{"as_of":"2026-08-17T07:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:925c7f78327cb8d68d59550c691fbc33422c19dcd82face28e18575222d15a40","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T21:08:24.159706Z","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":"2026-06-28T19:42:36.098411Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.01328","last_updated":"2025-06-30T06:37:05Z","snapshot_observed_at":"2026-08-16T12:53:40.593894Z","submitted_at":"2025-03-03T09:11:06Z","title":"PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization","version":2},"cited_work":{"arxiv_id":"2503.01328","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.01328","snapshot_observed_at":"2026-06-28T19:42:36.098411Z","title":"arXiv preprint arXiv:2503.01328 , year=","venue":null,"work_id":"c66776cc-e3d0-4eb1-99a7-29c9b56091c5","year":2025},"citing_paper":{"arxiv_id":"2605.02189","last_updated":"2026-05-04T03:37:40Z","snapshot_observed_at":"2026-08-12T14:41:49.916145Z","submitted_at":"2026-05-04T03:37:40Z","title":"PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-08T18:49:56.357400Z"},"links":{"cited_paper":"/paper/2503.01328","citing_paper":"/paper/2605.02189"},"observation_digest":"sha256:504dd27eb5fb0ebdba9a2b5b03f3406cba243ee038e747cfafb20d0a5d6c2f5a","observation_id":"0706ab8c-01e5-4875-ab1d-34f9f6b412ad","resolution":{"observed_at":"2026-05-09T06:10:41.549562Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01328","last_updated":"2025-06-30T06:37:05Z","snapshot_observed_at":"2026-08-16T12:53:40.593894Z","submitted_at":"2025-03-03T09:11:06Z","title":"PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization","version":2},"cited_work":{"arxiv_id":"2503.01328","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.01328","snapshot_observed_at":"2026-06-28T19:42:36.098411Z","title":"arXiv preprint arXiv:2503.01328 , year=","venue":null,"work_id":"c66776cc-e3d0-4eb1-99a7-29c9b56091c5","year":2025},"citing_paper":{"arxiv_id":"2606.00539","last_updated":"2026-05-30T05:11:13Z","snapshot_observed_at":"2026-08-15T23:47:05.326194Z","submitted_at":"2026-05-30T05:11:13Z","title":"GNMR: Runtime Stability Control for Low-Precision Large Language Model Training","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-28T18:53:47.187437Z"},"links":{"cited_paper":"/paper/2503.01328","citing_paper":"/paper/2606.00539"},"observation_digest":"sha256:96fba692be8581bea00655dd74a4a931a4039bcf310d2d69f7120e2e263d2df6","observation_id":"4b5197f1-63ba-40de-acb7-3b4ce230f0cf","resolution":{"observed_at":"2026-06-28T19:42:36.100245Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01328","last_updated":"2025-06-30T06:37:05Z","snapshot_observed_at":"2026-08-16T12:53:40.593894Z","submitted_at":"2025-03-03T09:11:06Z","title":"PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.01328","snapshot_observed_at":"2026-07-11T21:08:24.159706Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.04181","last_updated":"2026-07-05T08:52:41Z","snapshot_observed_at":"2026-08-09T05:20:52.108205Z","submitted_at":"2026-07-05T08:52:41Z","title":"CoCoScale: Leveraging Layer-wise Scaling to Unlock the Potential of Online LLM Serving","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-11T21:08:24.159706Z"},"links":{"cited_paper":"/paper/2503.01328","citing_paper":"/paper/2607.04181"},"observation_digest":"sha256:c7fdbfc9fd36169e10f96eb31e43477206c934e8220a27020ecaa23fb903cc91","observation_id":"67b9bd33-047f-47da-aa8d-adfb55794818","resolution":{"observed_at":"2026-07-11T21:08:24.159706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.01328/citation-record","integrity":"/paper/2503.01328/integrity","json":"/paper/2503.01328/citation-record.json","paper":"/paper/2503.01328"},"outbound":[],"paper":{"arxiv_id":"2503.01328","last_updated":"2025-06-30T06:37:05Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T12:53:40.593894Z","submitted_at":"2025-03-03T09:11:06Z","title":"PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.01328."}