":null,"work_id":"ea7544c7-8639-4eed-a697-18caa5e8316e","year":null},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.641236Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:7ce383c884c714713eeea8aab3f31cd896915e2bdd99843770039bf62be85f8b","observation_id":"0cbd5a17-1a52-4eb4-b2f4-106ea3d7de2e","resolution":{"observed_at":"2026-08-07T10:37:38.369722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.233119Z","title":"Cvpr2020 tutorial on image retrieval in the wild, 2020","venue":null,"work_id":"4accf879-5e92-45c2-84f4-2cb56de7cebb","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.747966Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:98879e3a4bd2cb0276226f4497761a35c7b966ba27de054f78b9c4c939571c07","observation_id":"47e7d837-7879-489d-9b8e-3d1472e48a38","resolution":{"observed_at":"2026-08-07T10:37:38.268619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.144595Z","title":"Arm 4-bit pq: Simd-based acceleration for approximate nearest neighbor search on arm","venue":null,"work_id":"58e429af-57ae-439e-83c4-7384782e2131","year":2022},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.823947Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:f30338451623b839e96207eafb505366bdb79c29dacfd21a7b1259ea4d9f5f6f","observation_id":"7ed2b6ae-c687-4919-9bcb-800245329102","resolution":{"observed_at":"2026-08-07T10:37:38.186919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:38.029852Z","title":"Cvpr2023 tutorial on neural search in action, 2023","venue":null,"work_id":"27006cc9-4edd-4fdc-a83a-4a36164621c3","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.916528Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:469f468ae96028d197599d5463ceca39a6a9031d1c78072e9fcce6d83c5b98c2","observation_id":"77a5877c-463e-45c1-ae77-b71f42c672b7","resolution":{"observed_at":"2026-08-07T10:37:38.071747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:37.914234Z","title":"A model for learned bloom filters, and optimizing by sandwiching","venue":null,"work_id":"2d895990-b1bc-4d7b-b156-1c3bb65e91a5","year":2018},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:32.989797Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:707d090df3e85b85333c0cebf474c76a1062eee32914deace88bcd9b4e90bffb","observation_id":"d91e59e1-5e86-4366-b393-e2f7272d0371","resolution":{"observed_at":"2026-08-07T10:37:37.962551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:37.792387Z","title":"Learning multi-dimensional indexes","venue":null,"work_id":"89f28b36-e42a-453f-aec9-ab13e3e9d79d","year":2020},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.099647Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:b6329e0ca8edcf847d01896b7f05e9b8443983eb2e557d3cdc203694b4af533b","observation_id":"ba4a20c4-312c-4f5d-a5ed-10dc13aa9c29","resolution":{"observed_at":"2026-08-07T10:37:37.852755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:37.653494Z","title":"General and practical tun- ing method for off-the-shelf graph-based index: Sisap index- ing challenge report by team utokyo","venue":null,"work_id":"252e08e1-a133-4ee9-88f0-7a43f26fced8","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.206032Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:e5a065f13a7474ef2acaf9d7f31e433917fa67389fc89749783ae8671cb51c52","observation_id":"f07fa6d5-0fb4-47c2-9d4d-a749db442140","resolution":{"observed_at":"2026-08-07T10:37:37.724700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04713","last_updated":"2024-02-07T10:05:42Z","snapshot_observed_at":"2026-08-09T04:04:46.584776Z","submitted_at":"2024-02-07T10:05:42Z","title":"Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search","version":1},"cited_work":{"arxiv_id":"2402.04713","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.04713","snapshot_observed_at":"2026-08-07T10:37:34.976241Z","title":"Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search","venue":"cs.IR","work_id":"71db0a0b-8131-40f6-8374-40b9dc451c36","year":2024},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.278187Z"},"links":{"cited_paper":"/paper/2402.04713","citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:d49b92c85d0c46f8563790043c1bb1d99eda3ca3ad221f2c3227ee0cc6e1409e","observation_id":"5aa0a325-7e53-4177-a54e-6af1958e8de2","resolution":{"observed_at":"2026-08-07T10:37:35.040405Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:37.429508Z","title":"Relative nn-descent: A fast index construction for graph-based approximate nearest neighbor search","venue":null,"work_id":"0f14f170-2da7-4e22-9b03-f615f255eac2","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.355981Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:0754fd86c344e6bdd026cb2c7e92bb10d10ef0e1c92d28b499c77560358c0bae","observation_id":"9cc4d7c6-b69d-4c89-b6c0-6e54b9ca2b40","resolution":{"observed_at":"2026-08-07T10:37:37.520472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:37.240399Z","title":"Revisiting oxford and paris: Large-scale image retrieval benchmarking","venue":null,"work_id":"f7790222-982f-43e6-8e54-2ed1ff776d22","year":2018},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.461554Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:4f6dde4e43f3e9280aeb92c82ddb54d01bd7c7b465dd5328247cb4f0666704ab","observation_id":"dcedb3aa-e9dd-41dd-ac67-42378b318b44","resolution":{"observed_at":"2026-08-07T10:37:37.341940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:37.072081Z","title":"Fine- tuning cnn image retrieval with no human annotation","venue":null,"work_id":"61f835ef-f17d-45eb-8208-27940e244e81","year":2018},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.538563Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:9cd3e837548f546ca0ab84a821fc27b2032ad8cfb2e6530731be35e56dc3a4b7","observation_id":"8fb037f3-18e1-471e-82bf-1c8e041e8d9f","resolution":{"observed_at":"2026-08-07T10:37:37.146823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:36.906787Z","title":null,"venue":null,"work_id":"868d9cf7-6cbd-4942-99a4-07c149b67453","year":2016},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.644399Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:cd6a264e11c033ed87d60e9e6ab75ac492c17153683c96ecb9591e670f3d0169","observation_id":"329c89b5-6e32-4504-864f-88da853715ef","resolution":{"observed_at":"2026-08-07T10:37:36.972846Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:36.735089Z","title":"Ravi, Daniel J","venue":null,"work_id":"bc9fd125-24b7-4994-83d5-620508585b54","year":1994},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.743893Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:1e516b4cb014b41da7239607410d5f7b9090a82929216a798e781273bf8631a8","observation_id":"e8a95796-5121-4156-92ea-bb9028105119","resolution":{"observed_at":"2026-08-07T10:37:36.821453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:36.546895Z","title":null,"venue":null,"work_id":"fcc18e2e-f37f-4abd-ac69-80d7bd9d9401","year":2015},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.886378Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:59481b194427b5df183bfe77ccd4574c9946daa09a5ee2d69aa694b543a637f5","observation_id":"d4b01840-c994-48bf-8572-57e3b413b943","resolution":{"observed_at":"2026-08-07T10:37:36.621337Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:36.364399Z","title":"Fast partitioned learned bloom filter","venue":null,"work_id":"bb1cc4aa-65e3-4326-a221-963601550903","year":2023},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:33.992833Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:8fa311a791fc6646f2f0de110a00e6935473d7a1eb3b24cf0793d3215a84f553","observation_id":"c66f7cb2-85d5-4943-9c47-3dffe859fb4b","resolution":{"observed_at":"2026-08-07T10:37:36.450788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:36.193249Z","title":"Glow: Global weighted self-attention network for web search","venue":null,"work_id":"4f496b50-bbf0-49bd-9621-06678ad6b534","year":2021},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.067962Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:110651de15f101c76714fc8d45c8888895ad09f1ba8d242f363c8cb15a6d1698","observation_id":"d53c2956-ad72-4f31-889e-64fd8397f70d","resolution":{"observed_at":"2026-08-07T10:37:36.276852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:36.023837Z","title":"Results of the neurips’21 challenge on billion-scale approximate nearest neighbor search","venue":null,"work_id":"f5bfb844-d457-4082-be8b-597b3fd229cd","year":2022},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.170760Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:0d5657e447366f3f5ba025a404685f268ffa2cb64eb629d9f87a768c8bd48f87","observation_id":"466e2177-cf36-48a8-b98e-f989b1f5e6c7","resolution":{"observed_at":"2026-08-07T10:37:36.065747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17424","last_updated":"2024-09-25T23:24:56Z","snapshot_observed_at":"2026-08-02T09:28:14.540794Z","submitted_at":"2024-09-25T23:24:56Z","title":"Results of the Big ANN: NeurIPS'23 competition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.17424","snapshot_observed_at":"2026-08-07T10:37:34.281513Z","title":"Results of the big ann: Neurips’23 competition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.281513Z"},"links":{"cited_paper":"/paper/2409.17424","citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:1461c4150d6d111809e896a58b1dd7710a380653318f83de70822f00ef062959","observation_id":"63796b15-1610-4e8d-9a2d-8bce6f54d998","resolution":{"observed_at":"2026-08-07T10:37:34.281513Z","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":"2026-08-07T10:37:35.864831Z","title":"Diskann: Fast accurate billion-point nearest neighbor search on a single node","venue":null,"work_id":"9377d1f8-2f5e-48ec-97fd-c9397f965eb3","year":2019},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.385124Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:5ee3dab6c65b6c71ce0adca7e406791e7136d55a92ea40d003c3c05152486549","observation_id":"df671512-be0c-4c57-a008-f9551a59c542","resolution":{"observed_at":"2026-08-07T10:37:35.948015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:35.666221Z","title":"Partitioned learned bloom filters","venue":null,"work_id":"156b715e-d323-47f5-a06b-339fc89ff682","year":null},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.467562Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:1d091d002e2d3089ae715522ec8e958564e5632fa974f0b28c67bf7c5c236a53","observation_id":"c7ca202a-c670-4ad7-8416-ebef1b05e465","resolution":{"observed_at":"2026-08-07T10:37:35.769319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:35.512896Z","title":"A comprehensive survey and experimental compari- son of graph-based approximate nearest neighbor search","venue":null,"work_id":"819dcec5-98b6-4588-8aee-df92442b5f86","year":2021},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.574874Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:d5a1b2c5616d05a1e7914d9f9704a3e79e3adf55a39462a95484ec9eea329ff9","observation_id":"fdfcb0f8-ae0d-4c40-b6c6-1b093c42b830","resolution":{"observed_at":"2026-08-07T10:37:35.578460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:35.331055Z","title":"Updatable learned index with precise positions","venue":null,"work_id":"a361673d-b035-4df2-bac7-3c0120a72b60","year":2021},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.688983Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:707c821b651ee0b5cc77fdd493136634743b2eae774657a2f20533030f710107","observation_id":"11b07d8a-5ed8-49f2-8117-ec42f7742983","resolution":{"observed_at":"2026-08-07T10:37:35.421287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T10:37:35.166280Z","title":"This condition","venue":null,"work_id":"1fd1a02f-4b9c-4e05-9580-353fd1e2d82e","year":2017},"citing_paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T10:37:34.805525Z"},"links":{"citing_paper":"/paper/2506.04790"},"observation_digest":"sha256:802b1795aabeb05eea96da2662e32cc873d2b025e9451c4b0cd1682b83ea1550","observation_id":"8f92090f-0cc3-4056-9a2f-5615c15eccc9","resolution":{"observed_at":"2026-08-07T10:37:35.235634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.04790","last_updated":"2025-06-05T09:17:30Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T10:30:25.261637Z","submitted_at":"2025-06-05T09:17:30Z","title":"LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":44},"total_outbound_references":50},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2506.04790."}