{"as_of":"2026-08-08T05:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f0bc8777274a08283a9f3908adf6614de20e0598761f744d7dc01979d4a2dec7","coverage":[{"denominator":6,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T14:42:35.162952Z","state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.21637/citation-record","integrity":"/paper/2607.21637/integrity","json":"/paper/2607.21637/citation-record.json","paper":"/paper/2607.21637"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T14:42:34.359852Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T14:42:34.359852Z"},"links":{"citing_paper":"/paper/2607.21637"},"observation_digest":"sha256:daf77e0ff014584681d899c14bc1eb0100e34ac7a743e460b704e35977493e1b","observation_id":"9c64aa4b-d434-4702-8d1b-8daa2112e39c","resolution":{"observed_at":"2026-08-01T14:42:34.359852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T14:42:34.518690Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T14:42:34.518690Z"},"links":{"citing_paper":"/paper/2607.21637"},"observation_digest":"sha256:fb718903ae646fdf15e6696ecc2bfd80829d27785b5bff7d6c6a615d9320f19c","observation_id":"e7c3ceb0-e4df-4750-a759-5934c0016d5d","resolution":{"observed_at":"2026-08-01T14:42:34.518690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.13234","last_updated":"2025-06-16T08:35:16Z","snapshot_observed_at":"2026-08-07T00:34:22.881688Z","submitted_at":"2025-06-16T08:35:16Z","title":"The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.13234","snapshot_observed_at":"2026-08-01T14:42:34.657120Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T14:42:34.657120Z"},"links":{"cited_paper":"/paper/2506.13234","citing_paper":"/paper/2607.21637"},"observation_digest":"sha256:322e848be72a9be6f3883d28af6f9bcaf563530bff0a7b14c0ecb206443d1db9","observation_id":"fc7d40c4-5438-430d-b000-d6116a2985c9","resolution":{"observed_at":"2026-08-01T14:42:34.657120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T14:42:34.860341Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T14:42:34.860341Z"},"links":{"citing_paper":"/paper/2607.21637"},"observation_digest":"sha256:7f25fe420e1cdcb29b975fa2a1d0bd7956086311900ae2cf4568da0541623fa8","observation_id":"01fa5ae2-42e0-4099-86fc-4065a88c9505","resolution":{"observed_at":"2026-08-01T14:42:34.860341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.07836","last_updated":"2026-07-13T20:41:27Z","snapshot_observed_at":"2026-08-03T23:00:43.525329Z","submitted_at":"2025-11-11T05:12:00Z","title":"Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Numerical Optimization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.07836","snapshot_observed_at":"2026-08-01T14:42:35.025479Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T14:42:35.025479Z"},"links":{"cited_paper":"/paper/2511.07836","citing_paper":"/paper/2607.21637"},"observation_digest":"sha256:b42dfc2b804a2bd525d9b403b27b462d1d5db7f70ea85bc04b29947a4de994b4","observation_id":"c20bf881-6b30-4a7b-b4ee-c31e6bb3f1d8","resolution":{"observed_at":"2026-08-01T14:42:35.025479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17032","last_updated":"2025-11-02T13:42:19Z","snapshot_observed_at":"2026-07-06T18:51:06.750136Z","submitted_at":"2024-07-24T06:35:05Z","title":"Gymnasium: A Standard Interface for Reinforcement Learning Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17032","snapshot_observed_at":"2026-08-01T14:42:35.162952Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T14:42:35.162952Z"},"links":{"cited_paper":"/paper/2407.17032","citing_paper":"/paper/2607.21637"},"observation_digest":"sha256:38065bb89c302d1ffd970dfce2a3c7845b0fa4079b437564a4ce82913fe02179","observation_id":"fa1084e6-7dae-455d-a055-f607ef3d2bc5","resolution":{"observed_at":"2026-08-01T14:42:35.162952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.21637","last_updated":"2026-07-21T03:30:58Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T08:30:51.139786Z","submitted_at":"2026-07-21T03:30:58Z","title":"Quasi-Monte Carlo Initialization for Meta-Reinforcement Learning"},"reference_resolution":{"displayed":6,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":6},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2607.21637."}