{"as_of":"2026-08-09T14:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0e86fdf273216eea7594ad11345b4e4b66b2d372873af1fd59a5fc1d1dc01383","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T15:09:03.216788Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T06:52:07.922463Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.12108","last_updated":"2024-10-09T17:45:07Z","snapshot_observed_at":"2026-08-04T09:52:16.576629Z","submitted_at":"2024-07-16T18:28:40Z","title":"Private prediction for large-scale synthetic text generation","version":2},"cited_work":{"arxiv_id":"2407.12108","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.12108","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Private prediction for large-scale synthetic text generation","venue":null,"work_id":"c2372603-aafc-4fe5-9b6a-c4d39a117457","year":2024},"citing_paper":{"arxiv_id":"2507.02974","last_updated":"2026-05-05T10:38:51Z","snapshot_observed_at":"2026-08-02T17:49:51.122968Z","submitted_at":"2025-06-30T18:00:41Z","title":"InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-19T06:51:03.385016Z"},"links":{"cited_paper":"/paper/2407.12108","citing_paper":"/paper/2507.02974"},"observation_digest":"sha256:2e0d3b56fa51b2fb594fce787299723007cff6c5e7d1bf3db03a6bfc4dbd9d8a","observation_id":"ecadc3e9-6a3a-43e6-9f61-4831d71b79b4","resolution":{"observed_at":"2026-05-19T06:52:07.925161Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12108","last_updated":"2024-10-09T17:45:07Z","snapshot_observed_at":"2026-08-04T09:52:16.576629Z","submitted_at":"2024-07-16T18:28:40Z","title":"Private prediction for large-scale synthetic text generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12108","snapshot_observed_at":"2026-08-05T15:09:03.216788Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20452","last_updated":"2025-08-28T05:57:47Z","snapshot_observed_at":"2026-08-06T16:29:22.774856Z","submitted_at":"2025-08-28T05:57:47Z","title":"Evaluating Differentially Private Generation of Domain-Specific Text","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T15:09:03.216788Z"},"links":{"cited_paper":"/paper/2407.12108","citing_paper":"/paper/2508.20452"},"observation_digest":"sha256:4e5a4d6382a3cbce9b649e513c71b0dd7f36c1d6fc8d1985b8fb0bd1cce2df95","observation_id":"1195cbc0-b761-45da-8d8f-9aa6dfb96d1b","resolution":{"observed_at":"2026-08-05T15:09:03.216788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12108","last_updated":"2024-10-09T17:45:07Z","snapshot_observed_at":"2026-08-04T09:52:16.576629Z","submitted_at":"2024-07-16T18:28:40Z","title":"Private prediction for large-scale synthetic text generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12108","snapshot_observed_at":"2026-08-04T17:03:11.832776Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11176","last_updated":"2025-09-14T09:16:11Z","snapshot_observed_at":"2026-08-07T09:02:12.441325Z","submitted_at":"2025-09-14T09:16:11Z","title":"Differentially-private text generation degrades output language quality","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T17:03:11.832776Z"},"links":{"cited_paper":"/paper/2407.12108","citing_paper":"/paper/2509.11176"},"observation_digest":"sha256:22dbc52f430a194997532b4bc18a6c1b660ac0c842f2569f5f147901ccd832bb","observation_id":"25297d5b-7998-4b2e-be85-57a4076537d3","resolution":{"observed_at":"2026-08-04T17:03:11.832776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12108","last_updated":"2024-10-09T17:45:07Z","snapshot_observed_at":"2026-08-04T09:52:16.576629Z","submitted_at":"2024-07-16T18:28:40Z","title":"Private prediction for large-scale synthetic text generation","version":2},"cited_work":{"arxiv_id":"2407.12108","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.12108","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Private prediction for large-scale synthetic text generation","venue":null,"work_id":"c2372603-aafc-4fe5-9b6a-c4d39a117457","year":2024},"citing_paper":{"arxiv_id":"2605.01425","last_updated":"2026-05-02T12:53:18Z","snapshot_observed_at":"2026-07-06T23:14:38.379875Z","submitted_at":"2026-05-02T12:53:18Z","title":"Barriers to Counterfactual Credit Attribution for Autoregressive Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-09T15:13:01.278599Z"},"links":{"cited_paper":"/paper/2407.12108","citing_paper":"/paper/2605.01425"},"observation_digest":"sha256:8cd629810964329f16e17492d6a01b62421db7e68beab867853769b2dc271002","observation_id":"ec8046e3-575e-4022-a586-b0bdeb422911","resolution":{"observed_at":"2026-05-11T16:46:05.246029Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2407.12108/citation-record","integrity":"/paper/2407.12108/integrity","json":"/paper/2407.12108/citation-record.json","paper":"/paper/2407.12108"},"outbound":[],"paper":{"arxiv_id":"2407.12108","last_updated":"2024-10-09T17:45:07Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T09:52:16.576629Z","submitted_at":"2024-07-16T18:28:40Z","title":"Private prediction for large-scale synthetic text generation"},"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 4 inbound Pith citation observations for arXiv:2407.12108."}