{"as_of":"2026-08-09T05:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:440945a144fafc2580462b6f64ea7c79449b1eb2d5ec37601cb5da71b603997c","coverage":[{"denominator":9,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:43:03.895253Z","state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2508.00882/citation-record","integrity":"/paper/2508.00882/integrity","json":"/paper/2508.00882/citation-record.json","paper":"/paper/2508.00882"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:43:03.804499Z","title":"Space/time trade-offs in hash coding with allowable errors","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.804499Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:ebf12f2d5c4db64f905fe579cd8341175515619a8c5db0e29f9ed2a03692e113","observation_id":"d378f10a-1864-4514-b00d-3d0e58ead555","resolution":{"observed_at":"2026-08-06T14:43:03.804499Z","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":"2026-08-06T14:43:04.229488Z","title":"Ada-bf: Adaptive bloom filter with learned model","venue":null,"work_id":"4bda33c1-3594-4357-8b1b-401342b441cf","year":2020},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.814687Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:5f24e153d8df5b7752fde60100ca7f3c168d58ab8254fff46f7261583f22eab4","observation_id":"62a60f60-c0c5-4dda-acec-66283a710396","resolution":{"observed_at":"2026-08-06T14:43:04.237076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:43:04.189275Z","title":"Monkey: Optimal navigable key-value store","venue":null,"work_id":"ab2e21b9-242f-43bd-b462-f37fe2682f4c","year":2018},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.823615Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:1eeed8489091a0ee8ccd18b3d55f983b7386effbf5f846d8aac5af9bb022e419","observation_id":"38870f40-fdc7-4d2d-bf15-630e6bca12c9","resolution":{"observed_at":"2026-08-06T14:43:04.206078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:43:04.137682Z","title":"Learned cardinalities: Estimating correlated joins with deep learning","venue":null,"work_id":"2919799b-98cf-46e3-980a-f4fdbdaa349d","year":2019},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.839109Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:930c9d93a676d87fa4b5adbd36cd55df5478a130194ef0453460e83996315581","observation_id":"d5987f01-ffad-4397-bfaf-ae4466ef00e4","resolution":{"observed_at":"2026-08-06T14:43:04.149153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:43:04.098080Z","title":"The case for learned index structures","venue":null,"work_id":"cec62ee3-9f69-4e76-82ae-126ec6b613f4","year":2018},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.851737Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:25b157fc10b3a6b8ddba6b00abb399fa809392f065de2633126dc6e8feed1d57","observation_id":"6bc91ed7-6619-4fd9-9bd1-49133b666b76","resolution":{"observed_at":"2026-08-06T14:43:04.111506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:43:04.079454Z","title":"A model for learned bloom filters and optimizing by sandwiching","venue":null,"work_id":"97f2de18-3c29-4e29-91ef-cd19689004d9","year":2018},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.862022Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:e540c28a3a7e726748d0874863ac66d74e05204d9e93950e695594c77d9b039b","observation_id":"c66e2ba1-5060-4f87-8722-869a0e53a051","resolution":{"observed_at":"2026-08-06T14:43:04.084231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09123","last_updated":"2020-06-16T13:13:28Z","snapshot_observed_at":"2026-08-02T22:17:57.034314Z","submitted_at":"2020-06-16T13:13:28Z","title":"Algorithms with Predictions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09123","snapshot_observed_at":"2026-08-06T14:43:03.870747Z","title":"Algorithms with predictions","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.870747Z"},"links":{"cited_paper":"/paper/2006.09123","citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:a665549a85a3304ce1ceccd8d029aa095bf0121c2b9d15c035633170bace3125","observation_id":"f974e7eb-8bb4-4c6e-ba2d-d7220c2a953d","resolution":{"observed_at":"2026-08-06T14:43:03.870747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10512","last_updated":"2019-05-25T03:52:23Z","snapshot_observed_at":"2026-08-08T22:10:11.693615Z","submitted_at":"2019-05-25T03:52:23Z","title":"Abelian subgroups, nilpotent subgroups, and the largest character degree of a finite group","version":1},"cited_work":{"arxiv_id":"1905.10512","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.10512","snapshot_observed_at":"2026-08-06T14:43:03.986295Z","title":"Abelian subgroups, nilpotent subgroups, and the largest character degree of a finite group","venue":"math.GR","work_id":"05d8da90-59f8-406a-a37f-8825982d699d","year":2019},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.885797Z"},"links":{"cited_paper":"/paper/1905.10512","citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:25a23b42bf38fe0e304cba45f88151c9d1632ef39e8baff8ce4f22345f6c1d01","observation_id":"2e57457e-ce0e-4331-9294-3e22656ddee5","resolution":{"observed_at":"2026-08-06T14:43:04.001184Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T14:43:04.056532Z","title":"Learning to index for nearest neighbor search","venue":null,"work_id":"ecc85208-d2a3-4786-b6c6-0262c3bb1797","year":2020},"citing_paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:03.895253Z"},"links":{"citing_paper":"/paper/2508.00882"},"observation_digest":"sha256:24caf768192ae8d1404e32fd3456f2cf04fb09cc3a566604d747088e1fe81cda","observation_id":"64d1d491-0eba-4a3c-ae58-80fbb70280e9","resolution":{"observed_at":"2026-08-06T14:43:04.063636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.00882","last_updated":"2025-07-24T04:23:52Z","latest_version":1,"primary_category":"cs.DS","snapshot_observed_at":"2026-08-07T12:39:13.019125Z","submitted_at":"2025-07-24T04:23:52Z","title":"Learned LSM-trees: Two Approaches Using Learned Bloom Filters"},"reference_resolution":{"displayed":9,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":6},"total_outbound_references":9},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2508.00882."}