{"as_of":"2026-08-15T08:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c0c168fbaba762f4b2e1adddde479f52cb5b00385af37130bebf124c1d7f8f95","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T02:03:42.456988Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-13T02:07:07.936411Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.03382","last_updated":"2023-03-06T18:59:13Z","snapshot_observed_at":"2026-08-14T18:00:16.904085Z","submitted_at":"2023-03-06T18:59:13Z","title":"Globally Optimal Training of Neural Networks with Threshold Activation Functions","version":1},"cited_work":{"arxiv_id":"2303.03382","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.03382","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Globally optimal training of neural networks with threshold activation functions","venue":null,"work_id":"59a8b31f-ac68-4f68-bee7-a2db205e558f","year":2023},"citing_paper":{"arxiv_id":"2604.22838","last_updated":"2026-04-21T06:27:18Z","snapshot_observed_at":"2026-08-13T01:18:51.805877Z","submitted_at":"2026-04-21T06:27:18Z","title":"Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T03:26:09.751493Z"},"links":{"cited_paper":"/paper/2303.03382","citing_paper":"/paper/2604.22838"},"observation_digest":"sha256:9244d60d96f40c004496a2103d79879f521295b3b1e60d256bedf61d3f59cbc6","observation_id":"e8aeb414-abc4-4ea3-abae-ba4a31d98818","resolution":{"observed_at":"2026-05-10T03:29:22.045809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.03382","last_updated":"2023-03-06T18:59:13Z","snapshot_observed_at":"2026-08-14T18:00:16.904085Z","submitted_at":"2023-03-06T18:59:13Z","title":"Globally Optimal Training of Neural Networks with Threshold Activation Functions","version":1},"cited_work":{"arxiv_id":"2303.03382","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.03382","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Globally optimal training of neural networks with threshold activation functions","venue":null,"work_id":"59a8b31f-ac68-4f68-bee7-a2db205e558f","year":2023},"citing_paper":{"arxiv_id":"2605.11558","last_updated":"2026-05-12T05:41:36Z","snapshot_observed_at":"2026-08-11T14:23:39.933384Z","submitted_at":"2026-05-12T05:41:36Z","title":"A Composite Activation Function for Learning Stable Binary Representations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-13T02:03:42.456988Z"},"links":{"cited_paper":"/paper/2303.03382","citing_paper":"/paper/2605.11558"},"observation_digest":"sha256:3d17fc170f7e35ec91e8b1c1509cdeee38d63ae7849aea15921da322a53f61f8","observation_id":"39068377-a22e-4d05-b16b-c135627799bd","resolution":{"observed_at":"2026-05-13T02:07:07.939635Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2303.03382/citation-record","integrity":"/paper/2303.03382/integrity","json":"/paper/2303.03382/citation-record.json","paper":"/paper/2303.03382"},"outbound":[],"paper":{"arxiv_id":"2303.03382","last_updated":"2023-03-06T18:59:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T18:00:16.904085Z","submitted_at":"2023-03-06T18:59:13Z","title":"Globally Optimal Training of Neural Networks with Threshold Activation Functions"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.03382."}