{"as_of":"2026-08-14T06:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c49bf3bbcd0e1b5f04c048aced9c8300dd0daff06049ec25866b65a815f29b97","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:35:18.055005Z","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":"2026-05-20T22:23:48.201525Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.16548","snapshot_observed_at":"2026-08-10T21:35:18.055005Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.04529","last_updated":"2025-01-08T14:21:03Z","snapshot_observed_at":"2026-08-12T11:26:04.395118Z","submitted_at":"2025-01-08T14:21:03Z","title":"A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T21:35:18.055005Z"},"links":{"cited_paper":"/paper/2006.16548","citing_paper":"/paper/2501.04529"},"observation_digest":"sha256:4e4b3d102adeb57697df8852621558f78da1493f24d309ac229356f6bb41e7a1","observation_id":"62b0cfff-58dd-4d3b-bbc3-89007683b3bb","resolution":{"observed_at":"2026-08-10T21:35:18.055005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.16548","snapshot_observed_at":"2026-08-10T17:44:27.405861Z","title":"Sinkhorn em: an expectation-maximization algorithm based on entropic optimal transport","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.12005","last_updated":"2025-01-23T09:47:30Z","snapshot_observed_at":"2026-08-13T16:05:58.031997Z","submitted_at":"2025-01-21T09:55:21Z","title":"A note on the relations between mixture models, maximum-likelihood and entropic optimal transport","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T17:44:27.405861Z"},"links":{"cited_paper":"/paper/2006.16548","citing_paper":"/paper/2501.12005"},"observation_digest":"sha256:fb295ef1dcc0c3e92e898299c4103f10a8ee90130b14788565be144bb97941c9","observation_id":"2a596450-7fb3-4cfc-93c5-779d1169f185","resolution":{"observed_at":"2026-08-10T17:44:27.405861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport","version":1},"cited_work":{"arxiv_id":"2006.16548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16548","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.16548 , year=","venue":null,"work_id":"6c4478a7-7376-4e2b-973b-df6578394e02","year":2006},"citing_paper":{"arxiv_id":"2605.03240","last_updated":"2026-05-05T00:17:27Z","snapshot_observed_at":"2026-08-11T09:03:51.231648Z","submitted_at":"2026-05-05T00:17:27Z","title":"On Model-Based Clustering With Entropic Optimal Transport","version":1},"reference_index":152,"source":"arxiv_source","source_observed_at":"2026-05-07T15:09:29.750492Z"},"links":{"cited_paper":"/paper/2006.16548","citing_paper":"/paper/2605.03240"},"observation_digest":"sha256:b672f71b45a94a93aa4fb79628975d1607f15ed0bba386f10ad72d96cedbab49","observation_id":"aeb0a1ba-6e17-4cb0-a293-75d26cd4b98b","resolution":{"observed_at":"2026-05-12T00:31:18.009871Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport","version":1},"cited_work":{"arxiv_id":"2006.16548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16548","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.16548 , year=","venue":null,"work_id":"6c4478a7-7376-4e2b-973b-df6578394e02","year":2006},"citing_paper":{"arxiv_id":"2605.08550","last_updated":"2026-05-16T02:31:48Z","snapshot_observed_at":"2026-08-12T12:26:02.413866Z","submitted_at":"2026-05-08T23:21:17Z","title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","version":1},"reference_index":179,"source":"arxiv_source","source_observed_at":"2026-05-12T01:20:12.816671Z"},"links":{"cited_paper":"/paper/2006.16548","citing_paper":"/paper/2605.08550"},"observation_digest":"sha256:f5ff482b15edcadf05ede221be3b4aa5e7bcf2750ced6cb4ac7f8651bcfd13d4","observation_id":"75b94124-3636-4305-8d82-72c2acd1c620","resolution":{"observed_at":"2026-05-12T08:06:27.174767Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport","version":1},"cited_work":{"arxiv_id":"2006.16548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16548","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.16548 , year=","venue":null,"work_id":"6c4478a7-7376-4e2b-973b-df6578394e02","year":2006},"citing_paper":{"arxiv_id":"2605.08550","last_updated":"2026-05-16T02:31:48Z","snapshot_observed_at":"2026-08-12T12:26:02.413866Z","submitted_at":"2026-05-08T23:21:17Z","title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","version":2},"reference_index":179,"source":"arxiv_source","source_observed_at":"2026-05-14T20:49:26.890463Z"},"links":{"cited_paper":"/paper/2006.16548","citing_paper":"/paper/2605.08550"},"observation_digest":"sha256:bb364ba8272aa2ec908e3847dfcf05e61ad7cc96b125ae6f3ec74cf2b81f2d74","observation_id":"e15b0a7f-9559-487a-8ec8-9cb9ca1e3fca","resolution":{"observed_at":"2026-05-14T20:52:59.284914Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport","version":1},"cited_work":{"arxiv_id":"2006.16548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16548","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2006.16548 , year=","venue":null,"work_id":"6c4478a7-7376-4e2b-973b-df6578394e02","year":2006},"citing_paper":{"arxiv_id":"2605.08550","last_updated":"2026-05-16T02:31:48Z","snapshot_observed_at":"2026-08-12T12:26:02.413866Z","submitted_at":"2026-05-08T23:21:17Z","title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","version":3},"reference_index":179,"source":"arxiv_source","source_observed_at":"2026-05-20T22:20:03.150386Z"},"links":{"cited_paper":"/paper/2006.16548","citing_paper":"/paper/2605.08550"},"observation_digest":"sha256:3ae09fe027e6484083474b076b250f7b3f982b7ebba77cca70d275c6cc0e1005","observation_id":"95820e38-3934-4ef4-b521-d78d733a7df5","resolution":{"observed_at":"2026-05-20T22:23:48.204324Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2006.16548/citation-record","integrity":"/paper/2006.16548/integrity","json":"/paper/2006.16548/citation-record.json","paper":"/paper/2006.16548"},"outbound":[],"paper":{"arxiv_id":"2006.16548","last_updated":"2020-06-30T06:03:37Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-08T21:10:40.485902Z","submitted_at":"2020-06-30T06:03:37Z","title":"Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2006.16548."}