{"as_of":"2026-08-08T15:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a8478c189434db202a904d29b8284e4cfad5261661200f25dada49b68725c965","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:06:26.464885Z","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":"2026-07-02T07:36:44.782476Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-08-06T19:06:26.464885Z","title":"AdaClip: Adaptive clipping for private sgd.arXiv:1908.07643,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2507.06525","last_updated":"2025-07-09T03:53:03Z","snapshot_observed_at":"2026-08-06T18:59:15.712263Z","submitted_at":"2025-07-09T03:53:03Z","title":"AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:26.464885Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2507.06525"},"observation_digest":"sha256:0f4468ad5f8e22dba82a3676987bcbd82d2c999ffaac9ac9b17a1311ea51e996","observation_id":"6c751ce0-90ed-4357-9fba-8e537a34f38f","resolution":{"observed_at":"2026-08-06T19:06:26.464885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-08-06T11:43:14.323680Z","title":"Adaclip: Adaptive clipping for private sgd","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-07T04:36:25.180750Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.323680Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:37710243f0f76d2bd3d10663cc9a37308e84eb0da6c8035871d927449567f035","observation_id":"69586a21-e7e8-4143-8853-2aa87e115a5b","resolution":{"observed_at":"2026-08-06T11:43:14.323680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":"1908.07643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-07-02T07:36:44.782476Z","title":"T., Yu, F","venue":null,"work_id":"ab0c0ee9-5801-40d4-aad1-e9e3ccda1d07","year":1908},"citing_paper":{"arxiv_id":"2602.22611","last_updated":"2026-05-11T08:15:32Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-26T04:32:14Z","title":"Mitigating Membership Inference in Intermediate Representations with Differentially Private Training","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T19:26:13.175797Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2602.22611"},"observation_digest":"sha256:517b88a8948208be0b92026b07e31a5225de7512a062d9e6c0cd3e2d29ab8066","observation_id":"f0ccc035-5bf6-409f-89c6-c8822f9748e8","resolution":{"observed_at":"2026-05-15T19:26:31.296429Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":"1908.07643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-07-02T07:36:44.782476Z","title":"T., Yu, F","venue":null,"work_id":"ab0c0ee9-5801-40d4-aad1-e9e3ccda1d07","year":1908},"citing_paper":{"arxiv_id":"2605.01679","last_updated":"2026-05-03T02:34:54Z","snapshot_observed_at":"2026-08-02T23:44:35.543346Z","submitted_at":"2026-05-03T02:34:54Z","title":"Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T16:25:50.762664Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2605.01679"},"observation_digest":"sha256:8eddfda40723fbc8aca66f4c0d28353ae2cb897d1e04f56836f94d81ea4d59d5","observation_id":"c343be83-57cf-40f5-b618-079350e83335","resolution":{"observed_at":"2026-05-11T08:56:00.141100Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":"1908.07643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-07-02T07:36:44.782476Z","title":"T., Yu, F","venue":null,"work_id":"ab0c0ee9-5801-40d4-aad1-e9e3ccda1d07","year":1908},"citing_paper":{"arxiv_id":"2606.04384","last_updated":"2026-06-03T03:00:26Z","snapshot_observed_at":"2026-08-02T17:43:05.261332Z","submitted_at":"2026-06-03T03:00:26Z","title":"Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T06:56:30.044721Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2606.04384"},"observation_digest":"sha256:126968b48959821d729c9a7ba74943bbe25356db354ca751f5954a916af161d4","observation_id":"4cc6aaef-acda-4ae9-85c0-576a37a907f1","resolution":{"observed_at":"2026-07-02T07:26:45.949137Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":"1908.07643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-07-02T07:36:44.782476Z","title":"T., Yu, F","venue":null,"work_id":"ab0c0ee9-5801-40d4-aad1-e9e3ccda1d07","year":1908},"citing_paper":{"arxiv_id":"2606.05435","last_updated":"2026-06-03T20:58:34Z","snapshot_observed_at":"2026-08-02T07:11:59.938831Z","submitted_at":"2026-06-03T20:58:34Z","title":"DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T06:53:20.330462Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2606.05435"},"observation_digest":"sha256:8bb328727114223eb724355e4b71c8b5a649ae500fee81eaba0ee0b8b6a359f7","observation_id":"2c859c33-6377-49ab-aebc-85dea9b5c0d8","resolution":{"observed_at":"2026-07-02T07:36:44.783834Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":"1908.07643","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-07-02T07:36:44.782476Z","title":"T., Yu, F","venue":null,"work_id":"ab0c0ee9-5801-40d4-aad1-e9e3ccda1d07","year":1908},"citing_paper":{"arxiv_id":"2606.29293","last_updated":"2026-07-02T19:48:17Z","snapshot_observed_at":"2026-08-07T05:55:49.383610Z","submitted_at":"2026-06-28T09:28:58Z","title":"Private training in quantum machine learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T07:36:50.442735Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2606.29293"},"observation_digest":"sha256:271433df79f50f64b565776f56ec3d2ba3d8fc0b5c65aac7a6969d70ee977484","observation_id":"dee681d4-0f20-49c9-964e-d83482a952f4","resolution":{"observed_at":"2026-06-30T08:04:28.927435Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-07-12T11:02:29.066725Z","title":"https://arxiv.org/abs/1908.07643","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.29293","last_updated":"2026-07-02T19:48:17Z","snapshot_observed_at":"2026-08-07T05:55:49.383610Z","submitted_at":"2026-06-28T09:28:58Z","title":"Private training in quantum machine learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-12T11:02:29.066725Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2606.29293"},"observation_digest":"sha256:3778695afb20407fc86123d2c520e6e8d4a97acbed77faa316fa89b7b13f3245","observation_id":"2a113c25-6f1c-454e-8595-eb5fc4cc1cca","resolution":{"observed_at":"2026-07-12T11:02:29.066725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-08-03T13:45:32.456451Z","title":"InInternational Conference on Learning Repre- sentations (ICLR)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.29100","last_updated":"2026-07-31T07:27:52Z","snapshot_observed_at":"2026-08-05T23:12:36.454894Z","submitted_at":"2026-07-31T07:27:52Z","title":"StraightDP: Geometry-Aware Differential Privacy for Rectified-Flow Transformers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T13:45:32.456451Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2607.29100"},"observation_digest":"sha256:88e8acb24fc4dc00e48812c8be983de00bf7e761790b256931205766b4d6bff5","observation_id":"2b61d6ec-fb24-4aab-a025-b5da1158c52d","resolution":{"observed_at":"2026-08-03T13:45:32.456451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1908.07643/citation-record","integrity":"/paper/1908.07643/integrity","json":"/paper/1908.07643/citation-record.json","paper":"/paper/1908.07643"},"outbound":[],"paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T00:40:41.624173Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1908.07643."}