{"as_of":"2026-08-16T17:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fe72dbfb684572f9d20fa00043c965f104d868162bdc0f9dc443992d71c101eb","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:58:32.558857Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2506.18604/citation-record","integrity":"/paper/2506.18604/integrity","json":"/paper/2506.18604/citation-record.json","paper":"/paper/2506.18604"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.15571","last_updated":"2023-03-09T15:18:40Z","snapshot_observed_at":"2026-08-14T22:23:53.150627Z","submitted_at":"2022-09-30T16:30:31Z","title":"Building Normalizing Flows with Stochastic Interpolants","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15571","snapshot_observed_at":"2026-08-15T18:58:32.377515Z","title":"Building normalizing flows with stochastic interpolants.arXiv preprint arXiv:2209.15571,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.377515Z"},"links":{"cited_paper":"/paper/2209.15571","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:834a3eb424dc7d5a630861a8af677d173eb5a6cca53293349a2dea32e8c87df1","observation_id":"e643c22d-30c3-48a2-aec4-8bdf9ec24f29","resolution":{"observed_at":"2026-08-15T18:58:32.377515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.02149","last_updated":"2023-07-18T10:09:13Z","snapshot_observed_at":"2026-08-16T16:50:07.353846Z","submitted_at":"2022-06-27T14:01:06Z","title":"Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths","version":2},"cited_work":{"arxiv_id":"2207.02149","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.02149","snapshot_observed_at":"2026-08-15T18:58:32.807516Z","title":"Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths","venue":"q-bio.BM","work_id":"c2207e9a-b1a5-46b3-8ee0-1efe63969f58","year":2022},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.411809Z"},"links":{"cited_paper":"/paper/2207.02149","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:db8919001a190ffeb04ace45baf39fffae37dcda6cfaf36678092e47110bb2e5","observation_id":"5a02e704-50cc-4c02-a075-718a4eb04e67","resolution":{"observed_at":"2026-08-15T18:58:32.858374Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:58:32.475091Z","title":"doi: 10.1109/tpami.2020","venue":null,"work_id":null,"year":1939},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.475091Z"},"links":{"citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:5790706559492983d6ee0b49015f72f29105b01ae738419afcd0be1c49432af3","observation_id":"b1798d1b-7f46-43d6-ad98-b9ea523d4c5b","resolution":{"observed_at":"2026-08-15T18:58:32.475091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15970","last_updated":"2023-10-28T17:38:59Z","snapshot_observed_at":"2026-08-16T14:57:04.793455Z","submitted_at":"2023-09-27T19:42:01Z","title":"Accelerating Motion Planning via Optimal Transport","version":2},"cited_work":{"arxiv_id":"2309.15970","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.15970","snapshot_observed_at":"2026-08-15T18:58:32.709388Z","title":"Accelerating Motion Planning via Optimal Transport","venue":"cs.RO","work_id":"62668ee7-1f87-4146-9403-3faf48c5cd89","year":2023},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.479335Z"},"links":{"cited_paper":"/paper/2309.15970","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:c71c708c68c37ec67bd29af75652c6fd905db602e097675b6dd22bb119e6bb7a","observation_id":"6010ff7a-85ef-4f12-a9f3-71a4e1940062","resolution":{"observed_at":"2026-08-15T18:58:32.714176Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-16T02:30:42.660030Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-15T18:58:32.483415Z","title":"Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.483415Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:d3500adead58a3cd10f6709b0b4138acbf63bfed08bfbd83abfd614604d4c024","observation_id":"483606c8-cb41-44a7-b1bf-768cf344ac8f","resolution":{"observed_at":"2026-08-15T18:58:32.483415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02233","last_updated":"2024-04-18T05:25:25Z","snapshot_observed_at":"2026-08-16T14:55:24.430442Z","submitted_at":"2023-10-03T17:42:11Z","title":"Generalized Schr\\\"odinger Bridge Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02233","snapshot_observed_at":"2026-08-15T18:58:32.487725Z","title":"Nanye Ma, Mark Goldstein, Michael S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.487725Z"},"links":{"cited_paper":"/paper/2310.02233","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:caba0527a56e4fafb019f3fd0e5baef0ddabc0aaec144bc7d7beb072388cf19d","observation_id":"250db038-0be7-4362-abd1-d2c96851af66","resolution":{"observed_at":"2026-08-15T18:58:32.487725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08740","last_updated":"2024-09-23T15:59:41Z","snapshot_observed_at":"2026-08-16T14:27:01.619497Z","submitted_at":"2024-01-16T18:55:25Z","title":"SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08740","snapshot_observed_at":"2026-08-15T18:58:32.491536Z","title":"Kevin R Moon, David Van Dijk, Zheng Wang, Scott Gi- gante, Daniel B Burkhardt, William S Chen, Kristina Yim, Antonia van den Elzen, Matthew J Hirn, Ronald R Coifman, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.491536Z"},"links":{"cited_paper":"/paper/2401.08740","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:7f2d2f93ad59635a393c60501ba3ee8c3986a79cb313ed06617c4aca8023198a","observation_id":"939d23c3-f6b9-46ff-b81c-5f4ea4e686de","resolution":{"observed_at":"2026-08-15T18:58:32.491536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-15T18:58:32.499717Z","title":"Matthew Tancik, Pratul P","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.499717Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:085717c7f248ef0380e7181747e94ec931b1c1beef218e50a0cd10e19ab4ea85","observation_id":"839586bc-60c7-47d2-867a-71ad341360d4","resolution":{"observed_at":"2026-08-15T18:58:32.499717Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:58:33.005399Z","title":"C PROOF OF PROPOSITION 1 Proof.We check thatρ t andut satisfy eq","venue":null,"work_id":"22b2c34a-0c3a-4eb5-9e1f-84249ad37760","year":2015},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.522049Z"},"links":{"citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:2121eea85fabae224f29a6e2724126121ce08fb213f4872692a2464d08d4894c","observation_id":"f89994a3-3c06-44bf-98a8-7e86432b71e8","resolution":{"observed_at":"2026-08-15T18:58:33.009246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:58:32.993578Z","title":"Also, the MLP parameterization along with the mixture combinations in the factorzied model turned out to be expressive enough for the experiments we have explored","venue":null,"work_id":"643b5054-a45d-4de2-9985-be0c7d6d9da4","year":2019},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":256,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.558857Z"},"links":{"citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:6ca8f2f63aeee3f7f69a9017a8bb4032587b44c1c03bc992f38440d28bc2a9b1","observation_id":"ef87a6c1-00fa-4bde-ac2f-9c11eb11cab2","resolution":{"observed_at":"2026-08-15T18:58:32.997616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:58:33.073359Z","title":"Path integrals and symmetry breaking for optimal control theory.Journal of statistical mechanics: theory and experiment, 2005(11):P11011,","venue":null,"work_id":"fcbce14a-ebed-440a-b637-9d831fbbbac0","year":2005},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":1989,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.416195Z"},"links":{"citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:fced2d7a11684e7663215132846b34bb7cd337022d93033a6e2e69ecd4a2ce6a","observation_id":"24576981-62f2-4be9-9664-506b02be25eb","resolution":{"observed_at":"2026-08-15T18:58:33.175111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s0378-4266(03)00138-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:58:32.623258Z","title":"doi: https://doi.org/10.1016/S0378-4266(03)00138-9","venue":null,"work_id":"8151ce76-a74a-42ea-be2c-456eb5e50c6f","year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2004,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.391730Z"},"links":{"citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:b4af2ed0e9ad5582f18879e2b4f6839b7a8547724fbfd4823676a4abe7e6601c","observation_id":"04c1097f-73d7-4b19-9926-f60c6674283e","resolution":{"observed_at":"2026-08-15T18:58:32.628468Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01367","last_updated":"2018-10-22T17:56:45Z","snapshot_observed_at":"2026-08-14T18:20:27.887044Z","submitted_at":"2018-10-02T16:56:37Z","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.01367","snapshot_observed_at":"2026-08-15T18:58:32.396266Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.396266Z"},"links":{"cited_paper":"/paper/1810.01367","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:0f9d727245221b1e58f69def7a27d4a5fbb4eb7985267ae2d0a3f86a5cdca354","observation_id":"d91b816b-59e1-492a-9d9c-e25897f81300","resolution":{"observed_at":"2026-08-15T18:58:32.396266Z","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":"2026-08-15T18:58:32.400843Z","title":"doi: 10.3390/e19110626","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.400843Z"},"links":{"citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:4f9e75c19227cf126c15e9d4787433425c053ea09a15a292ca82e3cb7c1084e4","observation_id":"7e5efae9-db0d-475e-98e6-1215793dca1b","resolution":{"observed_at":"2026-08-15T18:58:32.400843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.05517","last_updated":"2017-01-19T17:29:06Z","snapshot_observed_at":"2026-08-15T06:38:10.590597Z","submitted_at":"2017-01-19T17:29:06Z","title":"PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.05517","snapshot_observed_at":"2026-08-15T18:58:32.495581Z","title":"Pixelcnn++: Improving the pixelcnn with dis- cretized logistic mixture likelihood and other modifica- tions.arXiv preprint arXiv:1701.05517,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.495581Z"},"links":{"cited_paper":"/paper/1701.05517","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:edf9efaf90799c713fad6b49508165d12dcaa8c32774b2da89b73684fcb141fd","observation_id":"352dc1c9-237f-4277-9302-f159acea9fdf","resolution":{"observed_at":"2026-08-15T18:58:32.495581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-08-10T05:21:27.485481Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-08-15T18:58:32.405131Z","title":"Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, and Max Welling","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.405131Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:700653a549f71d65a408555bb4a42246cbbe57965df7f51048413bb4137a1cac","observation_id":"80e186ff-02fc-4bd8-904c-06e5687c90e9","resolution":{"observed_at":"2026-08-15T18:58:32.405131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-15T18:58:32.441078Z","title":"Adam: A method for stochastic opti- mization.arXiv preprint arXiv:1412.6980,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.441078Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:c0003fb362181760c4471c4114765699bcdc16c012b2bbd3b9ba3d198dd704fa","observation_id":"96ce00ad-9f59-4d0a-8ecc-43ef958d8ce1","resolution":{"observed_at":"2026-08-15T18:58:32.441078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-08-14T13:23:30.433320Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-15T18:58:32.503942Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws (Supplementary Material) Mengjian Hua1,2 Eric Vanden-Eijnden2 Ricky T.Q","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.503942Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:649881169547d1822c6077b0efca3cdd13d268a0c74c3f7f7e71db7a211baff7","observation_id":"0852f9b6-5cc4-4e37-a06e-4e6c457607df","resolution":{"observed_at":"2026-08-15T18:58:32.503942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08797","last_updated":"2025-10-09T00:43:44Z","snapshot_observed_at":"2026-07-06T15:03:52.079498Z","submitted_at":"2023-03-15T17:43:42Z","title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08797","snapshot_observed_at":"2026-08-15T18:58:32.382641Z","title":"org/abs/2303.08797","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.382641Z"},"links":{"cited_paper":"/paper/2303.08797","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:af5294a4be4e036a30e50369ee0095d404ceddfe9a81f6dd8346ed6750356c8b","observation_id":"91c164ad-03ae-4989-8f80-9771947dfa0a","resolution":{"observed_at":"2026-08-15T18:58:32.382641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03660","last_updated":"2024-02-26T17:52:00Z","snapshot_observed_at":"2026-08-16T15:57:01.124521Z","submitted_at":"2023-02-07T18:21:24Z","title":"Flow Matching on General Geometries","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.03660","snapshot_observed_at":"2026-08-15T18:58:32.387152Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T18:58:32.387152Z"},"links":{"cited_paper":"/paper/2302.03660","citing_paper":"/paper/2506.18604"},"observation_digest":"sha256:baafa17f2028e0811790e863f6338fd746dcc176258af3eef809ba0f0fca7353","observation_id":"1a0e001e-1398-4661-a56f-910f27ffce71","resolution":{"observed_at":"2026-08-15T18:58:32.387152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.18604","last_updated":"2025-06-23T13:04:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T18:43:44.274427Z","submitted_at":"2025-06-23T13:04:23Z","title":"Simulation-Free Differential Dynamics through Neural Conservation Laws"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":3,"verified_fuzzy":3},"total_outbound_references":20},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.18604."}