{"as_of":"2026-08-19T14:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1ca4d539728d2f71dbd5034210920475db762f1779dd59177dde129658404c2e","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":"2026-08-19T06:32:44.657259+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":"2026-08-15T19:10:54.459509Z","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":"2026-07-02T16:17:09.573107Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2005.02347","last_updated":"2020-09-30T00:31:30Z","snapshot_observed_at":"2026-08-13T13:41:44.485229Z","submitted_at":"2020-05-05T17:32:37Z","title":"Differential Machine Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.02347","snapshot_observed_at":"2026-08-15T19:10:54.459509Z","title":"Differential machine learning.arXiv preprint arXiv:2005.02347,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.17511","last_updated":"2025-06-20T23:21:39Z","snapshot_observed_at":"2026-08-17T19:01:39.599253Z","submitted_at":"2025-06-20T23:21:39Z","title":"Empirical Models of the Time Evolution of SPX Option Prices","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T19:10:54.459509Z"},"links":{"cited_paper":"/paper/2005.02347","citing_paper":"/paper/2506.17511"},"observation_digest":"sha256:a12e1645a7adc027edb3a6b918f297faf9af0131dec3ba4e4f0422c8b03f77ef","observation_id":"637bc1da-ab1a-45a3-93fd-9975903df2f7","resolution":{"observed_at":"2026-08-15T19:10:54.459509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02347","last_updated":"2020-09-30T00:31:30Z","snapshot_observed_at":"2026-08-13T13:41:44.485229Z","submitted_at":"2020-05-05T17:32:37Z","title":"Differential Machine Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.02347","snapshot_observed_at":"2026-08-06T20:47:23.695680Z","title":"Huge and A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.695680Z"},"links":{"cited_paper":"/paper/2005.02347","citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a3d73268e838eb1c0cce9aef21764d5a044d88033d50757f6f023292c003670b","observation_id":"57243ccd-b28b-491d-9bff-58c964a64606","resolution":{"observed_at":"2026-08-06T20:47:23.695680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02347","last_updated":"2020-09-30T00:31:30Z","snapshot_observed_at":"2026-08-13T13:41:44.485229Z","submitted_at":"2020-05-05T17:32:37Z","title":"Differential Machine Learning","version":4},"cited_work":{"arxiv_id":"2005.02347","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.02347","snapshot_observed_at":"2026-07-02T16:17:09.573107Z","title":"arXiv preprint arXiv:2005.02347 , year =","venue":null,"work_id":"a637a3b7-30fb-40df-9963-a5b72572d4ab","year":2005},"citing_paper":{"arxiv_id":"2606.05900","last_updated":"2026-06-04T09:06:18Z","snapshot_observed_at":"2026-08-08T01:36:47.361762Z","submitted_at":"2026-06-04T09:06:18Z","title":"Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Control","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-27T22:49:08.424468Z"},"links":{"cited_paper":"/paper/2005.02347","citing_paper":"/paper/2606.05900"},"observation_digest":"sha256:8286da9584003425492db07328b57e47adcf35bd7add47bc00455233a70bcf2a","observation_id":"fed6f6e4-e957-415b-aab2-fe079096a7b9","resolution":{"observed_at":"2026-07-02T16:17:09.574449Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2005.02347/citation-record","integrity":"/paper/2005.02347/integrity","json":"/paper/2005.02347/citation-record.json","paper":"/paper/2005.02347"},"outbound":[],"paper":{"arxiv_id":"2005.02347","last_updated":"2020-09-30T00:31:30Z","latest_version":4,"primary_category":"q-fin.CP","snapshot_observed_at":"2026-08-13T13:41:44.485229Z","submitted_at":"2020-05-05T17:32:37Z","title":"Differential Machine Learning"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2005.02347."}