{"as_of":"2026-08-21T10:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1e021723c8bbf4e08fc0953e925c0476fd6d0072b0b94d4ce4f6e66eae072b8b","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-05-10T01:14:18.241481Z","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":"2026-05-11T13:41:05.383914Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.09357","last_updated":"2025-03-12T13:00:29Z","snapshot_observed_at":"2026-08-19T13:26:42.332884Z","submitted_at":"2025-03-12T13:00:29Z","title":"Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach","version":1},"cited_work":{"arxiv_id":"2503.09357","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.09357","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2ac5214d-b23e-49e3-bbc8-229857785f37","year":2025},"citing_paper":{"arxiv_id":"2604.19654","last_updated":"2026-04-21T16:43:59Z","snapshot_observed_at":"2026-08-16T05:34:58.788654Z","submitted_at":"2026-04-21T16:43:59Z","title":"FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T01:14:18.241481Z"},"links":{"cited_paper":"/paper/2503.09357","citing_paper":"/paper/2604.19654"},"observation_digest":"sha256:dfa2d807ace493949a8248be3d94e6d75605a83b5abcba6f7c92a4fce31d5ae3","observation_id":"6ff9b1e0-74cf-4309-aae6-63041f71317c","resolution":{"observed_at":"2026-05-11T13:41:05.394898Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2503.09357/citation-record","integrity":"/paper/2503.09357/integrity","json":"/paper/2503.09357/citation-record.json","paper":"/paper/2503.09357"},"outbound":[],"paper":{"arxiv_id":"2503.09357","last_updated":"2025-03-12T13:00:29Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T13:26:42.332884Z","submitted_at":"2025-03-12T13:00:29Z","title":"Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach"},"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":"2026-08-21T06:32:19.484+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 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2503.09357."}