{"as_of":"2026-08-18T20:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:73cfac8097abdf8565489e92b8797f4a6d402336820a75a371a1f36c72e0699a","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:33:18.127596Z","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":"2026-07-02T16:57:09.414556Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.04610","last_updated":"2020-11-09T12:53:41Z","snapshot_observed_at":"2026-07-06T08:36:17.259270Z","submitted_at":"2019-10-24T00:13:54Z","title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.04610","snapshot_observed_at":"2026-08-16T04:33:18.127596Z","title":"Xpipe: Efficient pipeline model parallelism for multi- gpu dnn training","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.01099","last_updated":"2025-05-02T08:23:29Z","snapshot_observed_at":"2026-08-17T15:14:32.987521Z","submitted_at":"2025-05-02T08:23:29Z","title":"Nesterov Method for Asynchronous Pipeline Parallel Optimization","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:18.127596Z"},"links":{"cited_paper":"/paper/1911.04610","citing_paper":"/paper/2505.01099"},"observation_digest":"sha256:40703c79628f5263f8883e5c63d35064a9d3663360a3dbd322b97c9fb65c3cc7","observation_id":"6263b47d-7a6d-4a0d-9dc7-801cdcd5a309","resolution":{"observed_at":"2026-08-16T04:33:18.127596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04610","last_updated":"2020-11-09T12:53:41Z","snapshot_observed_at":"2026-07-06T08:36:17.259270Z","submitted_at":"2019-10-24T00:13:54Z","title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training","version":3},"cited_work":{"arxiv_id":"1911.04610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.04610","snapshot_observed_at":"2026-07-02T16:57:09.414556Z","title":"Xpipe: Efficient pipeline model parallelism for multi-gpu dnn training.arXiv preprint arXiv:1911.04610, 2019","venue":null,"work_id":"385af040-6ed0-411f-9eb7-99ff593d8c4a","year":1911},"citing_paper":{"arxiv_id":"2604.27085","last_updated":"2026-04-29T18:26:13Z","snapshot_observed_at":"2026-08-11T17:43:29.951165Z","submitted_at":"2026-04-29T18:26:13Z","title":"Efficient Training on Multiple Consumer GPUs with RoundPipe","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-07T10:37:22.251566Z"},"links":{"cited_paper":"/paper/1911.04610","citing_paper":"/paper/2604.27085"},"observation_digest":"sha256:f4cabed1135815eebd2d2426e553d478f7019575efaa30ff8dcb9b6525e5a9ef","observation_id":"42139d6c-cbc9-4868-9f4d-cbe870189d6e","resolution":{"observed_at":"2026-05-12T09:31:26.827187Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04610","last_updated":"2020-11-09T12:53:41Z","snapshot_observed_at":"2026-07-06T08:36:17.259270Z","submitted_at":"2019-10-24T00:13:54Z","title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training","version":3},"cited_work":{"arxiv_id":"1911.04610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.04610","snapshot_observed_at":"2026-07-02T16:57:09.414556Z","title":"Xpipe: Efficient pipeline model parallelism for multi-gpu dnn training.arXiv preprint arXiv:1911.04610, 2019","venue":null,"work_id":"385af040-6ed0-411f-9eb7-99ff593d8c4a","year":1911},"citing_paper":{"arxiv_id":"2606.07881","last_updated":"2026-06-05T22:33:57Z","snapshot_observed_at":"2026-08-03T04:13:36.304742Z","submitted_at":"2026-06-05T22:33:57Z","title":"Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T22:19:15.307830Z"},"links":{"cited_paper":"/paper/1911.04610","citing_paper":"/paper/2606.07881"},"observation_digest":"sha256:55e1929319eab43429ccf3b5e9424f17a4c668df82175317036d29384b439715","observation_id":"51e65c00-dfa2-4b1a-aa8c-93077f346761","resolution":{"observed_at":"2026-07-02T16:57:09.416074Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04610","last_updated":"2020-11-09T12:53:41Z","snapshot_observed_at":"2026-07-06T08:36:17.259270Z","submitted_at":"2019-10-24T00:13:54Z","title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training","version":3},"cited_work":{"arxiv_id":"1911.04610","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.04610","snapshot_observed_at":"2026-07-02T16:57:09.414556Z","title":"Xpipe: Efficient pipeline model parallelism for multi-gpu dnn training.arXiv preprint arXiv:1911.04610, 2019","venue":null,"work_id":"385af040-6ed0-411f-9eb7-99ff593d8c4a","year":1911},"citing_paper":{"arxiv_id":"2606.30634","last_updated":"2026-06-29T17:57:50Z","snapshot_observed_at":"2026-08-08T07:12:14.083887Z","submitted_at":"2026-06-29T17:57:50Z","title":"One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T06:41:55.732230Z"},"links":{"cited_paper":"/paper/1911.04610","citing_paper":"/paper/2606.30634"},"observation_digest":"sha256:a05ac4cd7f6f33fc47cc3653535cdb3aea247eb915e747b42d695967317f0a40","observation_id":"c5f39593-7af5-4f2b-91e1-7906d886eeaf","resolution":{"observed_at":"2026-06-30T06:44:18.859613Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1911.04610/citation-record","integrity":"/paper/1911.04610/integrity","json":"/paper/1911.04610/citation-record.json","paper":"/paper/1911.04610"},"outbound":[],"paper":{"arxiv_id":"1911.04610","last_updated":"2020-11-09T12:53:41Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:36:17.259270Z","submitted_at":"2019-10-24T00:13:54Z","title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1911.04610."}