{"as_of":"2026-08-14T00:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4b92fd53e49b29ec49faaf6d0811ce697680cd8f6708f752b41443a715149e81","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T04:41:45.363196Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2412.01181/citation-record","integrity":"/paper/2412.01181/integrity","json":"/paper/2412.01181/citation-record.json","paper":"/paper/2412.01181"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1260.9753","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:41:45.686234Z","title":null,"venue":null,"work_id":"c38ca574-9f93-4ecb-b51b-184356ddd061","year":null},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.328748Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:39325c99a34d9daf1f8c1670046a8f76d8e23aae63a15b2898396a161f0db421","observation_id":"852ecdf1-13ad-406c-89bb-9d0eb6e8aba4","resolution":{"observed_at":"2026-08-12T04:41:45.690946Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0927.46317","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:41:45.894982Z","title":"For comparison, we show the explicit exponential integrating factor Euler method method alongside a few implicit schemes","venue":null,"work_id":"a8b494f4-08b5-47f7-8081-d3ce61826391","year":null},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.318642Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:dad3c32a2e0c4253658a9afe7fa77ea540d9975dbe044066c9420071cd6d3222","observation_id":"7de5c171-9992-4eae-8974-c3ef60040a1e","resolution":{"observed_at":"2026-08-12T04:41:45.900208Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6743.0863","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:41:45.819508Z","title":"The stiffness can be seen by the fact that each of the variables approach steady-state at a different timescale","venue":null,"work_id":"1e4edf8c-150f-41f1-aa74-43267ca4a813","year":null},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.322390Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:4161859cdc68a5196e28d8e186f887690ae15910c5977ac951c3c942907aea32","observation_id":"01f87f1d-e8dd-49e8-a458-d94c9037caef","resolution":{"observed_at":"2026-08-12T04:41:45.824819Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1046.1492","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:41:45.604672Z","title":null,"venue":null,"work_id":"df46625d-e0d7-4d63-ad26-d228e92d4740","year":null},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.331952Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:ab16d977f8605fb6dc4426a48bcba1d011c81532330e615761bf2681a3efcc43","observation_id":"395412b4-d14b-48fc-ad13-d92abb98b02b","resolution":{"observed_at":"2026-08-12T04:41:45.609719Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"4996.1840","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:41:45.531250Z","title":null,"venue":null,"work_id":"503eae8d-1310-44b4-b557-0904fff48a07","year":2009},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.334734Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:72ee1539a6fdac850e6def5cf8d55df66f4e985cb6bcdbadcc3ba1020d360a3f","observation_id":"91f587e4-574b-4658-8a74-cbac12ac717e","resolution":{"observed_at":"2026-08-12T04:41:45.535688Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.02262","last_updated":"2023-06-02T14:52:33Z","snapshot_observed_at":"2026-08-13T12:32:22.163146Z","submitted_at":"2023-03-03T23:31:15Z","title":"Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!","version":3},"cited_work":{"arxiv_id":"2303.02262","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.02262","snapshot_observed_at":"2026-08-12T04:41:45.407707Z","title":"Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!","venue":"cs.LG","work_id":"e20bf493-cd9f-43f5-96dd-871d48f875f0","year":2023},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.354276Z"},"links":{"cited_paper":"/paper/2303.02262","citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:09ef4838f3be128a77bf07e339b85ab6743f416900013d68bcf02aa8d2c50184","observation_id":"e1ce4933-a3d7-4748-a86f-ead85ebb95da","resolution":{"observed_at":"2026-08-12T04:41:45.411010Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1000.00284","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:41:45.759504Z","title":null,"venue":null,"work_id":"5bbe3cdd-3296-4ce1-b66e-e94f0e51381c","year":null},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.325707Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:52fc0a6417f742d8885d63fb2b7a16400115412ea40283811469319a81e2d7b7","observation_id":"a8866a53-f6ff-473e-b3b3-94129994015f","resolution":{"observed_at":"2026-08-12T04:41:45.763903Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.07244","last_updated":"2022-02-06T15:35:49Z","snapshot_observed_at":"2026-08-13T20:46:55.304841Z","submitted_at":"2020-12-14T04:05:26Z","title":"Bayesian Neural Ordinary Differential Equations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.07244","snapshot_observed_at":"2026-08-12T04:41:45.337814Z","title":"Discovering governing equa- tions from data by sparse identification of nonlinear dynamical systems,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.337814Z"},"links":{"cited_paper":"/paper/2012.07244","citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:b49dd835aee596e43dbeaba9dc0b4a4f304b5c5a9abfdea87191bfe29af12bb1","observation_id":"91901113-4986-423b-918e-b1517a1317fa","resolution":{"observed_at":"2026-08-12T04:41:45.337814Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-12T04:41:45.926548Z","title":"Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems,","venue":null,"work_id":"95d039ec-01f0-48ed-9035-10151b4c3c82","year":2023},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":226,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.347827Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:4e561ef9c2e8ed366e4664911aaf38ec8f124efc3834c631efe6872b6dff6ded","observation_id":"7170f963-9d88-499a-be37-cafdfc280e2d","resolution":{"observed_at":"2026-08-12T04:41:45.929712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19374","last_updated":"2024-11-28T20:53:05Z","snapshot_observed_at":"2026-08-13T19:05:18.054780Z","submitted_at":"2024-11-28T20:53:05Z","title":"Performance Evaluation of Single-step Explicit Exponential Integration Methods on Stiff Ordinary Differential Equations","version":1},"cited_work":{"arxiv_id":"2411.19374","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.19374","snapshot_observed_at":"2026-08-12T04:41:45.392036Z","title":"Performance Evaluation of Single-step Explicit Exponential Integration Methods on Stiff Ordinary Differential Equations","venue":"math.NA","work_id":"5d0d6f73-4ca0-4452-8102-5ba0be05089f","year":2024},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":1960,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.360150Z"},"links":{"cited_paper":"/paper/2411.19374","citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:9bdd94395b82a9340416b146a60f018c1b73d31bb66fd52904dfc1c6ae12b621","observation_id":"96b77f1f-920a-4323-997c-c673782e71d3","resolution":{"observed_at":"2026-08-12T04:41:45.397248Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T04:41:45.907064Z","title":"High performance computing of the matrix exponential,","venue":null,"work_id":"f7d52ef7-4d6e-42dc-b1ed-47b895fb8a1e","year":2016},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":2003,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.363196Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:ef103ae5784f4050c7ffba006db9d2e43efd3788f69026aa04653aab81f99ead","observation_id":"1a9e2c66-4031-4f8e-9ead-6b79a5f3caf6","resolution":{"observed_at":"2026-08-12T04:41:45.910384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.02435","last_updated":"2022-02-04T23:32:29Z","snapshot_observed_at":"2026-08-13T16:47:02.414866Z","submitted_at":"2022-02-04T23:32:29Z","title":"On Neural Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.02435","snapshot_observed_at":"2026-08-12T04:41:45.341379Z","title":"Neural controlled differen- tial equations for irregular time series,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.341379Z"},"links":{"cited_paper":"/paper/2202.02435","citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:6887efeba339777abc30f5ebb270e4c725d57bf951b86625fe2ca0a0b0287229","observation_id":"41b0a012-8d15-477a-b509-d23935c234be","resolution":{"observed_at":"2026-08-12T04:41:45.341379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.03902","last_updated":"2021-10-27T17:16:56Z","snapshot_observed_at":"2026-08-13T23:57:19.539769Z","submitted_at":"2020-11-08T04:33:54Z","title":"Learning Neural Event Functions for Ordinary Differential Equations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.03902","snapshot_observed_at":"2026-08-12T04:41:45.344467Z","title":"Neural jump stochastic differential equations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.344467Z"},"links":{"cited_paper":"/paper/2011.03902","citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:2945f4414f11dd362230445ebac69be74ab8b85f53a50fa38fc14c548ef6c991","observation_id":"16432055-4fc1-4821-af92-a6c706d42fcc","resolution":{"observed_at":"2026-08-12T04:41:45.344467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.08621","last_updated":"2022-04-19T02:55:10Z","snapshot_observed_at":"2026-08-13T15:59:55.054750Z","submitted_at":"2022-04-19T02:55:10Z","title":"Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.08621","snapshot_observed_at":"2026-08-12T04:41:45.350756Z","title":"Proximal implicit ode solvers for accelerating learning neural odes,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.350756Z"},"links":{"cited_paper":"/paper/2204.08621","citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:86f439a640de3b80f38d96c7a6986373b7d12da0b8a1b8293b9c530012a194ea","observation_id":"ad97c4e4-402d-4ba0-9738-1dbfa62c9c85","resolution":{"observed_at":"2026-08-12T04:41:45.350756Z","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":"2026-08-12T04:41:45.916355Z","title":"Generalized integrating factor methods for stiff pdes,","venue":null,"work_id":"2befc8a2-7674-45b6-a1da-5ef4933dc6df","year":2005},"citing_paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T04:41:45.357470Z"},"links":{"citing_paper":"/paper/2412.01181"},"observation_digest":"sha256:a13dece71415339426736ee1b7a8548825814d72abfceb747dcbf8c2c9a78c69","observation_id":"207e4dd6-0eae-4162-8468-fa614569f04a","resolution":{"observed_at":"2026-08-12T04:41:45.920173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.01181","last_updated":"2024-12-02T06:40:08Z","latest_version":1,"primary_category":"math.NA","snapshot_observed_at":"2026-08-13T19:05:19.405413Z","submitted_at":"2024-12-02T06:40:08Z","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":5,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":3,"verified_fuzzy":3},"total_outbound_references":15},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2412.01181."}