{"as_of":"2026-08-10T06:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e37cab8a3931fc8abea038148f09576f82bb3855fd58bce0931b3680d88f1c5d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T10:15:41.449979Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T11:09:11.653819Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.00241","last_updated":"2025-09-14T02:19:44Z","snapshot_observed_at":"2026-07-06T15:21:31.380819Z","submitted_at":"2023-04-29T11:46:53Z","title":"When Deep Learning Meets Polyhedral Theory: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.00241","snapshot_observed_at":"2026-08-09T10:15:41.449979Z","title":"doi:10.48550/arXiv.2305.00241, 2305.00241","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03016","last_updated":"2025-02-05T09:18:07Z","snapshot_observed_at":"2026-08-09T10:09:28.202535Z","submitted_at":"2025-02-05T09:18:07Z","title":"An analysis of optimization problems involving ReLU neural networks","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-09T10:15:41.449979Z"},"links":{"cited_paper":"/paper/2305.00241","citing_paper":"/paper/2502.03016"},"observation_digest":"sha256:7d1d55c54a5de3bd15be94c0755aee8a4a004411f128c19f823511e80bb8ded0","observation_id":"4351aa3d-1cc2-4784-8db4-3cc9f6247a18","resolution":{"observed_at":"2026-08-09T10:15:41.449979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.00241","last_updated":"2025-09-14T02:19:44Z","snapshot_observed_at":"2026-07-06T15:21:31.380819Z","submitted_at":"2023-04-29T11:46:53Z","title":"When Deep Learning Meets Polyhedral Theory: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.00241","snapshot_observed_at":"2026-08-07T14:52:03.953651Z","title":"When deep learning meets polyhedral theory: A survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18023","last_updated":"2025-06-13T08:43:13Z","snapshot_observed_at":"2026-08-09T09:48:06.443683Z","submitted_at":"2025-05-23T15:28:00Z","title":"Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T14:52:03.953651Z"},"links":{"cited_paper":"/paper/2305.00241","citing_paper":"/paper/2505.18023"},"observation_digest":"sha256:a30ef5d7c1ebb8b80a8f96c1fc36b7483f4e74c803c83414f3f8aa1969e3b2f4","observation_id":"690244c6-5575-4482-a4f7-8160c1af102d","resolution":{"observed_at":"2026-08-07T14:52:03.953651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.00241","last_updated":"2025-09-14T02:19:44Z","snapshot_observed_at":"2026-07-06T15:21:31.380819Z","submitted_at":"2023-04-29T11:46:53Z","title":"When Deep Learning Meets Polyhedral Theory: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.00241","snapshot_observed_at":"2026-08-07T12:48:05.036677Z","title":"Huchette, G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23640","last_updated":"2025-05-29T16:46:29Z","snapshot_observed_at":"2026-08-09T08:14:25.527044Z","submitted_at":"2025-05-29T16:46:29Z","title":"Global optimization of graph acquisition functions for neural architecture search","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:48:05.036677Z"},"links":{"cited_paper":"/paper/2305.00241","citing_paper":"/paper/2505.23640"},"observation_digest":"sha256:9814d178fda8625061b7905ca466169ca674463933df3ed3caa18f649969ef84","observation_id":"d0dc9e99-2208-47ff-87d6-059ec96054e4","resolution":{"observed_at":"2026-08-07T12:48:05.036677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.00241","last_updated":"2025-09-14T02:19:44Z","snapshot_observed_at":"2026-07-06T15:21:31.380819Z","submitted_at":"2023-04-29T11:46:53Z","title":"When Deep Learning Meets Polyhedral Theory: A Survey","version":4},"cited_work":{"arxiv_id":"2305.00241","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.00241","snapshot_observed_at":"2026-08-07T11:09:11.653819Z","title":"When Deep Learning Meets Polyhedral Theory: A Survey","venue":"math.OC","work_id":"bdfc36a8-2c1b-4d28-9bae-6082af1d5663","year":2023},"citing_paper":{"arxiv_id":"2506.03531","last_updated":"2025-06-04T03:26:31Z","snapshot_observed_at":"2026-08-07T10:58:16.440017Z","submitted_at":"2025-06-04T03:26:31Z","title":"Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T11:09:11.124282Z"},"links":{"cited_paper":"/paper/2305.00241","citing_paper":"/paper/2506.03531"},"observation_digest":"sha256:9922233749b11bf503756b21c92537e3c4c7332a6c63a2132dcc6656790423cb","observation_id":"69613b3c-2b20-4433-a9f6-f571771d4363","resolution":{"observed_at":"2026-08-07T11:09:11.767778Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.00241/citation-record","integrity":"/paper/2305.00241/integrity","json":"/paper/2305.00241/citation-record.json","paper":"/paper/2305.00241"},"outbound":[],"paper":{"arxiv_id":"2305.00241","last_updated":"2025-09-14T02:19:44Z","latest_version":4,"primary_category":"math.OC","snapshot_observed_at":"2026-07-06T15:21:31.380819Z","submitted_at":"2023-04-29T11:46:53Z","title":"When Deep Learning Meets Polyhedral Theory: A Survey"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.00241."}