{"as_of":"2026-08-18T05:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a023846d11d52e40ddd93b0ffc2ecabfd4711c79b56616223b75e9e40bdb697","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":"2026-08-17T06:30:58.91139+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":"2026-08-11T14:01:31.145034Z","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":"2026-08-11T14:01:31.237794Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.05590","last_updated":"2021-03-09T17:56:04Z","snapshot_observed_at":"2026-08-16T18:40:07.879855Z","submitted_at":"2021-03-09T17:56:04Z","title":"Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency","version":1},"cited_work":{"arxiv_id":"2103.05590","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.05590","snapshot_observed_at":"2026-08-11T14:01:31.237794Z","title":"Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency","venue":"cs.CR","work_id":"35957b0d-ccb9-41ef-bf1a-e6b87e5baa1b","year":2021},"citing_paper":{"arxiv_id":"2412.12563","last_updated":"2024-12-17T05:46:50Z","snapshot_observed_at":"2026-08-17T05:47:53.534686Z","submitted_at":"2024-12-17T05:46:50Z","title":"Task-Agnostic Language Model Watermarking via High Entropy Passthrough Layers","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T14:01:31.145034Z"},"links":{"cited_paper":"/paper/2103.05590","citing_paper":"/paper/2412.12563"},"observation_digest":"sha256:1d8fc2cf39e2c07a24019f1d8b76feefdf95887e70a6d4de25e3b2396fcd1d4e","observation_id":"b89d7693-c194-46e8-bc69-2bf8c5c529f1","resolution":{"observed_at":"2026-08-11T14:01:31.245225Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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/2103.05590/citation-record","integrity":"/paper/2103.05590/integrity","json":"/paper/2103.05590/citation-record.json","paper":"/paper/2103.05590"},"outbound":[],"paper":{"arxiv_id":"2103.05590","last_updated":"2021-03-09T17:56:04Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-16T18:40:07.879855Z","submitted_at":"2021-03-09T17:56:04Z","title":"Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency"},"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 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2103.05590."}