{"as_of":"2026-08-09T21:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:845fecb842736425db50dcd266e44d69bf753eda91ed496409875e80aeaa71db","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T13:51:32.235849Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T03:42:19.894756Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"cited_work":{"arxiv_id":"2512.22838","doi":"10.48550/arxiv.2512.22838","metadata_source":"arxiv_reference","pith_arxiv_id":"2512.22838","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"faf5ea7e-b879-4360-92fb-01f68339880b","year":2025},"citing_paper":{"arxiv_id":"2605.05787","last_updated":"2026-05-07T07:25:47Z","snapshot_observed_at":"2026-08-03T04:34:54.108753Z","submitted_at":"2026-05-07T07:25:47Z","title":"Low-Latency Out-of-Core ANN Search in High-Dimensional Space","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T03:42:19.894756Z"},"links":{"cited_paper":"/paper/2512.22838","citing_paper":"/paper/2605.05787"},"observation_digest":"sha256:b304c38f0be469e27ae091e1237dc76a2bae0cc292b545af82a44c5a6dc2fd24","observation_id":"631119ec-ebbf-49d6-82d0-7759a1031b84","resolution":{"observed_at":"2026-07-08T02:18:09.499591Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2512.22838/citation-record","integrity":"/paper/2512.22838/integrity","json":"/paper/2512.22838/citation-record.json","paper":"/paper/2512.22838"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.948575Z","title":"Microsoft copilot and anthropic claude ai in education and library service","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.948575Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:2a1e15cbc115f57e1efdc83c4a7c6d3c0ad66fdb2cbef258d09e32b227b1eddb","observation_id":"7b8960ca-a692-49b4-9d4b-6128d1289157","resolution":{"observed_at":"2026-08-03T13:51:31.948575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.954468Z","title":"Achieving sub-second pairwise query over evolving graphs","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.954468Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:26af75ca797b2d73a8f70c2fc5dfbd7b2eca5197167511ec9acef3e199112379","observation_id":"b73dfe33-c185-4d4b-a15c-bdb55e190b5e","resolution":{"observed_at":"2026-08-03T13:51:31.954468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.959052Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.959052Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:a5a75ce398248168d23547c11fc5d0fb52218f8fdffb9d303d34b37075b223cb","observation_id":"cff4cd78-95e6-4fdd-8e73-b7dded5718c9","resolution":{"observed_at":"2026-08-03T13:51:31.959052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.964688Z","title":"Sptag: A library for fast approximate nearest neighbor search, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.964688Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:577a849204d4eb92324fc7b1bdbdc34d40e02e5c7516f8d68175fd2d243c9905","observation_id":"6eca795c-eca2-48a0-9aea-ed380ed76d37","resolution":{"observed_at":"2026-08-03T13:51:31.964688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.970073Z","title":"Spann: Highly-efficient billion-scale approximate nearest neighborhood search.Advances in Neural Information Processing Systems, 34:5199–5212, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.970073Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:fd2898c19ebfd950210403a2e959a1789f349582f525e61c941bff9c8734e4da","observation_id":"b52b93b7-6c5a-4123-ad19-644fbb79f2bb","resolution":{"observed_at":"2026-08-03T13:51:31.970073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23805","last_updated":"2025-08-20T06:16:28Z","snapshot_observed_at":"2026-07-06T19:42:47.238254Z","submitted_at":"2024-10-31T10:45:02Z","title":"UpANNS: Enhancing Billion-Scale ANNS Efficiency with Real-World PIM Architecture","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.23805","snapshot_observed_at":"2026-08-03T13:51:31.975266Z","title":"Memanns: Enhancing billion-scale anns efficiency with practical pim hardware.arXiv preprint arXiv:2410.23805, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.975266Z"},"links":{"cited_paper":"/paper/2410.23805","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:077508c03948cce200e7c991ca8117c63797f3624918c61b918c6465d58fce3a","observation_id":"6e3fd87c-9a73-4018-9fda-c6def9014cc1","resolution":{"observed_at":"2026-08-03T13:51:31.975266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.980696Z","title":"Onesparse: A unified system for multi-index vector search","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.980696Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:14feb8e31b70b3a2c13c764fc24df381a3e3432292ea857847ba6384ab49e268","observation_id":"347eea86-e8bf-4185-92e9-61e07d0f7cd7","resolution":{"observed_at":"2026-08-03T13:51:31.980696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16179","last_updated":"2024-12-18T17:36:36Z","snapshot_observed_at":"2026-07-06T19:37:11.636987Z","submitted_at":"2024-10-21T16:44:51Z","title":"MagicPIG: LSH Sampling for Efficient LLM Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.16179","snapshot_observed_at":"2026-08-03T13:51:31.985752Z","title":"Magicpig: Lsh sampling for efficient llm generation.arXiv preprint arXiv:2410.16179, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.985752Z"},"links":{"cited_paper":"/paper/2410.16179","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:e73aba67af6c2ba0a8df029ab7a2e157aeb664ccd3e62f1b00a59df63b918425","observation_id":"6a53338a-331b-438c-a910-9d65a8fc070c","resolution":{"observed_at":"2026-08-03T13:51:31.985752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.990860Z","title":"Deep neural networks for youtube recommendations","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.990860Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:b1cd11c48201cbf9baac025ca95e10f7fd54eb5e08291d8a02aec2d720100d46","observation_id":"5739dec8-c439-4f2d-b22f-5e3e4d5218e2","resolution":{"observed_at":"2026-08-03T13:51:31.990860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:31.995879Z","title":"The power of noise: Redefining retrieval for rag systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:31.995879Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:4e026d1770edc711ae8d7f51b8e0aa4f810121f5fe8d23007207e6ca51447cdb","observation_id":"0fc51e78-090a-46df-8701-e7bf69ebdd20","resolution":{"observed_at":"2026-08-03T13:51:31.995879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.10326","last_updated":"2025-04-14T15:34:26Z","snapshot_observed_at":"2026-08-07T16:05:50.584747Z","submitted_at":"2025-04-14T15:34:26Z","title":"AlayaDB: The Data Foundation for Efficient and Effective Long-context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10326","snapshot_observed_at":"2026-08-03T13:51:32.000242Z","title":"Alayadb: The data foundation for efficient and effective long-context llm inference.arXiv preprint arXiv:2504.10326, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.000242Z"},"links":{"cited_paper":"/paper/2504.10326","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:f84e866f90fa6f4af17811a52bd699df3db0c32a0f70907342f90b5f9d5d463e","observation_id":"92814cf7-8f3b-4fa5-8a92-7dcd66546209","resolution":{"observed_at":"2026-08-03T13:51:32.000242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.006719Z","title":"Risgraph: A real-time streaming system for evolving graphs to support sub-millisecond per-update analysis at millions ops/s","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.006719Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:f61c5dcdb40cd760c8288912255b4eaebda521e3955d58659ee3da4cbc84cf9f","observation_id":"4f6e4608-61c4-4845-8bbd-e3e9548b21a9","resolution":{"observed_at":"2026-08-03T13:51:32.006719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.00143","last_updated":"2025-07-06T11:35:42Z","snapshot_observed_at":"2026-07-06T05:49:21.582343Z","submitted_at":"2017-07-01T11:52:37Z","title":"Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.00143","snapshot_observed_at":"2026-08-03T13:51:32.020328Z","title":"Fast approximate near- est neighbor search with the navigating spreading-out graph.arXiv preprint arXiv:1707.00143, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.020328Z"},"links":{"cited_paper":"/paper/1707.00143","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:a5b90641d486e76927899d64d90e712c31c28fffe3be81577c49c5eac72bf0d5","observation_id":"675626a3-707e-465b-9827-d897c0d84702","resolution":{"observed_at":"2026-08-03T13:51:32.020328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.025952Z","title":"Fast approximate nearest neighbor search with the navigating spreading-out graph.Proc","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.025952Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:1cb4d23b31980767f21cb9ff2a68a6c2f2ca0219859000f7825acb0fd6afdd00","observation_id":"de330a51-a981-449a-a018-cc207c9fd74c","resolution":{"observed_at":"2026-08-03T13:51:32.025952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.030512Z","title":"Practical and asymptotically optimal quantization of high- dimensional vectors in euclidean space for approximate nearest neighbor search","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.030512Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:74962ea9cc9d23fdb56e052dd308dde99c41ec8f62e5726d0efe458a60b547ca","observation_id":"0454e062-ec6b-4105-aab8-f92bc16d12c2","resolution":{"observed_at":"2026-08-03T13:51:32.030512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.036842Z","title":"Rabitq: quantizing high-dimensional vectors with a theoretical error bound for approximate nearest neighbor search.Proceedings of the ACM on Management of Data, 2(3):1–27, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.036842Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:91b7a16e3da16d34eb066b6968c223b04f35dc836beaa0856f13134e8b92402d","observation_id":"fb20608f-29b4-4378-8f90-56e0dfcd48ce","resolution":{"observed_at":"2026-08-03T13:51:32.036842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.041267Z","title":"Symphonyqg: Towards symphonious integration of quantization and graph for approximate nearest neighbor search.Proceedings of the ACM on Management of Data, 3(1):1–26, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.041267Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:0a29d3b64b8d501e12c127a7013108b0081ead047b32a04ca0669a8ea725014a","observation_id":"a8d3a3fe-a455-4110-81b9-67cf7dcf67a5","resolution":{"observed_at":"2026-08-03T13:51:32.041267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.045719Z","title":"Ggnn: Graph-based gpu nearest neighbor search.IEEE Transactions on Big Data, 9(1):267–279, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.045719Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:bfe8d68289d1c38c4849d0279b1fccc07742c8a7b678658f1e19131dbc439d4c","observation_id":"a10b43fa-4ab3-496b-a25e-5b3aafc2bca5","resolution":{"observed_at":"2026-08-03T13:51:32.045719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.050094Z","title":"Achieving {Low-Latency}{ Graph-Based} vector search via aligning{Best-First} search algorithm with{SSD}","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.050094Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:e5e2c58d5343b9a4ece5eac79a0f7a96f4f2ebe82e11f557f0d4d4e950f68ffe","observation_id":"736ddd58-c12e-4e43-913c-75fdb1b0ac73","resolution":{"observed_at":"2026-08-03T13:51:32.050094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.055056Z","title":"Hedrarag: Co-optimizing generation and retrieval for hetero- geneous rag workflows","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.055056Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:76b10f1b0c1bfb42ce207514c4edc144dbd4ec58016c384f83f8b4c67d60aced","observation_id":"a4a09440-d406-4561-92ae-c828de57aa30","resolution":{"observed_at":"2026-08-03T13:51:32.055056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.061164Z","title":"In2023 USENIX Annual Technical Conference (USENIX ATC 23), pages 585–600, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.061164Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:742513bde790feadff350ac1b927d22c74e60e58e698cecbc1fe04a49b652773","observation_id":"271e7d9f-ada3-40b0-a578-0ac6b7ece976","resolution":{"observed_at":"2026-08-03T13:51:32.061164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.066246Z","title":"Diskann: Fast accurate billion-point nearest neighbor search on a single node.Advances in neural information processing Systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.066246Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:fdfc06a450a371191493954e4704ff70fc8ded409b3a2e8dc46692bd8c19731d","observation_id":"0cb3befe-b2ac-4bb6-a651-435a84b9e83d","resolution":{"observed_at":"2026-08-03T13:51:32.066246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.071044Z","title":"Product quantization for nearest neighbor search.IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1):117–128, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.071044Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:1ce41c358c4836567f37c3a55d3425ebd47ff27613b4c3e3086f01a11a1c1ebf","observation_id":"adf3332f-d598-42e8-b7e2-ff96559db149","resolution":{"observed_at":"2026-08-03T13:51:32.071044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.076056Z","title":"Billion-scale similarity search with gpus.IEEE Transactions on Big Data, 7(3):535–547, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.076056Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:e70e5964c2abf06c012acdc21b40ca7b8139fc3656889d5a109da15f5c93e003","observation_id":"6a06611d-1eda-43aa-af87-ef9528bc56a7","resolution":{"observed_at":"2026-08-03T13:51:32.076056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.03551","last_updated":"2017-05-13T21:12:37Z","snapshot_observed_at":"2026-08-02T11:13:42.401488Z","submitted_at":"2017-05-09T21:35:07Z","title":"TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.03551","snapshot_observed_at":"2026-08-03T13:51:32.081140Z","title":"Weld, and Luke Zettlemoyer","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.081140Z"},"links":{"cited_paper":"/paper/1705.03551","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:bd29b4ad43410b76b0814f789af95e01856905e5fd076e30f7becc4fdf5cc06c","observation_id":"a28c8812-8f9b-4392-8bea-fd75b89899c9","resolution":{"observed_at":"2026-08-03T13:51:32.081140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.086680Z","title":"Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.086680Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:1d92f77d5f19cd017ef8d65b3eb0cbc2a95c92e259267cf3d239037eab94a0d5","observation_id":"d96f2a48-f3a1-461f-afd6-35b048a11b03","resolution":{"observed_at":"2026-08-03T13:51:32.086680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.091791Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.091791Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:d3a5f2ab2f7db3ad2fa342069a6ccfa4c7ac7041a33a3989979e5414c26e1970","observation_id":"ed611c32-76a0-4efd-9e6c-9bd6a40623b0","resolution":{"observed_at":"2026-08-03T13:51:32.091791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.096193Z","title":"Ansmet: Approximate nearest neighbor search with near-memory processing and hybrid early termination","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.096193Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:a56b9ee6277ed6e460d9f8079c92ebb5a1598c7d164c59184a0524413a5d8f2d","observation_id":"c4ef36c4-f9d9-4df3-a8c1-5246fc3e164c","resolution":{"observed_at":"2026-08-03T13:51:32.096193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-03T13:51:32.100312Z","title":"Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.100312Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:07da76f6b4807e6662654a5a100b392f4ccb5f2486f45bda2c28980350eb4a5d","observation_id":"1ea031fa-db6d-4c77-98fd-40061f6b337e","resolution":{"observed_at":"2026-08-03T13:51:32.100312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10516","last_updated":"2024-12-31T07:11:00Z","snapshot_observed_at":"2026-08-07T09:13:28.333702Z","submitted_at":"2024-09-16T17:59:52Z","title":"RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10516","snapshot_observed_at":"2026-08-03T13:51:32.105366Z","title":"Retrievalattention: Accelerating long-context llm inference via vector retrieval.arXiv preprint arXiv:2409.10516, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.105366Z"},"links":{"cited_paper":"/paper/2409.10516","citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:3daf7983110d6dd5b4e08a4c343d2a11d37eb2fbcba17f310f2e2704e23e908a","observation_id":"8a3df2a8-96b4-4863-b7cf-3b9461b02289","resolution":{"observed_at":"2026-08-03T13:51:32.105366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.110409Z","title":"Malkov and D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.110409Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:4bb1f5887adc0ab016260d1c9cbd0af56f956c788448279561407e39c4cca7aa","observation_id":"2b0f70c0-d545-48ee-a892-e9a01e9c4844","resolution":{"observed_at":"2026-08-03T13:51:32.110409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.114819Z","title":"Parlayann: Scalable and deter- ministic parallel graph-based approximate nearest neighbor search algorithms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.114819Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:d1cbc9a172633d198fd3dc07282835b1ac01a18a300ea6da954deb393c2b8b89","observation_id":"ac47b1b7-6dce-482e-b523-39e4eab91134","resolution":{"observed_at":"2026-08-03T13:51:32.114819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.119256Z","title":"Ilyas, Theodoros Rekatsinas, and Shivaram Venkataraman","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.119256Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:915f7271ee335170a96fc9695a45faafee7f81da9191dba016521e1a98553073","observation_id":"01f0f1c5-b518-4ac0-b81c-fbbeb0099d56","resolution":{"observed_at":"2026-08-03T13:51:32.119256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.123519Z","title":"Exploring dis- tributed vector databases performance on hpc platforms: A study with qdrant","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.123519Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:9cc54d46de30d12f01096dc5d9397beb1e0a2cf28e962c98f143cd08855dc13d","observation_id":"e91459fc-9050-431e-8148-636cfc5e67e5","resolution":{"observed_at":"2026-08-03T13:51:32.123519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.128007Z","title":"Embedding- based news recommendation for millions of users","venue":null,"work_id":null,"year":1933},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.128007Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:6e6e040fbe334a71a31bbfe13f4227d430c7fb32bbcbb027df6edb19d57f3556","observation_id":"5c0b000d-e538-4806-9552-d37b1a761a11","resolution":{"observed_at":"2026-08-03T13:51:32.128007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.132425Z","title":"Cagra: Highly parallel graph construction and approximate nearest neighbor search for gpus","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.132425Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:8c181b1561168c518b4ae7f6690a880ace0d2dbff796a794d44205dd63b03e28","observation_id":"435926a0-fd08-4895-b30c-a1aafd831289","resolution":{"observed_at":"2026-08-03T13:51:32.132425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.137070Z","title":"Openai developer platform, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.137070Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:4aa8279a24776c577d1138d088b2fbec08d37c315b935563a38ae017cc8a55b3","observation_id":"dfda6a9f-7771-471f-8150-af57b6c01c6f","resolution":{"observed_at":"2026-08-03T13:51:32.137070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.142067Z","title":"Vector database management techniques and systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.142067Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:1953f8ad84e0bd5d75bd3406324d643a140fc2e6f2cb136c0892d04796885dfc","observation_id":"763cdb8e-6036-408e-9d76-69e676b105a7","resolution":{"observed_at":"2026-08-03T13:51:32.142067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.146561Z","title":"Chatgpt and open-ai models: A preliminary review.Future Internet, 15(6):192, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.146561Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:a36c2bced6508998706aa15305cbfc552c5000bdd050d44fc5f7cdfa3e538cb7","observation_id":"4d2b52af-8430-4515-b948-27b8f2cac140","resolution":{"observed_at":"2026-08-03T13:51:32.146561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.150863Z","title":"DiskANN: Graph-structured Indices for Scalable, Fast, Fresh and Filtered Approximate Nearest Neighbor Search, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.150863Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:6c59b2965bb0e5dce97ef2b20694c87b617f05a981a740da7ac3e622499a26d3","observation_id":"17e8c414-b6ad-47d5-bf21-50817316935c","resolution":{"observed_at":"2026-08-03T13:51:32.150863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.154812Z","title":"Scalable billion-point approximate nearest neighbor search using{SmartSSDs}","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.154812Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:a287d6f5d0d72c69f18434cca26c2fb93b260a2cb3ff626ccd37abb8ebf67810","observation_id":"9a565758-fccf-45d1-8e08-2f33de4a427f","resolution":{"observed_at":"2026-08-03T13:51:32.154812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.158718Z","title":"Towards high-throughput and low-latency billion-scale vector search via{CPU/GPU} collaborative filter- ing and re-ranking","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.158718Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:239c2a805e0a9199176dc974ccf63f4cd46dcf8f712fc674b8f6b1862061f7bb","observation_id":"4a698de2-a66b-4c5c-bf9b-00da695b557f","resolution":{"observed_at":"2026-08-03T13:51:32.158718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.163000Z","title":"Mirage-anns: Mixed approach graph-based indexing for approximate nearest neighbor search.Proceedings of the ACM on Management of Data, 3(3):1–27, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.163000Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:97b36bdd4d60f7b010be1086c3d8ebc062a7423cb36e880fc3ba395d9eed0f67","observation_id":"bc2eebd3-9cf1-489a-b912-16da9a9ba7c4","resolution":{"observed_at":"2026-08-03T13:51:32.163000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.167315Z","title":"Milvus: A purpose-built vector data management system","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.167315Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:d63d7c0d48af9b15f95b0bcd5db19483f88e1c8daf9a4c6ad3a491565c5f63cb","observation_id":"dbb2e8f6-ff45-4352-a308-e6db3bf759ef","resolution":{"observed_at":"2026-08-03T13:51:32.167315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.171912Z","title":"Accelerating graph indexing for anns on modern cpus.Proceedings of the ACM on Management of Data, 3(3):1–29, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.171912Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:86f0f96dac35c1727d44d67533af5e2635e209e2492b0dd4a5539795b1241c6c","observation_id":"17d7ae8d-6449-4ee7-ac0f-bc08134f57bf","resolution":{"observed_at":"2026-08-03T13:51:32.171912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.175917Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.175917Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:65a0e130b46bb9671d54ae26d4ed4012a99cae2b41c9884b5df7d162d674692a","observation_id":"21bed2e7-67cc-4b73-9fc4-1ba26b83b15b","resolution":{"observed_at":"2026-08-03T13:51:32.175917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.180319Z","title":"Deltapq: lossless product quantization code compression for high dimensional similarity search.Proceedings of the VLDB Endowment, 13(13):3603–3616, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.180319Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:63875f42ac45a91716546b72e75bb19e09f3e74aad98a4666da92254eafa3c81","observation_id":"68e11c5d-ce96-40e3-80d0-55f05e22567c","resolution":{"observed_at":"2026-08-03T13:51:32.180319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.184385Z","title":"Det-lsh: a locality- sensitive hashing scheme with dynamic encoding tree for approximate nearest neighbor search.arXiv preprint arXiv:2406.10938, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.184385Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:ae3a991191d502894eb73c55e561f4d6544235f5b70ccedbaaca88459df1d2ad","observation_id":"52b7a4db-95f6-455c-bd25-86d32f0a1555","resolution":{"observed_at":"2026-08-03T13:51:32.184385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.189176Z","title":"Turbocharge anns on real processing-in-memory by enabling fine-grained per-pim-core scheduling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.189176Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:6508a0f2f16120ab831cfc8abc0fb595d35a158b65b4134c010c5e6f0d30ae89","observation_id":"9e6cb545-f4db-4790-af30-209f57fb4a31","resolution":{"observed_at":"2026-08-03T13:51:32.189176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.193531Z","title":"C-pack: Pack- aged resources to advance general chinese embedding, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.193531Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:b509ebd5cb1ea6945ca6e8a39ff6018b514a4a0fca0f558d34ea204c6853f2c0","observation_id":"1845524b-1ab7-4bb0-ba96-163a3e1de6b8","resolution":{"observed_at":"2026-08-03T13:51:32.193531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.197768Z","title":"Tribase: A vector data query engine for reliable and lossless pruning compression using triangle inequalities.Proceedings of the ACM on Management of Data, 3(1):1–28, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.197768Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:2ec3d3b91a56b44cbd3115ed69bbb1f2dd4fd53494caa79324b444802e1efaeb","observation_id":"7b19c413-86ca-48b6-a9e7-8017900c9c87","resolution":{"observed_at":"2026-08-03T13:51:32.197768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.201888Z","title":"Spfresh: Incremental in-place update for billion-scale vector search","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.201888Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:b6c4430ed2bf322ab4d0289f5d7c8ac188a704257517166bed1bee6080a39028","observation_id":"57915785-0577-4f42-a0f5-61112d212dba","resolution":{"observed_at":"2026-08-03T13:51:32.201888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.205956Z","title":"Cohen, Ruslan Salakhutdinov, and Christopher D","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.205956Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:49fb7dde87716166abda6fa4c9ab6f5386a35629f7b7cfe766f1cc3a7cfd8808","observation_id":"eb00825a-9851-4165-96f7-544004684ac8","resolution":{"observed_at":"2026-08-03T13:51:32.205956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.210095Z","title":"Cacheblend: Fast large language model serving for rag with cached knowledge fusion","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.210095Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:e19c5526131b9dd90259fafcfe9599929e19901529a9d6b4c496b74bfe198843","observation_id":"a48a13fd-e17f-4717-8d50-07211a1bce47","resolution":{"observed_at":"2026-08-03T13:51:32.210095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.214125Z","title":"Gpu-accelerated proximity graph approximate nearest neighbor search and construction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.214125Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:7aab163962cb03887e597d1331e5d5e39b88f56246f435b8090fca51f9a8f925","observation_id":"74663c83-3687-4b64-a910-bbe0265a9aff","resolution":{"observed_at":"2026-08-03T13:51:32.214125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.218254Z","title":"Pqcache: Product quantization-based kvcache for long context llm inference.Proceedings of the ACM on Management of Data, 3(3):1–30, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.218254Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:b306ac5cee8dd857ff73bade467db180fb949603766361ce20913e1d510bb22f","observation_id":"355cd659-fef3-41b5-9b4d-0e1d46d3b85b","resolution":{"observed_at":"2026-08-03T13:51:32.218254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.222472Z","title":"Uni- retriever: Towards learning the unified embedding based retriever in bing spon- sored search","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.222472Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:90a1ce48e9fa15e4943b5d811196d6ea3ca567e7ace2f156a4c44ae672d276a8","observation_id":"7b998195-d9fb-438d-979b-d917534fc478","resolution":{"observed_at":"2026-08-03T13:51:32.222472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.226825Z","title":"{VBASE}: Unifying online vector similarity search and relational queries via relaxed monotonicity","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.226825Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:86bd1618a5531e5826356cb72988ad2617c35fe30f1be296a45f63643dc69feb","observation_id":"7517f8a1-265e-450a-8c9a-5b6fd18e3cd3","resolution":{"observed_at":"2026-08-03T13:51:32.226825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.231475Z","title":"Fast vector query processing for large datasets beyond {GPU} memory with reordered pipelining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.231475Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:4a5e26a91c8a096e6677d5a4f13e172d5c25bcc8fc113fc03616b7a261f664cc","observation_id":"72604198-59de-440c-bf80-de9fc593df41","resolution":{"observed_at":"2026-08-03T13:51:32.231475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T13:51:32.235849Z","title":"Gts: Gpu-based tree index for fast similarity search.Proceedings of the ACM on Management of Data, 2(3):1–27, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-03T13:51:32.235849Z"},"links":{"citing_paper":"/paper/2512.22838"},"observation_digest":"sha256:05f26d12f31ea2df74b5431c75c6b59227bffdb5a0bdeeaa1442b89dd9da6621","observation_id":"7d6bcf29-da92-418d-af62-e89743f83067","resolution":{"observed_at":"2026-08-03T13:51:32.235849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.22838","last_updated":"2026-07-07T09:01:40Z","latest_version":2,"primary_category":"cs.DB","snapshot_observed_at":"2026-08-03T13:51:30.167172Z","submitted_at":"2025-12-28T08:42:38Z","title":"OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":60,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":60},"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 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2512.22838."}