{"as_of":"2026-08-15T23:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dfe274b6b54145378ae9bfaa5554ba370973ed4c11012842437ad68ddb113955","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T13:47:52.970964Z","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":"2026-05-20T21:23:44.545109Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.06302","last_updated":"2022-09-13T21:09:34Z","snapshot_observed_at":"2026-08-15T19:03:12.756548Z","submitted_at":"2022-09-13T21:09:34Z","title":"Optimization without Backpropagation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.06302","snapshot_observed_at":"2026-08-11T13:47:52.970964Z","title":"Belouze, Optimization without backpropagation, ArXiv:2209.06302v1 [cs.LG] (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.12783","last_updated":"2025-04-16T11:16:31Z","snapshot_observed_at":"2026-08-11T13:41:45.942105Z","submitted_at":"2024-12-17T10:43:26Z","title":"Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T13:47:52.970964Z"},"links":{"cited_paper":"/paper/2209.06302","citing_paper":"/paper/2412.12783"},"observation_digest":"sha256:233c4f6b2bc81fb8255898900ede7ef6351eb342c594d2999250e4c7a4eafb6d","observation_id":"8d026726-b8f0-4a24-9375-aa6ffde050fd","resolution":{"observed_at":"2026-08-11T13:47:52.970964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.06302","last_updated":"2022-09-13T21:09:34Z","snapshot_observed_at":"2026-08-15T19:03:12.756548Z","submitted_at":"2022-09-13T21:09:34Z","title":"Optimization without Backpropagation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.06302","snapshot_observed_at":"2026-08-05T10:59:56.170025Z","title":"Optimization without backpropagation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03503","last_updated":"2025-09-03T17:35:51Z","snapshot_observed_at":"2026-08-15T04:45:34.071306Z","submitted_at":"2025-09-03T17:35:51Z","title":"Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:59:56.170025Z"},"links":{"cited_paper":"/paper/2209.06302","citing_paper":"/paper/2509.03503"},"observation_digest":"sha256:3b0588fd982564c43933df6692e1906bb6f8407d1b36011db78c8780fe793615","observation_id":"553782f1-621e-4dc0-9aab-f1cddcae824e","resolution":{"observed_at":"2026-08-05T10:59:56.170025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.06302","last_updated":"2022-09-13T21:09:34Z","snapshot_observed_at":"2026-08-15T19:03:12.756548Z","submitted_at":"2022-09-13T21:09:34Z","title":"Optimization without Backpropagation","version":1},"cited_work":{"arxiv_id":"2209.06302","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.06302","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2209.06302 , year=","venue":null,"work_id":"3743509d-6489-479f-905e-9012ea514522","year":null},"citing_paper":{"arxiv_id":"2605.15622","last_updated":"2026-05-18T07:21:10Z","snapshot_observed_at":"2026-07-06T23:26:51.110846Z","submitted_at":"2026-05-15T05:11:43Z","title":"Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-20T21:19:55.074853Z"},"links":{"cited_paper":"/paper/2209.06302","citing_paper":"/paper/2605.15622"},"observation_digest":"sha256:7f31eb0e7eb71144c9c5dfb16e2a2761b6ec41c268ee70b559e266d65cc18efa","observation_id":"810a616f-d1f7-4ded-89ba-6af0529e498b","resolution":{"observed_at":"2026-05-20T21:23:44.546444Z","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/2209.06302/citation-record","integrity":"/paper/2209.06302/integrity","json":"/paper/2209.06302/citation-record.json","paper":"/paper/2209.06302"},"outbound":[],"paper":{"arxiv_id":"2209.06302","last_updated":"2022-09-13T21:09:34Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T19:03:12.756548Z","submitted_at":"2022-09-13T21:09:34Z","title":"Optimization without Backpropagation"},"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 3 inbound Pith citation observations for arXiv:2209.06302."}