{"as_of":"2026-08-21T19:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:52da73de844bf67cf546032f0c209eb17e51e39a201f79caa5472a03285d6b2e","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":"2026-08-21T06:32:19.484+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":"2026-08-16T12:23:55.785936Z","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":"2026-05-11T11:01:04.357666Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.04630","last_updated":"2019-09-10T17:14:14Z","snapshot_observed_at":"2026-08-20T19:39:15.115192Z","submitted_at":"2019-09-10T17:14:14Z","title":"Meta-Learning with Implicit Gradients","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.04630","snapshot_observed_at":"2026-08-16T12:23:55.785936Z","title":"Meta-learning with implicit gradients,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2504.12970","last_updated":"2025-08-04T14:05:05Z","snapshot_observed_at":"2026-08-19T21:43:27.015125Z","submitted_at":"2025-04-17T14:22:27Z","title":"MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T12:23:55.785936Z"},"links":{"cited_paper":"/paper/1909.04630","citing_paper":"/paper/2504.12970"},"observation_digest":"sha256:eb6bfc0e271d8f2e3516debd414c1b6bfbc6e21435a15a13ef48288a97bbe9e7","observation_id":"01bd8cbe-d4af-4955-a407-bfeaf1b81cfd","resolution":{"observed_at":"2026-08-16T12:23:55.785936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.04630","last_updated":"2019-09-10T17:14:14Z","snapshot_observed_at":"2026-08-20T19:39:15.115192Z","submitted_at":"2019-09-10T17:14:14Z","title":"Meta-Learning with Implicit Gradients","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.04630","snapshot_observed_at":"2026-08-15T21:12:38.904031Z","title":"Kakade, and Sergey Levine","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2505.10830","last_updated":"2025-05-16T04:00:31Z","snapshot_observed_at":"2026-08-19T16:20:02.887178Z","submitted_at":"2025-05-16T04:00:31Z","title":"A Discretization Approach for Bilevel Optimization with Low-Dimensional and Non-Convex Lower-Level","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:12:38.904031Z"},"links":{"cited_paper":"/paper/1909.04630","citing_paper":"/paper/2505.10830"},"observation_digest":"sha256:599496c46ddfc6c0d2abde61abd1fbab0da01e833e2a09e28c76c846c5de2fa3","observation_id":"9c0ff6ab-fdb7-4bdb-9f75-3a6ed5d01834","resolution":{"observed_at":"2026-08-15T21:12:38.904031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.04630","last_updated":"2019-09-10T17:14:14Z","snapshot_observed_at":"2026-08-20T19:39:15.115192Z","submitted_at":"2019-09-10T17:14:14Z","title":"Meta-Learning with Implicit Gradients","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.04630","snapshot_observed_at":"2026-08-07T12:01:20.406654Z","title":"Rajeswaran, C","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00796","last_updated":"2025-06-01T02:56:52Z","snapshot_observed_at":"2026-08-21T10:22:53.270730Z","submitted_at":"2025-06-01T02:56:52Z","title":"Higher-Order Automatic Differentiation Using Symbolic Differential Algebra: Bridging the Gap between Algorithmic and Symbolic Differentiation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:01:20.406654Z"},"links":{"cited_paper":"/paper/1909.04630","citing_paper":"/paper/2506.00796"},"observation_digest":"sha256:89301ff4acfc162d8d623119e0384091314d05dfa58563453a97d39ee8e3b23f","observation_id":"f0947503-5305-4004-9491-ff345bdf26bc","resolution":{"observed_at":"2026-08-07T12:01:20.406654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.04630","last_updated":"2019-09-10T17:14:14Z","snapshot_observed_at":"2026-08-20T19:39:15.115192Z","submitted_at":"2019-09-10T17:14:14Z","title":"Meta-Learning with Implicit Gradients","version":1},"cited_work":{"arxiv_id":"1909.04630","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.04630","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Shai Shalev-Shwartz and Shai Ben-David.Understanding Machine Learning: From Theory to Algorithms","venue":null,"work_id":"0122a27a-c804-4c74-adbe-8bd43d83b0e6","year":1909},"citing_paper":{"arxiv_id":"2604.13130","last_updated":"2026-04-13T22:27:31Z","snapshot_observed_at":"2026-08-15T16:42:10.035385Z","submitted_at":"2026-04-13T22:27:31Z","title":"Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T15:13:18.231802Z"},"links":{"cited_paper":"/paper/1909.04630","citing_paper":"/paper/2604.13130"},"observation_digest":"sha256:fdfa90cbf6311dc61a1e4689c4c8b79dde35b93dcf07b5d273634b34be8e77e9","observation_id":"37e7c4b7-f3c1-46bc-a2e9-a0f3209d2ddf","resolution":{"observed_at":"2026-05-11T11:01:04.363794Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1909.04630/citation-record","integrity":"/paper/1909.04630/integrity","json":"/paper/1909.04630/citation-record.json","paper":"/paper/1909.04630"},"outbound":[],"paper":{"arxiv_id":"1909.04630","last_updated":"2019-09-10T17:14:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T19:39:15.115192Z","submitted_at":"2019-09-10T17:14:14Z","title":"Meta-Learning with Implicit Gradients"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1909.04630."}