{"as_of":"2026-08-11T10:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70c48012e3f009cf5ed1ca98b666f4f2c2329f040ba8c921b5d644819862f1aa","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T16:27:51.632224Z","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":"2026-08-09T16:27:51.871597Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2409.20447","last_updated":"2024-09-30T16:05:29Z","snapshot_observed_at":"2026-07-06T19:24:39.402031Z","submitted_at":"2024-09-30T16:05:29Z","title":"POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator","version":1},"cited_work":{"arxiv_id":"2409.20447","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.20447","snapshot_observed_at":"2026-08-09T16:27:51.871597Z","title":"POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator","venue":"cs.LG","work_id":"10962ee3-1653-4823-a205-64e921c9f8f9","year":2024},"citing_paper":{"arxiv_id":"2502.01700","last_updated":"2025-02-03T08:28:01Z","snapshot_observed_at":"2026-08-10T16:50:04.228992Z","submitted_at":"2025-02-03T08:28:01Z","title":"EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-09T16:27:51.632224Z"},"links":{"cited_paper":"/paper/2409.20447","citing_paper":"/paper/2502.01700"},"observation_digest":"sha256:0c855423a6b80840b7bfcc6044781b82f13d416c677edbdeb862dade21e0ac67","observation_id":"5ae1c322-c1e4-456b-9d9c-6627fea3e734","resolution":{"observed_at":"2026-08-09T16:27:51.877541Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2409.20447/citation-record","integrity":"/paper/2409.20447/integrity","json":"/paper/2409.20447/citation-record.json","paper":"/paper/2409.20447"},"outbound":[],"paper":{"arxiv_id":"2409.20447","last_updated":"2024-09-30T16:05:29Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T19:24:39.402031Z","submitted_at":"2024-09-30T16:05:29Z","title":"POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2409.20447."}