{"as_of":"2026-08-21T19:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:834569563d8ac091160e845ac2230cfee3c3efaf3ee9accf901cc832a2198ea3","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:52:34.115690Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.11783/citation-record","integrity":"/paper/2505.11783/integrity","json":"/paper/2505.11783/citation-record.json","paper":"/paper/2505.11783"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:52:34.761040Z","title":"Cowbird: Freeing cpus to compute by offloading the disaggregation of memory","venue":null,"work_id":"9057ce61-e1be-4986-a39e-eb10e207d112","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.910063Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:21680640439559898f41d6ea74e610afb1d6c2aa5c2636beec6b858594cfe9eb","observation_id":"d6bd7f6c-649b-4ec4-91e0-3ee4aa97abc2","resolution":{"observed_at":"2026-08-15T20:52:34.764903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.748683Z","title":"CloudLab: Flexible","venue":null,"work_id":"45de8050-d2dc-457c-8b91-8d760df6f3c9","year":null},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.914468Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:12c7cfbf90365fb63539d68ccdc23ae00badf0546bd42776e3a3c0aa7e28f79b","observation_id":"86f66c99-d01f-4375-8271-48348da0336e","resolution":{"observed_at":"2026-08-15T20:52:34.752633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.736802Z","title":"https://www.deepseek.com/","venue":null,"work_id":"b19dfd81-22d4-4376-81a7-dbeb20fff5a4","year":null},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.918435Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:6d225a7390d9acb7fea2d2730b077f9b328d65bdf04745908a09d75cbec2fc3e","observation_id":"93a93c8f-5552-4e1c-906f-cce5f73e5972","resolution":{"observed_at":"2026-08-15T20:52:34.740872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.725740Z","title":"Pyramid: A general framework for distributed similarity search on large-scale datasets","venue":null,"work_id":"49028782-aca0-4ca4-9475-c6772b62629f","year":2019},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.922595Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:26a815d62b78d8e333bba665f22f7ac1a097200b1af9d851c62cb1431bdf2ee3","observation_id":"a559de39-14e7-477e-b4e8-6e1eba1e521c","resolution":{"observed_at":"2026-08-15T20:52:34.729697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.713821Z","title":"https://github.com/deepseek-ai/3fs/","venue":null,"work_id":"d6e4d102-cd5a-4695-9624-4fb19d862974","year":null},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.926579Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:76a400e32ccd72c7a50a61828d3e46e3355e200e720df75c6435dbe7fe0afbeb","observation_id":"08d8a345-9167-40ca-8753-dafb6174eb9a","resolution":{"observed_at":"2026-08-15T20:52:34.717708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.700320Z","title":"Fast approximate nearest neighbor search with the navigating spreading-out graphs","venue":null,"work_id":"bf8538b6-2e48-495e-9358-d7d431f76455","year":2019},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.930912Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:d96ef209ccbdb09bb9423809410454bc85428e340ac10b6f0a2873a3d030fd21","observation_id":"b3cf5196-5fa6-4f18-8636-54eebbbd4a16","resolution":{"observed_at":"2026-08-15T20:52:34.704932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:33.935173Z","title":"Similarity search in high dimensions via hashing","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.935173Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:9c22013ce0ea4430524ba63388bfd759921d5bb2776692ce8c585f6bdd7b74a9","observation_id":"67a59c73-e46b-47da-bc1e-b0dc633b68c3","resolution":{"observed_at":"2026-08-15T20:52:33.935173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17565","last_updated":"2024-12-21T13:55:49Z","snapshot_observed_at":"2026-08-18T10:15:29.605221Z","submitted_at":"2024-06-25T14:02:08Z","title":"MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17565","snapshot_observed_at":"2026-08-15T20:52:33.939375Z","title":"Memserve: Context caching for disaggregated llm serving with elastic memory pool.arXiv preprint arXiv:2406.17565, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.939375Z"},"links":{"cited_paper":"/paper/2406.17565","citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:b715f9e6e2d8f973493c6e5056713491a78ace61f5532e8e2301e4c86bc43370","observation_id":"335661e9-079e-4294-aafa-37358d1b0e19","resolution":{"observed_at":"2026-08-15T20:52:33.939375Z","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":"2026-08-15T20:52:34.678158Z","title":"Cxl-anns:software-hardware collaborative memory disaggregation and computation for billion- scale approximate nearest neighbor search","venue":null,"work_id":"b9d0fd5d-33fb-453b-9fb1-50a9bbc06d4c","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.944518Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:a8b9b6b17a7f05be4bc97d714e8e56ccbf3d5e1af1b082731df8913d988a6b2e","observation_id":"bf42e775-488a-480c-8bde-69c48915798d","resolution":{"observed_at":"2026-08-15T20:52:34.682536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.12457","last_updated":"2024-04-25T06:47:57Z","snapshot_observed_at":"2026-08-19T19:49:59.264961Z","submitted_at":"2024-04-18T18:32:30Z","title":"RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.12457","snapshot_observed_at":"2026-08-15T20:52:33.950227Z","title":"Ragcache: Efficient knowledge caching for retrieval- augmented generation.arXiv preprint arXiv:2404.12457, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.950227Z"},"links":{"cited_paper":"/paper/2404.12457","citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:84c9490ffa1c992f69ac57628848be45042dcb4f42e5f39d73a4f4f239dc2d54","observation_id":"38a314fc-8bad-4012-9230-2b05e54131dc","resolution":{"observed_at":"2026-08-15T20:52:33.950227Z","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":"2026-08-15T20:52:34.665552Z","title":"Design guide- lines for high performance rdma systems","venue":null,"work_id":"d53d0f21-d8b9-4f1e-a45f-c1e457db400c","year":2016},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.955951Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:1e689ce6c4eadffb8ded81b6632c1c98132832fc09f69111c18cbc00bf1970fb","observation_id":"ade1bede-8003-4cdb-8427-957df2441aee","resolution":{"observed_at":"2026-08-15T20:52:34.669741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.651736Z","title":"Im- proving approximate nearest neighbor search through learned adaptive early termination","venue":null,"work_id":"e39de0a4-01d1-4e4d-b912-d1694e8fc65a","year":2020},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.960892Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:db2edb921eb32685137f63634ce151e8905c1b170c395ca1c94daafda9577945","observation_id":"2a6519d5-9539-49b7-ad93-bbbc8dd281aa","resolution":{"observed_at":"2026-08-15T20:52:34.657150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.639004Z","title":"Rolex: A scalable rdma-oriented learned key-value store for disaggre- gated memory systems","venue":null,"work_id":"34aa752b-5bb7-4bdf-afab-d2ef334d465a","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.966127Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:1213d8e63c78ee208b6eec1158b2d9fe8da0509652d60e09dbdce8818f0a1cae","observation_id":"f2a2160f-e685-4941-90de-4cd0eedd6525","resolution":{"observed_at":"2026-08-15T20:52:34.643536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.626617Z","title":"https://github.com/facebookresearch/faiss","venue":null,"work_id":"3a288e13-be88-487b-ac28-f9fd9286ee25","year":null},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.970690Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:a380ed004ad75fc9fc39851998d99353d43e2676afebefdd44e104649913cd30","observation_id":"bc12b0b8-b25f-406e-af17-39b28c9586cc","resolution":{"observed_at":"2026-08-15T20:52:34.630930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10516","last_updated":"2024-12-31T07:11:00Z","snapshot_observed_at":"2026-08-17T15:34:12.654681Z","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":"2026-08-15T20:52:33.974786Z","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":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.974786Z"},"links":{"cited_paper":"/paper/2409.10516","citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:bb5c4b26f9f5647958f9c08ae2c4d7489d7618af9e2c083dae41c8dd35af20a3","observation_id":"d019bcb4-afcc-40d1-9e58-2c8b23e2c191","resolution":{"observed_at":"2026-08-15T20:52:33.974786Z","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":"2026-08-15T20:52:34.609374Z","title":"Outback: Fast and communication-efcient index for key-value store on disaggregated memory.PVLDB, 18(2):335 – 348, 2024","venue":null,"work_id":"3110fa82-8d18-4f2a-bfbf-e7bfd3456d3d","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.978837Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:298b56534391ce67a22b2e0a541bb01732bfc453bb8e40e0d3b65e44d4111ada","observation_id":"1d7c709d-d00f-434a-8c1d-5cd4030423c8","resolution":{"observed_at":"2026-08-15T20:52:34.613953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.596580Z","title":"Dex: Scalable range indexing on disaggregated memory","venue":null,"work_id":"2aa22ac9-fc9f-451d-8a98-f8cb9fb2a03d","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.982043Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:72880990f61b07d8e6f27669af31397aaee6749a7e75a999264b69e31b1a3fe3","observation_id":"22b41958-8f3d-4da7-9cd8-4da59093c1ad","resolution":{"observed_at":"2026-08-15T20:52:34.600641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.583912Z","title":"Chime: A cache-efficient and high-performance hybrid index on disaggregated memory","venue":null,"work_id":"110a33dd-1a74-4aaf-88c3-fdc37fa37db2","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.986109Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:388e24e7589c1cae785b5edcebdbcde518307950f53d5b86be4558ba772c7a29","observation_id":"518f9dad-8e8f-4d4b-87ea-bb598f50817a","resolution":{"observed_at":"2026-08-15T20:52:34.587934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.571344Z","title":"Smart: A high-performance adap- tive radix tree for disaggregated memory","venue":null,"work_id":"563ea928-2e0e-416f-9717-1bc43dd019e1","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.989878Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:f7c91d1fefe765acb0616784ca28a95f0cce760a1ddea5df7f30debc0db23dae","observation_id":"b2e6f496-b5e2-40fd-89c7-af0b2104e787","resolution":{"observed_at":"2026-08-15T20:52:34.575649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.554409Z","title":null,"venue":null,"work_id":"d0cd24e3-fc54-452e-9878-f4d929a3c7a3","year":2018},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.993497Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:176effa4963ca3b0050601ba0a77c357961b906aecce5de7b6c7eaba0347bde7","observation_id":"2006c337-0469-40ae-9e61-8f2b090bc692","resolution":{"observed_at":"2026-08-15T20:52:34.559175Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.539404Z","title":"Revisit- ing network support for rdma","venue":null,"work_id":"0c391593-dfc5-4796-a008-628365eee17e","year":2018},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:33.997199Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:3e9a6fb953697497444bd5c237f1f64c690a4984725286e325fb1743d2600fcb","observation_id":"74fd96c8-436c-445a-ae16-8a2c7412bd97","resolution":{"observed_at":"2026-08-15T20:52:34.544246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.001119Z","title":"Survey of vector database management systems.The VLDB Journal, 33(5):1591–1615, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.001119Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:14d9d3d974016ddd139175f732b49ba03df82f9ca9520c82b4b9ebf4c8306a96","observation_id":"4834bed8-a43e-4956-9b13-9f0c55e7fa6e","resolution":{"observed_at":"2026-08-15T20:52:34.001119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00079","last_updated":"2025-09-03T14:56:29Z","snapshot_observed_at":"2026-08-17T18:39:57.612444Z","submitted_at":"2024-06-24T02:05:32Z","title":"Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00079","snapshot_observed_at":"2026-08-15T20:52:34.007092Z","title":"Mooncake: A kvcache-centric disag- gregated architecture for llm serving.arXiv preprint arXiv:2407.00079, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.007092Z"},"links":{"cited_paper":"/paper/2407.00079","citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:80ed6dbf667249248408946c5934a4ee7556726e5c6ac7aff00aa3cfa2a5e6e5","observation_id":"08b13d7d-0b9e-442d-8b4e-5527060ca979","resolution":{"observed_at":"2026-08-15T20:52:34.007092Z","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":"2026-08-15T20:52:34.519401Z","title":"Revisiting kd-tree for nearest neigh- bor search","venue":null,"work_id":"f9710806-76b8-44f4-a3d3-daa03756e99a","year":2019},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.012193Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:343d7d424a008bd836d831ca322c2453de179d5bab5ff46a2d95294237fe7c20","observation_id":"7eba66a2-5119-4424-af4d-865c5005bd4e","resolution":{"observed_at":"2026-08-15T20:52:34.523526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.507217Z","title":"Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory","venue":null,"work_id":"f14bae19-045e-4423-9c2a-45fb18fdacf4","year":2020},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.017893Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:c1e2fd1edfd25b097bcc24ef31c5f730aeff96f9f2470e47ec9f7410dc922fd0","observation_id":"d1c34e68-f41f-4989-bdb8-68b1277c2be0","resolution":{"observed_at":"2026-08-15T20:52:34.511490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.492118Z","title":"Legoos: A disseminated, distributed os for hardware resource disaggregation","venue":null,"work_id":"a5836b6a-b4e5-4ee9-ade5-629411c8c31c","year":2018},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.023095Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:040a8ed28a3c5d7a29b969b5016e36bfc7e07743033d2947f8607cba39e0e6fa","observation_id":"e91fa7ef-09ea-4c96-87f2-26adce47a010","resolution":{"observed_at":"2026-08-15T20:52:34.498073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.479345Z","title":"Ditto: An elastic and adaptive memory-disaggregated caching system","venue":null,"work_id":"7eefc83e-1758-407d-a4c0-807b2c17ae97","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.027530Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:9d2a18c4490d446a290c555ac54eb9e243f53076f5cff106bc68c36edec8f4c3","observation_id":"c24fed35-1897-4f38-8141-98159e82cab3","resolution":{"observed_at":"2026-08-15T20:52:34.483585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.466421Z","title":"Fusee: A fully memory- disaggregatedkey-value store","venue":null,"work_id":"caece5a6-cb28-4371-8f20-95130d7f12c7","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.032090Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:a7a459e038a447caa1df2415c33e7ec50b671c0040ac6107bf02ff4a848e8e05","observation_id":"0d5eea7a-e418-436a-a8d2-dfae248eb09f","resolution":{"observed_at":"2026-08-15T20:52:34.470655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.454573Z","title":"Large language models are learnable planners for long-term recommendation","venue":null,"work_id":"1051b7d2-7318-4aef-a124-b31fda1b1498","year":1903},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.036306Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:03468215c7b183b75b1e7133b409a57ebcd94d23b1c142210c4370175cd0f800","observation_id":"f84334c9-fa30-4636-8555-23c1de67fe90","resolution":{"observed_at":"2026-08-15T20:52:34.458591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.442685Z","title":"Vexless: A serverless vector data management system using cloud functions","venue":null,"work_id":"60b7590d-83fc-481f-a3cd-c751fbb44c45","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.042101Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:9c18351339899d64dcb29d886f073ee06872c7705840f048553123b387469a11","observation_id":"0f3fc51c-ed1a-4af6-a98e-88e9fdedbe70","resolution":{"observed_at":"2026-08-15T20:52:34.446779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.430219Z","title":"Disaggregating persis- tent memory and controlling them remotely: An exploration of passive disaggregated key-value stores","venue":null,"work_id":"46b76ca4-6dca-4d0e-be4d-b0e0b8459bb9","year":2020},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.046731Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:681650891b984d1ecd4fae42a5c1f0af4c41497bd20fe483f5ec5b4c86bf8431","observation_id":"416f23f6-b9ff-4e91-b60c-cb0ed8618be1","resolution":{"observed_at":"2026-08-15T20:52:34.434718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.417925Z","title":"Semeru: A memory-disaggregated managed runtime","venue":null,"work_id":"af4ad311-f1dd-4891-824f-f124e1cce2e2","year":2020},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.050823Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:66a0e85f11ae7cff2eb5a14686eb0dbf4172c75e92e962bc7a653b711ee709be","observation_id":"384a7284-5da7-4a5a-b1b1-c399b66be594","resolution":{"observed_at":"2026-08-15T20:52:34.421909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.404999Z","title":"Vector databases: What’s really new and what’s next?(vldb 2024 panel).Proceedings of the VLDB Endowment, 17(12):4505–4506, 2024","venue":null,"work_id":"fbfd2524-84ef-4ff2-a4c1-765cee98a90d","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.054413Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:c31cb8eec95e0cd776b7ae990665ec2425b227a2c6cd4cc26ee74ae38d1424ba","observation_id":"3a929d2e-c481-4451-bfb4-256485042c5c","resolution":{"observed_at":"2026-08-15T20:52:34.408979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.386006Z","title":"Milvus: A purpose-built vector data management system","venue":null,"work_id":"340975b3-c0ec-4392-a342-b38ab7f1674a","year":2021},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.058752Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:d0fd487acce8cfc70a350607f93d5a51db8ccb94d2259de3eccdb16a54ed5eef","observation_id":"a2eed13e-77a6-49df-b830-cd32e83995f6","resolution":{"observed_at":"2026-08-15T20:52:34.393719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.372008Z","title":"Disaggregated database systems","venue":null,"work_id":"e4af4ad5-01d2-407c-860d-9b9a40690d23","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.062607Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:bf0cecf4fe87fdcbb9543509043b4ac619c9ceecaaba57d9b4c4a074369813d7","observation_id":"5b4da960-0b27-4520-a680-4d11a624a93a","resolution":{"observed_at":"2026-08-15T20:52:34.376415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.359208Z","title":"Sherman: A write-optimized distributed b+ tree index on disaggregated memory","venue":null,"work_id":"c857c3ab-158d-444f-bcbb-53d0ece20876","year":2022},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.067627Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:f7eaec5f0159af9fd73a88b466345ac4af3e67ade385286b4a4256cdb2b1e608","observation_id":"0ba4b9ab-b812-4949-9552-a2c1f7bdcce0","resolution":{"observed_at":"2026-08-15T20:52:34.363906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.03027","last_updated":"2022-07-07T00:45:21Z","snapshot_observed_at":"2026-08-20T13:35:08.487637Z","submitted_at":"2022-07-07T00:45:21Z","title":"The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory Disaggregation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.03027","snapshot_observed_at":"2026-08-15T20:52:34.071729Z","title":"The case for distributed shared-memory databases with rdma-enabled memory disaggregation.arXiv preprint arXiv:2207.03027, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.071729Z"},"links":{"cited_paper":"/paper/2207.03027","citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:b31a14a44b6a3d084f27336ea5c558c8474ca0b65aa0eaae027b0f555d4f5030","observation_id":"e3d32707-e097-4868-b750-f62cf7880f59","resolution":{"observed_at":"2026-08-15T20:52:34.071729Z","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":"2026-08-15T20:52:34.346462Z","title":"Rcmp: Reconstructing rdma-based memory disaggregation via cxl.ACM Transactions on Architecture and Code Optimization, 21(1):1–26, 2024","venue":null,"work_id":"4e8d4fb9-4ac0-4fb4-bf4c-191b4bc038af","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.076829Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:605ebf53dcc724e0c50d497be6d829eccf7b732ddbc9861656587e81c80f9192","observation_id":"412c715e-e773-4d07-acfc-d40bcf32608e","resolution":{"observed_at":"2026-08-15T20:52:34.350480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.333843Z","title":"Characterizing off-path smartnic for accelerating distributed systems","venue":null,"work_id":"c6715a87-5916-4d00-851c-775193ca4813","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.083142Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:6e43e2a3c1efc448d6b6308914c707a1efde7e52cb9a3789b10bb168b17c595e","observation_id":"10dacc6a-b620-4abb-bb2c-eaf529acd505","resolution":{"observed_at":"2026-08-15T20:52:34.338264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.321500Z","title":"Lighttraffic: On optimizing cpu-gpu data traffic for efficient large- scale random walks","venue":null,"work_id":"b6500aeb-5e18-44ad-ae55-daf50cae4962","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.087469Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:cfd50b7c705c50f6f1dc8292f14c0994060e63a27bdee5aa04b87401b53fbbf5","observation_id":"9657c252-865f-42c1-9784-161e04ec62ab","resolution":{"observed_at":"2026-08-15T20:52:34.325659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.309178Z","title":"Motor: Enabling multi- versioning for distributed transactions on disaggregated memory","venue":null,"work_id":"871ce6e1-9a81-47ef-a8ff-c68bf3efd94c","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.091214Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:120d8fa5b566f12f534e8de4c002af3800b81c38f17ef11f0347480070fc7b96","observation_id":"ae9a31ed-e6a6-4d86-8257-d247bffff73d","resolution":{"observed_at":"2026-08-15T20:52:34.313792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.295182Z","title":"Ford: Fast one- sided rdma-based distributed transactions for disaggregated persistent memory","venue":null,"work_id":"d55f6fec-5d19-49f4-a131-7fcc5e237883","year":2022},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.096021Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:6d7dbea91fe29c6b467a354d61aa5cdb3a1eae1d3ca8b4eced8d377e53b7eeae","observation_id":"c37837b9-d6c2-420d-ae7d-cb01a11476e4","resolution":{"observed_at":"2026-08-15T20:52:34.299915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.281088Z","title":"Grip: Multi-store capacity-optimized high-performance nearest neighbor search for vector search engine","venue":null,"work_id":"c5a1a89b-1d47-4d71-8673-f826c0549e10","year":2019},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.100638Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:9462c3e9b01406579b294357c1213b776c6d74e509ef8e2042db277c38abb036","observation_id":"b8aad984-db20-41c3-ad89-df7fe125fb2a","resolution":{"observed_at":"2026-08-15T20:52:34.285584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.267497Z","title":"Fast, approximate vector queries on very large unstructured datasets","venue":null,"work_id":"648ce9aa-2364-4719-88df-61c2ba41255e","year":2023},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.104546Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:da4f548d9b1d0a582990b04c4717ad59a05ff361d211541edd65004372e0e76e","observation_id":"5905d1c0-264f-4304-affc-228fb40a9fc3","resolution":{"observed_at":"2026-08-15T20:52:34.272120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.253390Z","title":"Fast vector query processing for large datasets beyond gpu memory with reordered pipelining","venue":null,"work_id":"70ea8ec6-2159-43fe-adad-626f2573260a","year":2024},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.108471Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:8469417ced53f5078c5faa9b3f25a0fc63dac8ad71851fefb893b215fe87ded6","observation_id":"26263be1-bdbc-4264-80e9-a627e23a45e0","resolution":{"observed_at":"2026-08-15T20:52:34.258224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.241926Z","title":"Hidpu: A dpu-oriented hybrid indexing scheme for disaggregated storage systems","venue":null,"work_id":"e271668e-22e2-4d91-bed6-713fc6620735","year":2025},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.112386Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:7eda9b38c8eb4994fed98bf25bf8a3299eb691a19c48f40f7917d820955dc69d","observation_id":"61c6c057-8e83-4576-b581-43eda80e7e70","resolution":{"observed_at":"2026-08-15T20:52:34.245756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:52:34.227616Z","title":"Race: one- sided rdma-conscious extendible hashing.ACM Transactions on Storage (TOS), 18(2):1–29, 2022","venue":null,"work_id":"357dcee1-7362-4038-93fc-374f65aa4934","year":2022},"citing_paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T20:52:34.115690Z"},"links":{"citing_paper":"/paper/2505.11783"},"observation_digest":"sha256:4f9b7d47005e277e248310aab5176c41aae7dc206ead197dfbbfd683dc1d2e50","observation_id":"b1348e1b-ff8c-4d9f-a0c0-2c0e2dbf1c22","resolution":{"observed_at":"2026-08-15T20:52:34.233604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.11783","last_updated":"2025-05-17T01:31:21Z","latest_version":1,"primary_category":"cs.DB","snapshot_observed_at":"2026-08-18T08:35:48.052627Z","submitted_at":"2025-05-17T01:31:21Z","title":"Efficient Vector Search on Disaggregated Memory with d-HNSW"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":39},"total_outbound_references":47},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.11783."}