{"as_of":"2026-08-21T06:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7dc611acead6d19da93ad3e2080c10bbfeb57508011346ecd85b62fc0a862ce8","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:07:35.089407Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T02:48:27.081996Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.18292","last_updated":"2025-03-24T02:28:04Z","snapshot_observed_at":"2026-08-19T00:24:51.262568Z","submitted_at":"2025-03-24T02:28:04Z","title":"Jenga: Effective Memory Management for Serving LLM with Heterogeneity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.18292","snapshot_observed_at":"2026-08-15T18:07:35.089407Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.19017","last_updated":"2025-07-25T07:11:49Z","snapshot_observed_at":"2026-08-19T06:36:45.292994Z","submitted_at":"2025-07-25T07:11:49Z","title":"MindSpeed RL: Distributed Dataflow for Scalable and Efficient RL Training on Ascend NPU Cluster","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T18:07:35.089407Z"},"links":{"cited_paper":"/paper/2503.18292","citing_paper":"/paper/2507.19017"},"observation_digest":"sha256:64d873c848831c3d083f62312ad16d0d6f93cac97f9374d5912a448faca8805b","observation_id":"e5d02f0d-7ba9-4478-a393-3e2549207f83","resolution":{"observed_at":"2026-08-15T18:07:35.089407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18292","last_updated":"2025-03-24T02:28:04Z","snapshot_observed_at":"2026-08-19T00:24:51.262568Z","submitted_at":"2025-03-24T02:28:04Z","title":"Jenga: Effective Memory Management for Serving LLM with Heterogeneity","version":1},"cited_work":{"arxiv_id":"2503.18292","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.18292","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2503.18292 (2025)","venue":null,"work_id":"eb9b8e3a-d582-4602-b928-f26613d9c2b2","year":2025},"citing_paper":{"arxiv_id":"2604.18529","last_updated":"2026-04-20T17:25:44Z","snapshot_observed_at":"2026-08-12T17:05:28.384765Z","submitted_at":"2026-04-20T17:25:44Z","title":"HybridGen: Efficient LLM Generative Inference via CPU-GPU Hybrid Computing","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T02:48:18.330339Z"},"links":{"cited_paper":"/paper/2503.18292","citing_paper":"/paper/2604.18529"},"observation_digest":"sha256:6c5cbe2eaf70d2f6016f0c7281f725dc8f7d117479ee0eb3d9144c8dc93467a8","observation_id":"f79e7d39-93d7-4662-8af7-3c8f32826329","resolution":{"observed_at":"2026-05-10T02:48:27.083217Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2503.18292/citation-record","integrity":"/paper/2503.18292/integrity","json":"/paper/2503.18292/citation-record.json","paper":"/paper/2503.18292"},"outbound":[],"paper":{"arxiv_id":"2503.18292","last_updated":"2025-03-24T02:28:04Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-19T00:24:51.262568Z","submitted_at":"2025-03-24T02:28:04Z","title":"Jenga: Effective Memory Management for Serving LLM with Heterogeneity"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.18292."}