{"as_of":"2026-08-09T12:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c769519eb6241bc1f5bb90f1634ec10dd6bf2fbeb6f3fd58d6f1d9e5a7f0039","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":"2026-08-09T06:31:02.800959+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":"2026-07-12T14:06:47.857777Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2403.09441","last_updated":"2026-05-28T08:31:23Z","snapshot_observed_at":"2026-07-06T17:44:37.894023Z","submitted_at":"2024-03-14T14:34:25Z","title":"An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09441","snapshot_observed_at":"2026-07-12T14:06:47.857777Z","title":"Adversarial fine-tuning of compressed neural networks for joint improvement of robustness and efficiency,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.14427","last_updated":"2026-06-12T13:05:34Z","snapshot_observed_at":"2026-08-04T19:13:08.620726Z","submitted_at":"2026-06-12T13:05:34Z","title":"Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-12T14:06:47.857777Z"},"links":{"cited_paper":"/paper/2403.09441","citing_paper":"/paper/2606.14427"},"observation_digest":"sha256:db8a983a5e39025673d23a88cf3795b1c46873a607e1db41050b0d4c788011af","observation_id":"9f81d80e-b45d-47e3-8046-5c86a8ad210b","resolution":{"observed_at":"2026-07-12T14:06:47.857777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2403.09441/citation-record","integrity":"/paper/2403.09441/integrity","json":"/paper/2403.09441/citation-record.json","paper":"/paper/2403.09441"},"outbound":[],"paper":{"arxiv_id":"2403.09441","last_updated":"2026-05-28T08:31:23Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:44:37.894023Z","submitted_at":"2024-03-14T14:34:25Z","title":"An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural 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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2403.09441."}