{"as_of":"2026-08-10T00:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b68cb1ede551eac1356aa65a429d033deae8cb783b2b3899610579f3c0c073ee","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T20:38:25.897770Z","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":"2026-07-04T06:39:36.998873Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-08-08T20:38:25.897770Z","title":"Dataset distillation via the wasserstein metric","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05027","last_updated":"2025-02-07T15:57:39Z","snapshot_observed_at":"2026-08-08T20:30:22.780105Z","submitted_at":"2025-02-07T15:57:39Z","title":"Trust-Aware Diversion for Data-Effective Distillation","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T20:38:25.897770Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2502.05027"},"observation_digest":"sha256:78ba100fdb837ddb0ff55dac027c48a5b14a4ab27ebc81ae3647de5001a5a204","observation_id":"aea5dbb4-7a0d-4f15-8c3c-d3cd0c739247","resolution":{"observed_at":"2026-08-08T20:38:25.897770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-08-08T18:29:50.273079Z","title":"Dataset distillation via the wasserstein metric,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05673","last_updated":"2025-07-03T14:56:22Z","snapshot_observed_at":"2026-08-09T10:27:08.020566Z","submitted_at":"2025-02-08T19:37:33Z","title":"The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T18:29:50.273079Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2502.05673"},"observation_digest":"sha256:e2694c9cdd8351b22351a4630d931a597e4290bb8ddf45c9fda00c0d70da69ee","observation_id":"ac96e9f9-6e94-431e-9aa8-262b8d3547fc","resolution":{"observed_at":"2026-08-08T18:29:50.273079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-08-06T05:32:45.374982Z","title":"Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.01749","last_updated":"2025-08-03T13:15:52Z","snapshot_observed_at":"2026-08-07T23:27:32.572707Z","submitted_at":"2025-08-03T13:15:52Z","title":"Improving Noise Efficiency in Privacy-preserving Dataset Distillation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T05:32:45.374982Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2508.01749"},"observation_digest":"sha256:6b93df2727eb076c6875fdb8d483e96ce4ac6fd604f5c276f8d1271a4e27c9cf","observation_id":"158634ce-4b73-4f9c-b6ce-ccdb31d334f0","resolution":{"observed_at":"2026-08-06T05:32:45.374982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-08-04T17:57:54.799494Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10367","last_updated":"2025-09-12T16:00:49Z","snapshot_observed_at":"2026-08-08T19:18:42.995107Z","submitted_at":"2025-09-12T16:00:49Z","title":"A Discrepancy-Based Perspective on Dataset Condensation","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-04T17:57:54.799494Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2509.10367"},"observation_digest":"sha256:8d181503129e3da3012a4eee09fe9c564eed683e783b35597c8efbd1b0e62347","observation_id":"65927d45-4d30-4227-9d16-e306d6f0c9d0","resolution":{"observed_at":"2026-08-04T17:57:54.799494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-08-04T11:29:21.212959Z","title":"Dataset distillation via the wasserstein metric","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.04602","last_updated":"2026-07-24T10:35:03Z","snapshot_observed_at":"2026-08-08T15:01:41.789569Z","submitted_at":"2025-10-06T09:07:12Z","title":"Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T11:29:21.212959Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2510.04602"},"observation_digest":"sha256:6f0fce8e3713c768fa5f184c08a981de4e28c069b14b9ee82a1fa9106244fad6","observation_id":"e5d8d50a-c524-4738-bdd1-1b6cc63e7380","resolution":{"observed_at":"2026-08-04T11:29:21.212959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":"2311.18531","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-07-04T06:39:36.998873Z","title":"Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531","venue":null,"work_id":"ef2cde41-a9a1-43da-acf7-914ddd981be9","year":2023},"citing_paper":{"arxiv_id":"2605.03877","last_updated":"2026-05-05T15:37:50Z","snapshot_observed_at":"2026-08-04T08:46:20.575366Z","submitted_at":"2026-05-05T15:37:50Z","title":"DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-07T17:42:00.634333Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2605.03877"},"observation_digest":"sha256:a25c93da0e99657a87e3e58e519c1771467136f6aa9cd159effa6af79ce2affc","observation_id":"020b78ee-ccc6-4dbe-abb4-768fcd78c0c3","resolution":{"observed_at":"2026-05-11T23:16:29.039399Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":"2311.18531","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-07-04T06:39:36.998873Z","title":"Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531","venue":null,"work_id":"ef2cde41-a9a1-43da-acf7-914ddd981be9","year":2023},"citing_paper":{"arxiv_id":"2606.20196","last_updated":"2026-06-30T06:11:47Z","snapshot_observed_at":"2026-08-03T23:25:05.101981Z","submitted_at":"2026-06-18T13:11:13Z","title":"Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T18:03:47.140620Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2606.20196"},"observation_digest":"sha256:2ea0fc721410e7bb5d4b1cc34b43a7863266f6cef9d95c1c617da6b6c435e66e","observation_id":"24c9b46c-9d4f-4a52-8e02-d57749d10064","resolution":{"observed_at":"2026-07-04T03:29:29.761931Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":"2311.18531","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-07-04T06:39:36.998873Z","title":"Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531","venue":null,"work_id":"ef2cde41-a9a1-43da-acf7-914ddd981be9","year":2023},"citing_paper":{"arxiv_id":"2606.20196","last_updated":"2026-06-30T06:11:47Z","snapshot_observed_at":"2026-08-03T23:25:05.101981Z","submitted_at":"2026-06-18T13:11:13Z","title":"Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-01T07:08:57.768207Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2606.20196"},"observation_digest":"sha256:664811475126e40c55bc601a7f6b6c7fef3902c0dca967a404a1e1ddb56a01a3","observation_id":"f49863b8-e8e4-4103-a15d-2f91b488b2ad","resolution":{"observed_at":"2026-07-01T08:45:35.518805Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":"2311.18531","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-07-04T06:39:36.998873Z","title":"Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531","venue":null,"work_id":"ef2cde41-a9a1-43da-acf7-914ddd981be9","year":2023},"citing_paper":{"arxiv_id":"2606.21705","last_updated":"2026-06-19T19:33:15Z","snapshot_observed_at":"2026-08-07T09:56:30.299577Z","submitted_at":"2026-06-19T19:33:15Z","title":"Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T14:22:33.482498Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2606.21705"},"observation_digest":"sha256:d1874f8068a5b6b74289da3ca32158e3c2dd55cf7f2009d57e0ce155af4d4ed9","observation_id":"fda7fcf6-8be9-4ebc-be35-da367a07419c","resolution":{"observed_at":"2026-07-04T06:39:37.000220Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric","version":3},"cited_work":{"arxiv_id":"2311.18531","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.18531","snapshot_observed_at":"2026-07-04T06:39:36.998873Z","title":"Dataset distillation via the wasserstein metric.arXiv preprint arXiv:2311.18531","venue":null,"work_id":"ef2cde41-a9a1-43da-acf7-914ddd981be9","year":2023},"citing_paper":{"arxiv_id":"2607.00916","last_updated":"2026-07-01T13:21:44Z","snapshot_observed_at":"2026-08-03T02:46:32.964561Z","submitted_at":"2026-07-01T13:21:44Z","title":"Condensing Large-Scale Datasets Directly with Minimal Information Loss","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-02T13:59:59.488216Z"},"links":{"cited_paper":"/paper/2311.18531","citing_paper":"/paper/2607.00916"},"observation_digest":"sha256:9a4bc3c88f038a3de91383e136734bd26aabc1639a903b04caeb9e093e61af30","observation_id":"551d76a2-f819-4190-84ad-605098088a1d","resolution":{"observed_at":"2026-07-02T14:07:02.395368Z","resolver_source":"arxiv_id","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/2311.18531/citation-record","integrity":"/paper/2311.18531/integrity","json":"/paper/2311.18531/citation-record.json","paper":"/paper/2311.18531"},"outbound":[],"paper":{"arxiv_id":"2311.18531","last_updated":"2025-07-02T04:25:43Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T00:36:09.177938Z","submitted_at":"2023-11-30T13:15:28Z","title":"Dataset Distillation via the Wasserstein Metric"},"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 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2311.18531."}