{"as_of":"2026-08-09T10:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3b7233c5caf63d5e79f8bd4cd27333959f274f67b324712693eabd108688299c","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T04:57:52.403708Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T04:57:52.912574Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.13853","last_updated":"2024-12-12T03:21:13Z","snapshot_observed_at":"2026-08-08T03:13:51.064859Z","submitted_at":"2024-07-18T18:47:52Z","title":"Forecasting GPU Performance for Deep Learning Training and Inference","version":3},"cited_work":{"arxiv_id":"2407.13853","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.13853","snapshot_observed_at":"2026-08-07T04:57:52.912574Z","title":"Forecasting GPU Performance for Deep Learning Training and Inference","venue":"cs.LG","work_id":"934932ed-22cc-4784-b570-023450adc70f","year":2024},"citing_paper":{"arxiv_id":"2506.09275","last_updated":"2025-06-10T22:25:29Z","snapshot_observed_at":"2026-08-09T08:55:31.235174Z","submitted_at":"2025-06-10T22:25:29Z","title":"A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T04:57:52.403708Z"},"links":{"cited_paper":"/paper/2407.13853","citing_paper":"/paper/2506.09275"},"observation_digest":"sha256:3cec5fbf1ddca5d3781476d9da0e29c7566f1c561c6f659219f4669f3a892ff8","observation_id":"ded2ce9d-69bc-4240-ac79-fdaa2cb71152","resolution":{"observed_at":"2026-08-07T04:57:52.957704Z","resolver_source":"local_arxiv","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/2407.13853/citation-record","integrity":"/paper/2407.13853/integrity","json":"/paper/2407.13853/citation-record.json","paper":"/paper/2407.13853"},"outbound":[],"paper":{"arxiv_id":"2407.13853","last_updated":"2024-12-12T03:21:13Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T03:13:51.064859Z","submitted_at":"2024-07-18T18:47:52Z","title":"Forecasting GPU Performance for Deep Learning Training and Inference"},"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":"2026-08-09T06:31:02.800959+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 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2407.13853."}