{"as_of":"2026-08-21T04:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6c8940a73c8825c92c6da2d7f892d691206276026af6395186c6509d5abd9f62","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:54:57.399887Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2504.17139/citation-record","integrity":"/paper/2504.17139/integrity","json":"/paper/2504.17139/citation-record.json","paper":"/paper/2504.17139"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:54:57.920880Z","title":"Rapidly exponentially stabilizing control lyapunov functions and hybrid zero dynamics","venue":null,"work_id":"627cbe9c-8506-47fa-a51a-a0d65e677e75","year":2014},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.276543Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:d77955fe438e25e6798118d43492957d7cb2a2e530331e9877eab22d6169c439","observation_id":"efc85902-f74c-43c1-b2bd-551a0fa0a2fe","resolution":{"observed_at":"2026-08-16T10:54:57.925093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.281866Z","title":"Control barrier function based quadratic programs for safety critical systems","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.281866Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:336d3f95862762a9d5b1a355b777aff91c1a8091ae0f315a6a9b4e0a161f6c6f","observation_id":"235dcdd1-f95a-433f-af4f-063d25f98b54","resolution":{"observed_at":"2026-08-16T10:54:57.281866Z","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":"2026-08-16T10:54:57.287319Z","title":"Control barrier functions: Theory and applications","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.287319Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:47376c9074f24050be6b1e79ab9b27381b1c02d13fd370833bec0eafb44cbe11","observation_id":"9eaaf056-0f6f-4af5-8268-325cd0577771","resolution":{"observed_at":"2026-08-16T10:54:57.287319Z","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":"2026-08-16T10:54:57.294608Z","title":"Optnet: Differentiable optimization as a layer in neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.294608Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:e625cb62daf352d3bb231f6f02e1ccb546968b8cd4891f6f1b191b7ea746a6af","observation_id":"a61f10fc-8c2f-45d9-8e16-581a9b6c77e8","resolution":{"observed_at":"2026-08-16T10:54:57.294608Z","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":"2026-08-16T10:54:57.885293Z","title":"Neural odes for data-driven automatic self-design of finite-time output feedback control for unknown nonlinear dynamics","venue":null,"work_id":"dbd61d9c-02d6-4719-afc6-625151508f92","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.300193Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:138c4c2af987dbe1a79986077452466294e781638d71f4c06c4bfeb0f053b4d3","observation_id":"6f3dede5-062c-43ac-987c-2770541c6cf1","resolution":{"observed_at":"2026-08-16T10:54:57.889716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.872160Z","title":"Ai pontryagin or how artificial neural networks learn to control dynamical systems","venue":null,"work_id":"ba1d6c78-3411-46e6-be22-ce012c7b79f0","year":2022},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.304477Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:c6add05705c312bc6115c0f45b7caf511fd93e5acb9cca760bb968790e8fcf98","observation_id":"ba460fb9-450e-442b-86cf-d8452d64fa7f","resolution":{"observed_at":"2026-08-16T10:54:57.876625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.308997Z","title":"Neural ordinary differential equations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.308997Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:3a4e08f125a51152188ac5d1d76cf859aab7dff9ca2b40add664f1e5ee8cdd89","observation_id":"34105dd6-14c2-4bae-80ca-0311257d2d4a","resolution":{"observed_at":"2026-08-16T10:54:57.308997Z","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":"2026-08-16T10:54:57.313180Z","title":"Safe nonlinear control using robust neural lyapunov-barrier functions","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.313180Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:6796cede2822006c743ed276ad72b686c7c54f5a34281cceaaccc96e8ce2890e","observation_id":"f824d41a-8d47-46eb-a455-121735f770a8","resolution":{"observed_at":"2026-08-16T10:54:57.313180Z","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":"2026-08-16T10:54:57.843662Z","title":"Neural networks with physics-informed architectures and constraints for dynamical systems modeling","venue":null,"work_id":"57ed248a-af6b-4ce0-ae3a-f4e1ce72b523","year":2022},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.317388Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:ebe65de1f0590f893ad4cb12527d1f0debfc68428027a3d290378021c9600042","observation_id":"a6c22199-66e5-48cd-97a3-0ebdcbb4c233","resolution":{"observed_at":"2026-08-16T10:54:57.847978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.321672Z","title":"Implicit functions and solution mappings, volume 543","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.321672Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:fbb573c595c70fdceab4977c545aed7c3967753dfb2058b4254aa7118d537547","observation_id":"8b0cf544-05b6-4e52-976b-450711efff2b","resolution":{"observed_at":"2026-08-16T10:54:57.321672Z","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":"2026-08-16T10:54:57.325565Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.325565Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:870e5b9ea00cdd81fa87e211147a1a671e190ca4ef0142ff151f6ec311973200","observation_id":"5362898d-da7e-4386-923f-4a4a51fd4838","resolution":{"observed_at":"2026-08-16T10:54:57.325565Z","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":"2409.00393","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:54:57.611739Z","title":"Lyapunov neural ode feedback control policies","venue":null,"work_id":"1f3056aa-8eb2-46a1-ac73-d2e3c112016b","year":2024},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.329725Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:1a797007e9142432a97169cbf64efcb1ed6bec9da006918587afd3f3e8a26352","observation_id":"5f44301d-5b5d-466f-a60e-9bb9338eb4ac","resolution":{"observed_at":"2026-08-16T10:54:57.619723Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08465","last_updated":"2020-06-15T15:14:18Z","snapshot_observed_at":"2026-08-19T13:03:36.650078Z","submitted_at":"2020-06-15T15:14:18Z","title":"Neural Certificates for Safe Control Policies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08465","snapshot_observed_at":"2026-08-16T10:54:57.333895Z","title":"Neural certificates for safe control policies","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.333895Z"},"links":{"cited_paper":"/paper/2006.08465","citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:8cad0a541532e7311d1487b127a933ed617cc6e8fde777ea265f8492be954606","observation_id":"3ad30e10-7fe4-4e5a-a229-a0bfeba6cf38","resolution":{"observed_at":"2026-08-16T10:54:57.333895Z","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":"2026-08-16T10:54:57.816513Z","title":"Modeling trajectories with neural ordinary differential equations","venue":null,"work_id":"429de247-48a5-4d1e-8b57-b03105d0c684","year":2021},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.337806Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:fd54f7ea585766b767212465e6f9ecbb5e9e84e650cd06d4ff58c441a6e02e09","observation_id":"f4be4ac0-a64a-4ae1-ab82-76f87614eae1","resolution":{"observed_at":"2026-08-16T10:54:57.820470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.801407Z","title":"Learning robust state observers using neural odes","venue":null,"work_id":"deeaf702-19c2-4949-8c22-1e1a992f1cf6","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.341824Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:bf6b5c6ac7c604c89d6763ef1867f270eb37242452c199114fc9478a785dabed","observation_id":"c8b38b9a-5105-4056-8efd-3a94512026f5","resolution":{"observed_at":"2026-08-16T10:54:57.805500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.788427Z","title":"How deep do we need: Accelerating training and inference of neural ODE s via control perspective","venue":null,"work_id":"cbf70be4-1619-4196-9f56-6ccae155828f","year":2024},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.345625Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:13ed755ddad6e40f5c229c770d85e1fb4527f68c28b80d51e91197ac4c6d1526","observation_id":"4b9c3895-235e-433e-9aa5-ac9bad549eb8","resolution":{"observed_at":"2026-08-16T10:54:57.793029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.349473Z","title":"Learning complex motion plans using neural odes with safety and stability guarantees","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.349473Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:cb38212adad5722def63f6208a9c4853eda3f04316a393f9fe5ecd14b1bb2f76","observation_id":"bbdcf6fd-b525-4984-aee6-7d2a36ed0946","resolution":{"observed_at":"2026-08-16T10:54:57.349473Z","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":"2026-08-16T10:54:57.773003Z","title":"Safe optimal control using stochastic barrier functions and deep forward-backward sdes","venue":null,"work_id":"9bfb0892-f9da-47bc-98e1-af49e6dbd60e","year":2021},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.353485Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:214a2d484ce3e0ca7b14ba9e7909942a6bfe22fe50df3385b47af862c9225614","observation_id":"46f67ec2-5e8f-4a72-bd84-a2c829b9b633","resolution":{"observed_at":"2026-08-16T10:54:57.779698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.758969Z","title":"Mathematical theory of optimal processes","venue":null,"work_id":"691759d9-8129-4c1d-a6ab-54fda3c69f3b","year":1987},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.357478Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:916406180d8b5907697521c2ca3a62ad0a968555f1b670662d5f649f38be3580","observation_id":"cec51bcb-cb50-4a6d-aebd-130ed22d033e","resolution":{"observed_at":"2026-08-16T10:54:57.763333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.747313Z","title":"Lyanet: A lyapunov framework for training neural odes","venue":null,"work_id":"6a1c949b-6e05-4914-b6b6-69c1d767b1de","year":2022},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.361047Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:05635d685cbad7cf9d1229b05c14bec415916ced93d98e98c05203680e26984a","observation_id":"7fff659d-7477-4a8b-8064-8dbae66c2714","resolution":{"observed_at":"2026-08-16T10:54:57.751312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.736040Z","title":"Neural odes as feedback policies for nonlinear optimal control","venue":null,"work_id":"22179685-58d5-49f7-a350-02a2f04b2570","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.365296Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:d8e1ff1b89f3904df29533c77e9c376aa336aa85bca4ab06d45863dc44c96f25","observation_id":"9566e97a-6f5a-4644-8b1f-f6686e5ddf94","resolution":{"observed_at":"2026-08-16T10:54:57.739780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.723970Z","title":"A ‘universal’construction of artstein's theorem on nonlinear stabilization","venue":null,"work_id":"9480081a-cb41-4a78-bfd3-8a3a5633e7c8","year":1989},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.369097Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:cef0c97233fa38cc11cf69ff3f46e84c63ee51ca501afd1bd8912c693e725d54","observation_id":"95157fa9-321f-4dcf-b812-dc188348e58f","resolution":{"observed_at":"2026-08-16T10:54:57.728008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.710854Z","title":"Learning for safety-critical control with control barrier functions","venue":null,"work_id":"c4f134ff-f5e1-411a-b3a1-694ad268fcbc","year":2020},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.372793Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:9ccf5c072ae0ac93a21aac84f80496a6abbfdd272fe5a0e052f2f284ba931652","observation_id":"24e98e0a-ce19-444a-a561-c7a0748b0bb4","resolution":{"observed_at":"2026-08-16T10:54:57.715453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.697692Z","title":"Safety verification and controller synthesis for systems with input constraints","venue":null,"work_id":"8c5f1706-f68b-409c-925c-ca0f86d02409","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.376654Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:e50a442e7b3bb5f630441dfd8c844ca19581c6f909e77b992f039496da2e4946","observation_id":"624e3ccf-53b9-4f48-960b-7bc94f438a1a","resolution":{"observed_at":"2026-08-16T10:54:57.701994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11763","last_updated":"2024-03-18T13:20:38Z","snapshot_observed_at":"2026-08-17T20:02:01.228579Z","submitted_at":"2024-03-18T13:20:38Z","title":"Convex Co-Design of Control Barrier Function and Safe Feedback Controller Under Input Constraints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.11763","snapshot_observed_at":"2026-08-16T10:54:57.380659Z","title":"Convex co-design of control barrier function and safe feedback controller under input constraints","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.380659Z"},"links":{"cited_paper":"/paper/2403.11763","citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:c77296943e640d96f1c8d4bacd924622dcb394e9feefbfa0e1aedd6176cdea4a","observation_id":"266c80b6-a895-4775-a9f0-4df5d428b3c9","resolution":{"observed_at":"2026-08-16T10:54:57.380659Z","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":"2026-08-16T10:54:57.684472Z","title":"Control barrier functions for systems with high relative degree","venue":null,"work_id":"707a3db7-5e5e-437a-b9f3-49cc1b146fb5","year":2019},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.385124Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:b2bda4908acd9e4b1a9c691e62b7a66d94a01540ee1a68f06ef30b4dc5415a1d","observation_id":"9a51a668-a1c9-4ffa-8f86-e5f8fc99afb7","resolution":{"observed_at":"2026-08-16T10:54:57.689023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.670952Z","title":"Safe neural control for non-affine control systems with differentiable control barrier functions","venue":null,"work_id":"8ee5b2f2-0680-4b2b-9fc9-a3d9bf358b4e","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.388664Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:a8b5bad266897dce0f2f436af792c67c5040b3a62bbfeb22b16067b056ea0411","observation_id":"8855670b-0fbb-4316-a21b-aca6257bdd00","resolution":{"observed_at":"2026-08-16T10:54:57.675822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.656687Z","title":"Barriernet: Differentiable control barrier functions for learning of safe robot control","venue":null,"work_id":"4f182c31-cd2c-4d39-ba8d-2c40fac2bcaa","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.392325Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:cee1c8715687e0ff049827b22f1c06efea6c9baccecc37553c56a5aa84742a12","observation_id":"eca0a500-3211-4215-a2f0-333e0ecb8780","resolution":{"observed_at":"2026-08-16T10:54:57.660978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.641861Z","title":"Stable and safe reinforcement learning via a barrier-lyapunov actor-critic approach","venue":null,"work_id":"d71afd5b-fbed-49cc-816d-4e2e1a73c744","year":2023},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.396232Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:1d0013803bbcaef262420a37089e31c23acb8590b38198c58c2ada057eef60b1","observation_id":"798541aa-78c0-4dd7-8057-36911b6bfe1e","resolution":{"observed_at":"2026-08-16T10:54:57.646623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T10:54:57.628269Z","title":"Nlbac: A neural ode-based algorithm for state-wise stable and safe reinforcement learning","venue":null,"work_id":"6fcb38d4-ec05-4a51-8726-1c48916bcf36","year":2025},"citing_paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-16T10:54:57.399887Z"},"links":{"citing_paper":"/paper/2504.17139"},"observation_digest":"sha256:c59e67a0365844b7603ee24ae2b3a11c044f8d5fde3bcb270b54172874014aec","observation_id":"9ba518d2-e87f-4a34-99fa-93ee6ce21968","resolution":{"observed_at":"2026-08-16T10:54:57.632229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.17139","last_updated":"2025-04-23T23:09:37Z","latest_version":1,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-19T13:06:56.978791Z","submitted_at":"2025-04-23T23:09:37Z","title":"Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":19},"total_outbound_references":30},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2504.17139."}