{"as_of":"2026-08-19T07:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a4e5fecd02001dc16338459ce7a35549298e11807a8ec91c96574dc3ceef725c","coverage":[{"denominator":11,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T18:32:47.329159Z","state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.04500/citation-record","integrity":"/paper/2607.04500/integrity","json":"/paper/2607.04500/citation-record.json","paper":"/paper/2607.04500"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2606.28383","last_updated":"2026-06-21T05:26:58Z","snapshot_observed_at":"2026-08-13T14:55:54.469079Z","submitted_at":"2026-06-21T05:26:58Z","title":"Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.28383","snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"arXiv preprint arXiv:2606.28383 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"cited_paper":"/paper/2606.28383","citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:af6a2d275e7a020f67a291f34176eeef8a78e125747711e6b69c30096a7347ff","observation_id":"1283b372-7ff4-4fa9-b541-913633981bac","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.23444","last_updated":"2026-06-23T04:50:32Z","snapshot_observed_at":"2026-08-02T06:26:16.707206Z","submitted_at":"2026-06-22T15:00:59Z","title":"SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.23444","snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"arXiv preprint arXiv:2606.23444 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"cited_paper":"/paper/2606.23444","citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:f10fdbcd90075529141cc7924b5bf11765f00da8735d4af4fc90c9bef0573a86","observation_id":"d3952ceb-9370-4413-bebb-097244fcce4b","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"OpenReview , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:7182911357d1e85fd00de6a89676730c06a07d893208c282eec8f30d4c4c2cbc","observation_id":"000ec7eb-11f6-47ca-9fac-bed41c096e0a","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"CVPR Workshop on Autonomous Driving , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:74418cdf60e2d95f544667715cd30b23c0bc113c00c2062835e9dd4701a4b98f","observation_id":"d46c3187-6cb2-4131-85d4-da071914336a","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"NeurIPS Datasets and Benchmarks Track , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:41815338c63accb4cc9529202521943b453992006dc13bd831e2546dd270cbeb","observation_id":"9fb4f777-d33e-49cc-8b99-d2276ecf7d1e","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:bc9ed28a9650a589a5803506a0e2a0360494e4bbd26ca35f11aa76bd27e4b9e5","observation_id":"68b3b5fe-3005-457f-a216-c86c369c1c97","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:4f9298560804dbc92bdc6c05187d89a76197c389ab73604df96cb5eb299f2e66","observation_id":"3a940b82-0a55-45dc-b267-c87cdde713ec","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"CVPR , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:66f7c7eb9ee86bf6d763f339ef4489dafa4a1893dbfaf3935e93d1c23cbbcc5f","observation_id":"351658cf-7089-4a0a-b950-b520e734b200","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"IJCAI , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:f8d770a07f9054f8bb5efe06e181a6980d30aacb259d8bcbd3d6d58da5f31950","observation_id":"8da90ccf-fc27-46b7-a869-f1f8a451b9b8","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"NeurIPS Workshop on Self-Supervised Learning , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:0b712869fb21f580ebdbb47c3dfb5f0e33fbd719e9a034365813d6d824ed84d1","observation_id":"8dab35ae-e7fd-4e2e-9dd8-38f64f3ff93a","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.08360","last_updated":"2022-02-22T18:15:21Z","snapshot_observed_at":"2026-08-16T17:20:43.611977Z","submitted_at":"2022-02-16T22:26:47Z","title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.08360","snapshot_observed_at":"2026-07-11T18:32:47.329159Z","title":"arXiv preprint arXiv:2202.08360 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-07-11T18:32:47.329159Z"},"links":{"cited_paper":"/paper/2202.08360","citing_paper":"/paper/2607.04500"},"observation_digest":"sha256:70d751bbf1971657a3887d4bbb1b0d65cfd01e136aeccdf69e71e6cd354e65a5","observation_id":"9c5a6414-787f-464f-91df-b2899f208387","resolution":{"observed_at":"2026-07-11T18:32:47.329159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.04500","last_updated":"2026-07-05T20:58:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T02:46:58.900793Z","submitted_at":"2026-07-05T20:58:16Z","title":"Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models"},"reference_resolution":{"displayed":11,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":11},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2607.04500."}