{"as_of":"2026-08-24T03:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7a50672a5bf00660bbca1f4ba124eb85f2ff98f5480b7b1eccf412c868f11205","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T04:38:34.676450Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-19T22:11:08.273883Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T22:12:50.673092Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"cited_work":{"arxiv_id":"2509.06046","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.06046","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"01324ea7-dad2-4eae-852f-6d5befeec0fd","year":2025},"citing_paper":{"arxiv_id":"2604.20401","last_updated":"2026-04-22T10:12:19Z","snapshot_observed_at":"2026-08-07T15:21:24.958235Z","submitted_at":"2026-04-22T10:12:19Z","title":"Onyx: Cost-Efficient Disk-Oblivious ANN Search","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T00:22:59.265993Z"},"links":{"cited_paper":"/paper/2509.06046","citing_paper":"/paper/2604.20401"},"observation_digest":"sha256:52885cf43ab831aa00363d648203881f9b8c44ad729a2c79541dfb7fa6087b5b","observation_id":"6a198959-4c3f-4293-93eb-3ea0da29df0c","resolution":{"observed_at":"2026-05-10T00:24:46.871126Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"cited_work":{"arxiv_id":"2509.06046","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.06046","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"01324ea7-dad2-4eae-852f-6d5befeec0fd","year":2025},"citing_paper":{"arxiv_id":"2605.16007","last_updated":"2026-05-15T14:37:18Z","snapshot_observed_at":"2026-08-15T00:05:44.805455Z","submitted_at":"2026-05-15T14:37:18Z","title":"Ascend-RaBitQ: Heterogeneous NPU-CPU Acceleration of Billion-Scale Similarity Search with 1-bit Quantization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-19T22:11:08.273883Z"},"links":{"cited_paper":"/paper/2509.06046","citing_paper":"/paper/2605.16007"},"observation_digest":"sha256:f50e3da088e872abb349c9f039d924d01344e99178a4b705c5870d53bf812751","observation_id":"5759dc13-77a2-4bf3-8a39-87bd815d9add","resolution":{"observed_at":"2026-05-19T22:12:50.676586Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.06046/citation-record","integrity":"/paper/2509.06046/integrity","json":"/paper/2509.06046/citation-record.json","paper":"/paper/2509.06046"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T04:38:31.937768Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:31.937768Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:502073fc72828d50591335c59c0ffaf7945a5a9f732ed9319fb3eec9e675f7af","observation_id":"e2b7f569-beb3-406d-999e-3214a7875c39","resolution":{"observed_at":"2026-08-05T04:38:31.937768Z","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":"2026-08-05T04:38:38.747770Z","title":"and Indyk, P","venue":null,"work_id":"94226919-5985-4680-a3a7-c3f5de514e88","year":2008},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.017945Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:9e2db62d748b0af29e5e51c13efbbbb099db32fa4be1633b3c70ed66bfba742e","observation_id":"00f98d1a-cb32-46e2-ad5d-4b62a5e1fc9e","resolution":{"observed_at":"2026-08-05T04:38:38.860109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.05614","last_updated":"2018-07-17T20:45:47Z","snapshot_observed_at":"2026-08-15T03:53:21.759572Z","submitted_at":"2018-07-15T21:25:55Z","title":"ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.05614","snapshot_observed_at":"2026-08-05T04:38:32.120149Z","title":"Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.120149Z"},"links":{"cited_paper":"/paper/1807.05614","citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:6af7f0968e35473dc59d173d340639f62daebfba0ee5a08f4e21f870c3268e42","observation_id":"60826c42-d94c-4fc5-84f2-2cf9aa1cd103","resolution":{"observed_at":"2026-08-05T04:38:32.120149Z","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":"2026-08-05T04:38:38.515305Z","title":"and Lempitsky, V","venue":null,"work_id":"3e673cc7-26fa-4a61-b4df-511e71585eed","year":2014},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.224210Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:313d19e404ab4f95da2a406b3a0fcba494a2309085c548177f7669f2e5fd39b3","observation_id":"e488598f-63a1-4d83-81f3-e89e5a316da9","resolution":{"observed_at":"2026-08-05T04:38:38.626420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:38.268464Z","title":"Big-ann benchmarks: Neurips 2023","venue":null,"work_id":"9d27965e-8e43-47d2-bfab-a1220a629536","year":2023},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.331766Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:e352a411e968cffb8deed0ce990418d7ad9f0bd71a6601d4f2ca21ac2fa759b0","observation_id":"8cfd66dd-193d-454a-bd19-3dc4dd724693","resolution":{"observed_at":"2026-08-05T04:38:38.383083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:38.018339Z","title":"and Barroso, L","venue":null,"work_id":"d78ea4a9-5a6d-4942-8683-cacd69de71b8","year":2013},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.501931Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:84f3b51c4c7857b826ec1723e6a7b45a1fce3de39fc9e93318bea7d4123ff435","observation_id":"d1ff0965-0418-4206-8586-fd77a3a79a80","resolution":{"observed_at":"2026-08-05T04:38:38.116961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:37.699176Z","title":"Dynamo: Amazon's highly available key-value store","venue":null,"work_id":"91331a24-4987-434a-b049-e152f139eab2","year":2007},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.613368Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:82b90928ec24f5c6470b578763e8f9bc5e303021caf28d61e978b947a9c6560c","observation_id":"e67ad181-215f-440a-a3d3-8b786393b87c","resolution":{"observed_at":"2026-08-05T04:38:37.868978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:37.437588Z","title":"P., and Wagner, T","venue":null,"work_id":"5b38d29d-3143-40e5-9d21-5805380009d7","year":2019},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.729739Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:d4cfd038755c876e270f28209853a12bc0d6428349a7714342f840b9a79d2bfc","observation_id":"ff92dc10-3237-4448-8a60-429b11d6d740","resolution":{"observed_at":"2026-08-05T04:38:37.545844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:37.197944Z","title":"and Long, C","venue":null,"work_id":"07e01a0f-86b8-448d-9e43-c97359727341","year":2024},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.832294Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:3beefff7a9bf3197eff8facfec7710c149bbef7bc6a25d8552025ddf6b6d0d57","observation_id":"a31e952f-0afc-458a-aa01-d1ffd8a22334","resolution":{"observed_at":"2026-08-05T04:38:37.308299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:32.956795Z","title":"Optimized product quantization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:32.956795Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:4c9aca3e972957d810dc73bfefe55dd8465fb51d86f24154fbd04abaad8db47d","observation_id":"5a311658-04aa-465a-ad28-0a95b140db5d","resolution":{"observed_at":"2026-08-05T04:38:32.956795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01797","last_updated":"2024-03-04T07:37:09Z","snapshot_observed_at":"2026-08-17T18:36:35.242533Z","submitted_at":"2024-03-04T07:37:09Z","title":"Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01797","snapshot_observed_at":"2026-08-05T04:38:33.076265Z","title":"Unleashing graph partitioning for large-scale nearest neighbor search, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.076265Z"},"links":{"cited_paper":"/paper/2403.01797","citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:5c01d867d12c909d0bb2d22e49ff80b749251c4ff9b8f42e0d6f8c2df51c4f2f","observation_id":"dcbdb107-3cbb-4818-86de-e85426951a7a","resolution":{"observed_at":"2026-08-05T04:38:33.076265Z","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":"2026-08-05T04:38:36.960077Z","title":"CXL-ANNS : Software-Hardware collaborative memory disaggregation and computation for Billion-Scale approximate nearest neighbor search","venue":null,"work_id":"69a625f9-9e35-42f1-a916-13e230f8239a","year":2023},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.248751Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:b2340d00a235ec50257ebefaef1bccd3eed805cc7ccdf925c2dc17cad0c48e3b","observation_id":"f30b96a9-ebad-4e52-a2a0-e61d1ea3356e","resolution":{"observed_at":"2026-08-05T04:38:37.077851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:36.744948Z","title":"Product quantization for nearest neighbor search","venue":null,"work_id":"610a6968-6964-482e-9279-c2788e3e9054","year":2010},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.339171Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:02b7cdeff5110771a14a153f5e29eb223240be63b0b9239e26115ffce95bd9de","observation_id":"6a4cfb1b-de3e-465e-a60a-c2e1aa69ca50","resolution":{"observed_at":"2026-08-05T04:38:36.834239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:36.502773Z","title":"Billion-scale similarity search with gpus","venue":null,"work_id":"d351d934-12f4-4f04-8445-438289e291a1","year":2019},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.459103Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:65a7e99947f65094571859139fecba21bae85f29dd4667fdd7615117f0ce2c2f","observation_id":"38c231a9-3352-433a-b997-f1ebafa1f9cc","resolution":{"observed_at":"2026-08-05T04:38:36.592366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11324","last_updated":"2025-04-12T18:43:50Z","snapshot_observed_at":"2026-08-18T20:14:45.454723Z","submitted_at":"2024-01-20T21:09:19Z","title":"BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11324","snapshot_observed_at":"2026-08-05T04:38:33.558372Z","title":"V., Vedurada, J., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.558372Z"},"links":{"cited_paper":"/paper/2401.11324","citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:1f7fe033470afc856eac64e4c254bfc753a4d3da2b49056b079f5beb810a3e66","observation_id":"ab8f11f4-3d6d-4cd9-972b-84e4316acef8","resolution":{"observed_at":"2026-08-05T04:38:33.558372Z","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":"2026-08-05T04:38:36.261392Z","title":"u ttler, H., Lewis, M., Yih, W.-t., Rockt \\","venue":null,"work_id":"1e3547e3-a0e1-4b7c-be37-36861b41e397","year":2020},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.718142Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:5d7db8ffb054fcbdba185d8ddace94356e880945564f16f1b2c2be7696f03154","observation_id":"1bd97bfd-81da-4919-abbf-4344b3f7f001","resolution":{"observed_at":"2026-08-05T04:38:36.376864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:36.049898Z","title":null,"venue":null,"work_id":"9d90f930-f31a-4d78-bd8e-1100069e4fd6","year":2018},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:33.865056Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:4db1fbdacb3a71e69d1200825b07e605b7d7b3ad74f1cae0a32d09c1ea425f86","observation_id":"8a9dce86-9db2-439d-a21b-e5de2f3fe17f","resolution":{"observed_at":"2026-08-05T04:38:36.173516Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:35.885391Z","title":"Cagra: Highly parallel graph construction and approximate nearest neighbor search for gpus","venue":null,"work_id":"b7ba0002-b789-43e5-9901-c928b3d1f4b0","year":2024},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.021966Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:c261b1dc9c7e30ae053a8c75b66f7f7ca76fdad6939f9f5953bd288b5d9cb5c3","observation_id":"a4d5b317-ba92-4f6b-b87b-e20f75b6f5f2","resolution":{"observed_at":"2026-08-05T04:38:35.969737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:35.646737Z","title":"Lm-diskann: Low memory footprint in disk-native dynamic graph-based ann indexing","venue":null,"work_id":"a7e6ad3c-ce6f-4067-93b4-a59e8db4024d","year":2023},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.131933Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:f873302ff395b0590f842329d4c0e47ea3f4f9eab5b2b345fd439262679bc116","observation_id":"f40f7036-a1a6-4d7f-8ede-03971411cc17","resolution":{"observed_at":"2026-08-05T04:38:35.773683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:35.459870Z","title":"J., Devvrit, Kadekodi, R., Krishaswamy, R., and Simhadri, H","venue":null,"work_id":"fd572443-970b-4627-9585-fe703bf7c051","year":2019},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.275466Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:dfb22484e70ef2ae99425286ef88e2a200cb749582283c586a7b52591c63a688","observation_id":"01f363a3-cee0-46f4-bcd5-dc125a61e3d2","resolution":{"observed_at":"2026-08-05T04:38:35.538741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06004","last_updated":"2025-02-26T07:47:30Z","snapshot_observed_at":"2026-08-17T17:20:43.227072Z","submitted_at":"2024-04-09T04:20:27Z","title":"AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.06004","snapshot_observed_at":"2026-08-05T04:38:34.393729Z","title":"Aisaq: All-in-storage anns with product quantization for dram-free information retrieval","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.393729Z"},"links":{"cited_paper":"/paper/2404.06004","citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:a241d38d62d5e4c7478bff037989c20427a948cb6a88634ae8ca7f4ce4fc603a","observation_id":"b2bedc99-5cc4-4e43-9ef2-d2776599c4c9","resolution":{"observed_at":"2026-08-05T04:38:34.393729Z","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":"2026-08-05T04:38:35.272454Z","title":null,"venue":null,"work_id":"85f9c146-2558-460d-bbf3-bdb5410c7907","year":2021},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.468668Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:0fc8414e672dd3db833cf2a79db9fe0ae24fd3d9b8f6821c23e8e02c52ff86f1","observation_id":"223a456c-566f-4aef-be62-18e082373b94","resolution":{"observed_at":"2026-08-05T04:38:35.382394Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:35.061850Z","title":null,"venue":null,"work_id":"15c07be6-9a28-4c41-a96b-d320efb4d0e6","year":2018},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.548947Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:91cc33758f93fa5ac5cfe07e58e7c85828bc7e6b09b087b7e30a91d4e65e8c26","observation_id":"69f4b813-17a6-4df8-a0fd-7c16b60ff276","resolution":{"observed_at":"2026-08-05T04:38:35.144511Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T04:38:34.868331Z","title":"Song: Approximate nearest neighbor search on gpu","venue":null,"work_id":"61e52d19-74a8-408e-8a52-184e97cb4289","year":2020},"citing_paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T04:38:34.676450Z"},"links":{"citing_paper":"/paper/2509.06046"},"observation_digest":"sha256:12bca116dad1b22e22bd703076ef890ac712ae82e7bbc0203231087fd3e66b0c","observation_id":"dd5545bf-6d1f-483a-8c5b-354334f5e1ca","resolution":{"observed_at":"2026-08-05T04:38:34.952429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.06046","last_updated":"2025-09-07T13:13:02Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-15T03:54:16.260839Z","submitted_at":"2025-09-07T13:13:02Z","title":"DISTRIBUTEDANN: Efficient Scaling of a Single DISKANN Graph Across Thousands of Computers"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":24},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 24 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2509.06046."}