{"as_of":"2026-08-17T13:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff9c032d971377feaebe40f3ea46fce1eb24806121e5569502e3478f58e51d8b","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":"2026-08-17T06:30:58.91139+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":"2026-08-14T15:53:12.507171Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T13:22:00.345590Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1602.04723","last_updated":"2016-02-15T16:16:56Z","snapshot_observed_at":"2026-08-16T09:54:49.688935Z","submitted_at":"2016-02-15T16:16:56Z","title":"Efficient Representation of Low-Dimensional Manifolds using Deep Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.04723","snapshot_observed_at":"2026-08-14T15:53:12.507171Z","title":"Efficient Representation of Low-Dimensional Manifolds using Deep Networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.00695","last_updated":"2019-08-02T04:01:13Z","snapshot_observed_at":"2026-08-15T18:34:39.250999Z","submitted_at":"2019-08-02T04:01:13Z","title":"Deep ReLU network approximation of functions on a manifold","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:53:12.507171Z"},"links":{"cited_paper":"/paper/1602.04723","citing_paper":"/paper/1908.00695"},"observation_digest":"sha256:415399e3b3fb01b82809f1c68fb5308b39ce5de38713b0a45cdc9f96326a34b0","observation_id":"f8f3b6f2-8362-4775-b0c8-9390d9797ffa","resolution":{"observed_at":"2026-08-14T15:53:12.507171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.04723","last_updated":"2016-02-15T16:16:56Z","snapshot_observed_at":"2026-08-16T09:54:49.688935Z","submitted_at":"2016-02-15T16:16:56Z","title":"Efficient Representation of Low-Dimensional Manifolds using Deep Networks","version":1},"cited_work":{"arxiv_id":"1602.04723","doi":null,"metadata_source":"pith","pith_arxiv_id":"1602.04723","snapshot_observed_at":"2026-08-06T13:22:00.345590Z","title":"Efficient Representation of Low-Dimensional Manifolds using Deep Networks","venue":"cs.NE","work_id":"14a8dbea-c2f4-4f94-89ac-81a84977f3de","year":2016},"citing_paper":{"arxiv_id":"2507.20853","last_updated":"2025-07-28T14:06:44Z","snapshot_observed_at":"2026-08-13T02:46:13.000774Z","submitted_at":"2025-07-28T14:06:44Z","title":"Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T13:21:58.932509Z"},"links":{"cited_paper":"/paper/1602.04723","citing_paper":"/paper/2507.20853"},"observation_digest":"sha256:1ef73f3912c5e75a26eff16d81982b00e30fccc9f8dd103002029465427bcbf1","observation_id":"3d0ddd87-e67e-4eff-b578-8cc59f93c5fe","resolution":{"observed_at":"2026-08-06T13:22:00.437498Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1602.04723/citation-record","integrity":"/paper/1602.04723/integrity","json":"/paper/1602.04723/citation-record.json","paper":"/paper/1602.04723"},"outbound":[],"paper":{"arxiv_id":"1602.04723","last_updated":"2016-02-15T16:16:56Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-16T09:54:49.688935Z","submitted_at":"2016-02-15T16:16:56Z","title":"Efficient Representation of Low-Dimensional Manifolds using Deep Networks"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1602.04723."}