{"as_of":"2026-08-10T16:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bfd724680df262291a58b1c6adc3a94c03c00982fbf043387e0d43019ff72f3a","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T22:05:02.705232Z","state":"measured"},{"denominator":86,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":86,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T02:50:09.196457Z","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":"2026-05-22T02:50:58.332378Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"cited_work":{"arxiv_id":"2501.18914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.18914","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Choquette-Choo, Badih Ghazi, George Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, and Chiyuan Zhang","venue":null,"work_id":"048d2023-9d6a-4075-916a-df606314df31","year":2025},"citing_paper":{"arxiv_id":"2505.16329","last_updated":"2026-04-26T14:33:57Z","snapshot_observed_at":"2026-08-02T23:19:18.105404Z","submitted_at":"2025-05-22T07:34:27Z","title":"High-Dimensional Private Linear Regression with Optimal Rates","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-22T02:50:09.196457Z"},"links":{"cited_paper":"/paper/2501.18914","citing_paper":"/paper/2505.16329"},"observation_digest":"sha256:5d1b1b9246777f15ebd01f1543e89b66d1032ce2ee42673d9e32a22e9f9c39f4","observation_id":"629c9eb3-66de-4892-a8d2-0135ea09d741","resolution":{"observed_at":"2026-05-22T02:50:58.335502Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"cited_work":{"arxiv_id":"2501.18914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.18914","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Choquette-Choo, Badih Ghazi, George Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, and Chiyuan Zhang","venue":null,"work_id":"048d2023-9d6a-4075-916a-df606314df31","year":2025},"citing_paper":{"arxiv_id":"2601.10237","last_updated":"2026-04-16T13:16:57Z","snapshot_observed_at":"2026-07-30T08:45:45.642515Z","submitted_at":"2026-01-15T09:50:36Z","title":"Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-16T13:37:50.765735Z"},"links":{"cited_paper":"/paper/2501.18914","citing_paper":"/paper/2601.10237"},"observation_digest":"sha256:24b527fd6e0f9c974971e97643ef2650df2fada212f7634e29f5750d0cdcb5c5","observation_id":"3524c178-261c-4dfc-8770-f30d501c0f19","resolution":{"observed_at":"2026-05-16T13:37:56.495543Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"cited_work":{"arxiv_id":"2501.18914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.18914","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Choquette-Choo, Badih Ghazi, George Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, and Chiyuan Zhang","venue":null,"work_id":"048d2023-9d6a-4075-916a-df606314df31","year":2025},"citing_paper":{"arxiv_id":"2605.07072","last_updated":"2026-05-08T00:47:11Z","snapshot_observed_at":"2026-07-06T23:19:30.387067Z","submitted_at":"2026-05-08T00:47:11Z","title":"Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-11T01:06:21.633242Z"},"links":{"cited_paper":"/paper/2501.18914","citing_paper":"/paper/2605.07072"},"observation_digest":"sha256:f157d8e1ff48872160bf90c5d863d106725182c5a19e67673fd1cf7772f1ab03","observation_id":"d3926d10-a519-42cb-a817-f435214fa1e7","resolution":{"observed_at":"2026-05-11T04:45:58.064210Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.18914/citation-record","integrity":"/paper/2501.18914/integrity","json":"/paper/2501.18914/citation-record.json","paper":"/paper/2501.18914"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.460455Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.460455Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:382951be6e01942eaffa521219c3a0b14cd51bbbd4d2502e6f0f14c3b4661b35","observation_id":"f0373ff8-9971-4279-a031-a6ba44d8f316","resolution":{"observed_at":"2026-08-09T22:05:02.460455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.464676Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.464676Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:3ce992e7366d9205e08ec2b4be298a6864528cb9214f13edb740951ea9940074","observation_id":"61f594a2-8dd0-4d6c-bd0e-35f56ae47906","resolution":{"observed_at":"2026-08-09T22:05:02.464676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.468415Z","title":"B., Mironov, I., Talwar, K., and Zhang, L","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.468415Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:e0c69cb8459d2c833496f2af2b26345854276953392675c18f2f0a81868fa5cb","observation_id":"a02fce59-7c6a-4391-8ce3-37579efdf4a8","resolution":{"observed_at":"2026-08-09T22:05:02.468415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-09T22:05:02.471763Z","title":"L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.471763Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:714e1a4d397e7f9cbb7c44293a5804edda18d669b73760246c9167ef1e133e8f","observation_id":"875a1ea7-8e41-42da-89ba-f79f146975aa","resolution":{"observed_at":"2026-08-09T22:05:02.471763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.761225Z","title":"The crossroads of innovation and privacy: Private synthetic data for generative AI","venue":null,"work_id":"891c84b2-b9c0-4368-a599-e9d9ba50e3c4","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.475509Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:f79e7eac33ec30e9bcad8606b115d09cc5dc2361c2d8937a785c45b58623360d","observation_id":"a46ef737-e423-4636-84f9-8516cfe717dd","resolution":{"observed_at":"2026-08-09T22:05:15.764022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T22:05:02.478985Z","title":"Private prediction for large-scale synthetic text generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.478985Z"},"links":{"cited_paper":"/paper/2407.12108","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:84623cf9152cce0549943103cba3861838cdc9f9e6d3f7e015daaaf11051d138","observation_id":"d15e446e-47b6-442b-9e5e-0fbcaab2412c","resolution":{"observed_at":"2026-08-09T22:05:02.478985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.754059Z","title":"Large-scale differentially private BERT","venue":null,"work_id":"a820ac97-7be8-4523-9684-ee1fb2137ffe","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.482745Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:b226686e89feb10edcc74e64e4251d9d48f20891a4bcbc982a49a03c015e41f9","observation_id":"5ac5903d-fda1-4a9c-a007-0b513de417f4","resolution":{"observed_at":"2026-08-09T22:05:15.756627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10403","last_updated":"2023-09-13T20:35:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T17:46:53Z","title":"PaLM 2 Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10403","snapshot_observed_at":"2026-08-09T22:05:02.485922Z","title":"M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.485922Z"},"links":{"cited_paper":"/paper/2305.10403","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:802bb1ae24b0514dc2c6a7d2c46fd632039e29444605cd5c5fbcabbe86d09048","observation_id":"7a2c9af1-4763-4bb4-88c1-68c25e77beba","resolution":{"observed_at":"2026-08-09T22:05:02.485922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.746796Z","title":"Privacy amplification by subsampling: Tight analyses via couplings and divergences, 2018","venue":null,"work_id":"983ecb80-e0fd-42e4-bd0e-88669f0a5a5b","year":2018},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.489773Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:c81dc2b7726cdfe61225fb206c872aa4ef743ccc002fb405310149887c8244e6","observation_id":"4bdb8029-de59-4c2b-b0a2-d7f9d1169c81","resolution":{"observed_at":"2026-08-09T22:05:15.749559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.738986Z","title":"Reconstructing training data with informed adversaries","venue":null,"work_id":"c3bc8f02-714f-4092-af92-81b2a736a7aa","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.492996Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:f0bdf08a7d5e91e3cec8a66aeb707cb231ff1d58df403d9a3e1cdddd76707454","observation_id":"c4ea427c-54a1-4fb6-8335-d8a221d33be2","resolution":{"observed_at":"2026-08-09T22:05:15.742146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.730874Z","title":"Private empirical risk minimization: Efficient algorithms and tight error bounds","venue":null,"work_id":"322baa9a-4b0c-4fab-9ed7-7b6159673dc5","year":2014},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.495991Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:4a80536f6763d1cd1c9be3493dc1a19e53a496a3c016d972d6d311f4d0e25581","observation_id":"5bab3390-66b1-4e27-90c5-f98b05735290","resolution":{"observed_at":"2026-08-09T22:05:15.733951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.10888","last_updated":"2023-08-21T17:42:33Z","snapshot_observed_at":"2026-08-07T20:51:50.220870Z","submitted_at":"2023-08-21T17:42:33Z","title":"Unlocking Accuracy and Fairness in Differentially Private Image Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10888","snapshot_observed_at":"2026-08-09T22:05:02.499144Z","title":"H., Hayes, J., Stanforth, R., Stutz, D., Kohli, P., Smith, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.499144Z"},"links":{"cited_paper":"/paper/2308.10888","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:4b6234f8f1cc93c12baa11ddc4e1e7751512d5ea58f69ceb328f31a207317097","observation_id":"eea3ad11-cfac-4603-b784-4bf4cd836ef2","resolution":{"observed_at":"2026-08-09T22:05:02.499144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.722572Z","title":"S., Sutawika, L., Schoelkopf, H., Anthony, Q., Purohit, S., and Raff, E","venue":null,"work_id":"93d82531-6095-43e5-bd50-1816778d7e88","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.502400Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:994d0eac1783beae14afbfde8d271223d8a312a9d82cb7532f595073c91a6e54","observation_id":"e3d5a317-e77e-4395-ae95-ce8fff419810","resolution":{"observed_at":"2026-08-09T22:05:15.725626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.714429Z","title":"Scalable and efficient training of large convolutional neural networks with differential privacy","venue":null,"work_id":"46517355-d6d7-4057-9aaf-96a1a1eef896","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.505391Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:df9f658adf3e921006fb8150c68786a3578534d52cf89da9dc4a7636a9efb903","observation_id":"49a556cf-ce3c-42f5-a39f-07d14ef2ded5","resolution":{"observed_at":"2026-08-09T22:05:15.717505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.706366Z","title":"Differentially private optimization on large model at small cost","venue":null,"work_id":"e9645118-2f8a-4549-885f-2f147c4f8310","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.508420Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:c71a013674907db13f1c6514effd07ed4d5de6b9597a685af2a214b3ecdf5fef","observation_id":"3f005122-75f0-4d23-a70a-c9456393397c","resolution":{"observed_at":"2026-08-09T22:05:15.709378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.698123Z","title":"Extracting training data from large language models","venue":null,"work_id":"cd8d2fb1-6818-4bfa-bb4a-f5fbf8fdd527","year":2021},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.511347Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:d2cdc5cd1d9c312ade31f93f41fb45b7c6a258d8b7c6c485f039ee829090ed40","observation_id":"3deb14b3-6036-4044-abd7-fa063ceba6ec","resolution":{"observed_at":"2026-08-09T22:05:15.701156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.689893Z","title":"Quantifying memorization across neural language models","venue":null,"work_id":"97921ede-e550-4cc8-916d-41f5209c75bf","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.514388Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:18b4564a12616611494063931fc7a154b59666f683ffd81bf9bc1e4e7be99e8a","observation_id":"895368f2-7799-4861-9444-ce22f1b1ffa0","resolution":{"observed_at":"2026-08-09T22:05:15.692999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.681675Z","title":"A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tram \\`e r, F","venue":null,"work_id":"0d8a7bbc-4e9a-42e0-aa3c-c8b1d43d7dd6","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.517425Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:b1a7395de2ddf9139fe31944c89585ba09fee4cb96cea373c9b16536e80ecf12","observation_id":"2a206b09-f01a-4bc1-8e81-19b919aa87bb","resolution":{"observed_at":"2026-08-09T22:05:15.684835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07737","last_updated":"2024-07-10T15:07:58Z","snapshot_observed_at":"2026-08-10T09:03:38.272446Z","submitted_at":"2024-07-10T15:07:58Z","title":"Fine-Tuning Large Language Models with User-Level Differential Privacy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07737","snapshot_observed_at":"2026-08-09T22:05:02.520377Z","title":"B., Mitchell, N., Pillutla, K., and Rush, K","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.520377Z"},"links":{"cited_paper":"/paper/2407.07737","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:3b1ffcf6d0eae709869faef0964b4f10535aeffa3e1694cc3ecf943eac01fd55","observation_id":"f4006fd1-d80f-413e-a2dd-f0258cdd653a","resolution":{"observed_at":"2026-08-09T22:05:02.520377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.06675","last_updated":"2023-05-08T21:49:57Z","snapshot_observed_at":"2026-08-02T01:29:22.350700Z","submitted_at":"2023-02-13T20:27:30Z","title":"Symbolic Discovery of Optimization Algorithms","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.06675","snapshot_observed_at":"2026-08-09T22:05:02.523732Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.523732Z"},"links":{"cited_paper":"/paper/2302.06675","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:df92eed2833515bcbc5ab64ec7143c3d8de486dfb6d58ced9832e27cd213183e","observation_id":"ca211374-f08d-44f9-80fb-7c41187d1ccf","resolution":{"observed_at":"2026-08-09T22:05:02.523732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.673858Z","title":null,"venue":null,"work_id":"055ee324-3608-4268-a4d8-ea013dc12f2b","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.526910Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:c9b90d11fc713c59e3ab98e4bbc88478d9448ce880b47430230cbd1f9de7df2d","observation_id":"e21b2d25-e02d-4472-9a69-ad509a81ea54","resolution":{"observed_at":"2026-08-09T22:05:15.676664Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.665405Z","title":"Mind the privacy unit! user-level differential privacy for language model fine-tuning","venue":null,"work_id":"28e2b217-3e62-4e22-861b-e0a07a7cd11c","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.529416Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:2530a5f28a8c282ee6e533cdcd45121621f433ac3f87fe17a841d20ae1bdd92f","observation_id":"dedc7793-05d4-437a-87d8-d0d3afbcad11","resolution":{"observed_at":"2026-08-09T22:05:15.668663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.656728Z","title":"Scalable DP-SGD : Shuffling vs","venue":null,"work_id":"740d22af-15d9-4bb8-9203-eee62606ac93","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.531816Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:fbea1c5e6aed024fa70ba1d9127b773a9223c54a879c740e5607a1077908fc4e","observation_id":"b28f6f38-5cbb-4a31-8648-59dafbe8671b","resolution":{"observed_at":"2026-08-09T22:05:15.659847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.13650","last_updated":"2022-06-16T17:47:42Z","snapshot_observed_at":"2026-08-09T01:53:18.372890Z","submitted_at":"2022-04-28T17:10:56Z","title":"Unlocking High-Accuracy Differentially Private Image Classification through Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.13650","snapshot_observed_at":"2026-08-09T22:05:02.534080Z","title":"L., and Balle, B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.534080Z"},"links":{"cited_paper":"/paper/2204.13650","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:cc6e4e5a57d45faad86e0c610057066a5e7f8c1fb2a0188e3df3ff941d449102","observation_id":"304dea68-846e-4f8a-a2b4-55a2597a44d4","resolution":{"observed_at":"2026-08-09T22:05:02.534080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.648174Z","title":"BERT : Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":"4cdf30d3-c5a8-4075-9925-21877d1ef570","year":2019},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.536687Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:2d6fc71ad8530ac40d20af1e168824b3139275a4b32cbc7deebd14b4c0101ace","observation_id":"799558ed-aab7-495f-856f-0828e7cf1d72","resolution":{"observed_at":"2026-08-09T22:05:15.651406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.640385Z","title":"S., Wang, T., Huang, C., and Sun, H","venue":null,"work_id":"cba2a927-05a6-483b-a9a9-6a9fcbafa4c8","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.539119Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:6c5af1dc4614950d339ad05de4752ae92d53b9548d3b492fb2d1db2de15c342c","observation_id":"dd298b68-236c-4c98-9fde-c471a95bdfa4","resolution":{"observed_at":"2026-08-09T22:05:15.642934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.632543Z","title":"Flocks of stochastic parrots: Differentially private prompt learning for large language models","venue":null,"work_id":"d5752dbe-b1ec-4891-87cc-0de96ccc04bc","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.541494Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:ca8b78fcdbb57785afcde18e6be8d98f74d252f7448d2ae0b1b8db7b4c7e3764","observation_id":"247ce4aa-b7e9-4cbd-abe2-95d59c2926dd","resolution":{"observed_at":"2026-08-09T22:05:15.635318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.624493Z","title":"On the privacy risk of in-context learning","venue":null,"work_id":"ebd27529-96a6-4c95-a900-7f6005a98b3e","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.543817Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:d51e5ebff2bdb895bf007b5e6bdb98f99be84910862f7ad1236ca96467ed08c3","observation_id":"7b89da64-14c3-45b9-b4f5-e59a42c54203","resolution":{"observed_at":"2026-08-09T22:05:15.627467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-09T22:05:02.546186Z","title":"The Llama 3 herd of models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.546186Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:bed002523bdf57a4770b590ee038a1c22674b6661850afcf4e6055e471bba6a2","observation_id":"0550f31a-a31d-4492-afb0-6c9bc3dd29d3","resolution":{"observed_at":"2026-08-09T22:05:02.546186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.616160Z","title":"Calibrating noise to sensitivity in private data analysis","venue":null,"work_id":"8453b5e3-6ea9-42f2-adfe-c00c27d1e042","year":2006},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.548692Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:81a174e3bec1a6ae02e35116d1321c40b9547001bbc88da538f6009bb9c904aa","observation_id":"78dda07b-586a-4f8a-9f13-8d2da79155ef","resolution":{"observed_at":"2026-08-09T22:05:15.619209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08540","last_updated":"2024-06-14T20:21:05Z","snapshot_observed_at":"2026-08-10T12:51:53.068218Z","submitted_at":"2024-03-13T13:54:00Z","title":"Language models scale reliably with over-training and on downstream tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08540","snapshot_observed_at":"2026-08-09T22:05:02.550944Z","title":"Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.550944Z"},"links":{"cited_paper":"/paper/2403.08540","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:df0b00ae93c9a8fbc91cbfd3e4ebe1db870ede117912a37db7fa241b3a4199b8","observation_id":"822192f8-2cf8-41a8-a43b-7ef6ea8c6068","resolution":{"observed_at":"2026-08-09T22:05:02.550944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.607378Z","title":"Predictability and surprise in large generative models","venue":null,"work_id":"5b1856df-b543-44fd-bef7-ca13ac9a20bf","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.554175Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:d90f770efdb7d6be3e581ed7c0269846b76f131178ad583413fa474dd9b90d5d","observation_id":"c493a56c-3e1d-4b3e-be08-4fb18e958e3d","resolution":{"observed_at":"2026-08-09T22:05:15.610626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-09T22:05:02.557123Z","title":"Gemini: a family of highly capable multimodal models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.557123Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:0c438510ca7674f9caf4399c33078f016e081dc1589a0d1ee1d57d253de62472","observation_id":"5f30aae0-6cd9-4171-91ea-2497b41e13cc","resolution":{"observed_at":"2026-08-09T22:05:02.557123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08295","last_updated":"2024-04-16T12:52:47Z","snapshot_observed_at":"2026-08-03T03:29:01.959523Z","submitted_at":"2024-03-13T06:59:16Z","title":"Gemma: Open Models Based on Gemini Research and Technology","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08295","snapshot_observed_at":"2026-08-09T22:05:02.560629Z","title":"S., Love, J., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.560629Z"},"links":{"cited_paper":"/paper/2403.08295","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:1224f338327697cf77ba51aef782c39317d7b8aab4359fc2b03e86e9ac4972a0","observation_id":"8cdadba0-1e12-4b4e-a025-6c47bd7578cc","resolution":{"observed_at":"2026-08-09T22:05:02.560629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00118","last_updated":"2024-10-02T15:22:49Z","snapshot_observed_at":"2026-08-02T16:20:09.773989Z","submitted_at":"2024-07-31T19:13:07Z","title":"Gemma 2: Improving Open Language Models at a Practical Size","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00118","snapshot_observed_at":"2026-08-09T22:05:02.563895Z","title":"G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ram \\'e , A., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.563895Z"},"links":{"cited_paper":"/paper/2408.00118","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:6e7835d73b78f325b492ee5f8531561c20cf554fbb4a9be1ed5dc28ca9bc5071","observation_id":"4cb2ca6f-924e-4292-a411-ce10f8cc8f47","resolution":{"observed_at":"2026-08-09T22:05:02.563895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13861","last_updated":"2023-02-27T15:02:04Z","snapshot_observed_at":"2026-08-08T18:55:37.205842Z","submitted_at":"2023-02-27T15:02:04Z","title":"Differentially Private Diffusion Models Generate Useful Synthetic Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13861","snapshot_observed_at":"2026-08-09T22:05:02.566936Z","title":"L., Wiles, O., and Balle, B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.566936Z"},"links":{"cited_paper":"/paper/2302.13861","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:64cea4b563017e79a0e3faf87767e7aec175224537ed74389ce076d50a6e1259","observation_id":"bc758505-3f81-4876-a21b-66ee7e1a0cb8","resolution":{"observed_at":"2026-08-09T22:05:02.566936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.598773Z","title":"and Latonero, M","venue":null,"work_id":"d83132c1-b8d0-4d48-a07e-c2462cb741fc","year":2017},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.570165Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:4c5e2e223843cbc70feca402153b9126e47ad8f67bffc36d52369d9647c9fe47","observation_id":"51b4e952-24b9-47a9-92a5-e08d6f6b7bc2","resolution":{"observed_at":"2026-08-09T22:05:15.601864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.589861Z","title":"Google's differential privacy libraries., 2022","venue":null,"work_id":"053b0490-e377-4e97-a3da-d0d3df889243","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.573081Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:1b6b880084bc0a4e8b99c37913ca6f0549e5e63e4c19aba6238e3acae259948a","observation_id":"531f4b89-b57e-4725-9399-fafa7517f89f","resolution":{"observed_at":"2026-08-09T22:05:15.593009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-09T19:52:33.533277Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-09T22:05:02.576030Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.576030Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:e0601390e75b4576bb2c29acbc0081dca1642d2cb24bf3c7c8fd5b407ab80e9e","observation_id":"204ccb5c-3c55-4daa-bada-596d6f559990","resolution":{"observed_at":"2026-08-09T22:05:02.576030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.581088Z","title":"T., Zhang, C., Li, Z., Li, B., and Wang, Z","venue":null,"work_id":"07336aab-0357-42f2-8190-65c6c53dd8c1","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.579167Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:de90875d3303c13a446d3a516e66546ef49875cbabac496e014968cecdda6cba","observation_id":"9ab250db-00ac-4786-9ab7-0eb61115ac32","resolution":{"observed_at":"2026-08-09T22:05:15.584233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.582136Z","title":null,"venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.582136Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:9100e1c5fe47c8de6278965fe5c64997b0718b6b87ccfe45d4093272571f0633","observation_id":"efcf04e7-7395-4d3f-a199-0eff39f8a78b","resolution":{"observed_at":"2026-08-09T22:05:02.582136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17546","last_updated":"2023-09-11T16:58:48Z","snapshot_observed_at":"2026-08-04T04:34:05.607113Z","submitted_at":"2022-10-31T17:57:55Z","title":"Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.17546","snapshot_observed_at":"2026-08-09T22:05:02.585035Z","title":"A., and Carlini, N","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.585035Z"},"links":{"cited_paper":"/paper/2210.17546","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:3878ba49867f97329bb9fa1bcb65ce7df734740b7cd6b6e79eeb2d9bf7577dcc","observation_id":"e246c354-c05c-45b2-8cd9-db0c8346a679","resolution":{"observed_at":"2026-08-09T22:05:02.585035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03928","last_updated":"2023-07-08T08:02:47Z","snapshot_observed_at":"2026-07-06T15:51:43.130122Z","submitted_at":"2023-07-08T08:02:47Z","title":"Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03928","snapshot_observed_at":"2026-08-09T22:05:02.588331Z","title":"Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.588331Z"},"links":{"cited_paper":"/paper/2307.03928","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:9ac84756f2181795f3d8208de79f2d039d888bdbdd39d5fe8bd235c62e8d419d","observation_id":"610698f1-82a2-4550-b270-86ea014c0dba","resolution":{"observed_at":"2026-08-09T22:05:02.588331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.567379Z","title":"Beyond the calibration point: Mechanism comparison in differential privacy","venue":null,"work_id":"79d8f7dc-28bc-4bce-9541-2b32e17346fb","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.591575Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:4f1c89e955b68b5ba3038ef4b64d8ab8432c1d65df8726ef5070f7aa5cfa829d","observation_id":"3b8f90fa-1f90-4755-8bcc-6ff6a9fb6b59","resolution":{"observed_at":"2026-08-09T22:05:15.570636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-09T22:05:02.594525Z","title":"B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.594525Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:e5ed0f40c1346623efa2f4fb17e80438fb851d70943ccf008f0ae9bf5f545470","observation_id":"9010c1b9-ea61-4ba5-9d2d-a4474def2505","resolution":{"observed_at":"2026-08-09T22:05:02.594525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.597755Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.597755Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:7d5d297f72c4c30584bcbaf6e3d0bf0b96163fbff6b28e41062f2ffe6ab16b26","observation_id":"c916c46d-e4b3-4813-aa2d-8e886573fe4e","resolution":{"observed_at":"2026-08-09T22:05:02.597755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.553092Z","title":"and Ponomareva, N","venue":null,"work_id":"ba6a1a11-0ea9-46ff-b04c-41bc41949a3a","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.600675Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:794504137ffa5b3b93a4a01c589af574fe6d2b1e32a715a66ce3e5b351a55eee","observation_id":"e04b89ba-cf82-455d-8e87-1fbc40ecf757","resolution":{"observed_at":"2026-08-09T22:05:15.556608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12328","last_updated":"2022-02-09T04:55:14Z","snapshot_observed_at":"2026-08-04T13:45:26.163424Z","submitted_at":"2022-01-28T18:48:18Z","title":"Toward Training at ImageNet Scale with Differential Privacy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12328","snapshot_observed_at":"2026-08-09T22:05:02.603557Z","title":"Toward training at ImageNet scale with differential privacy","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.603557Z"},"links":{"cited_paper":"/paper/2201.12328","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:68999e49c214a7ec10f9a24262c9b66acb7385f45654260c5cb025a543683d46","observation_id":"1494b815-bea2-4346-a848-0da50d6038ac","resolution":{"observed_at":"2026-08-09T22:05:02.603557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.544099Z","title":"Large language models can be strong differentially private learners","venue":null,"work_id":"df99af2e-46c7-4a12-b39a-39404e3be36e","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.606708Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:b4bab74678266a3e175885cefb46d418c59503e6a29a961074f1131e4eb46464","observation_id":"3ea0bf12-00c5-4c98-99fc-9ba54567ef65","resolution":{"observed_at":"2026-08-09T22:05:15.547312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.535216Z","title":"J., Novak, R., Lee, J., Wortsman, M., Xiao, L., Everett, K., Alemi, A","venue":null,"work_id":"88349b94-b7e8-4b1a-a17b-8e95a7376af4","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.609764Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:f0da2956a930199d29dd54093931e752bd6822f5f81b3b4001ecc333f08ff820","observation_id":"1b9ec1d5-c3ae-46f4-ad65-f9684a0ff5d0","resolution":{"observed_at":"2026-08-09T22:05:15.538451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-09T20:34:52.923500Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-09T22:05:02.612675Z","title":"and Hutter, F","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.612675Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:2e26706ea2e550424bf2c23361fc44d9c104d3357913c153f5f83edcf843fd5e","observation_id":"d814cd33-f1f7-40e9-a583-81e4d4b5e092","resolution":{"observed_at":"2026-08-09T22:05:02.612675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.526228Z","title":"Analyzing leakage of personally identifiable information in language models","venue":null,"work_id":"eccee2cb-327f-4523-b5cc-2bcaf1a97ae7","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.615787Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:a13db277dce23cc43796dbfebcd77ed50735821e0bfb5927b8eeff5c7f2dd24f","observation_id":"3c33765d-4565-45cf-b9da-55ed2a7fbd24","resolution":{"observed_at":"2026-08-09T22:05:15.529460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.06162","last_updated":"2018-12-14T20:49:09Z","snapshot_observed_at":"2026-07-06T07:21:19.842962Z","submitted_at":"2018-12-14T20:49:09Z","title":"An Empirical Model of Large-Batch Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.06162","snapshot_observed_at":"2026-08-09T22:05:02.618642Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.618642Z"},"links":{"cited_paper":"/paper/1812.06162","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:dbac01416aa2a85068b797c768fb19f55a5f293f0d06c31bb3b27f34b969b349","observation_id":"e409d138-deec-47c0-ab74-44f5c6817c87","resolution":{"observed_at":"2026-08-09T22:05:02.618642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.517555Z","title":"Updating quasi- N ewton matrices with limited storage","venue":null,"work_id":"7a1f94cc-091f-4b57-836c-d16fbc76a648","year":1980},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.621389Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:b1c4f23da1fe646b0ded9d59c11474c2f1645a15fc84f842ccb3d890863be1fd","observation_id":"0d005d45-91d4-4feb-8b38-a8ecedfa403b","resolution":{"observed_at":"2026-08-09T22:05:15.520704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.623885Z","title":"and Wright, S","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.623885Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:5607bdd898601185b3fb910006c4dd3fa0e370ca9e1fbfc19c7e04ad41e90a40","observation_id":"23fd9076-2f1c-4476-a358-4c62dc875249","resolution":{"observed_at":"2026-08-09T22:05:02.623885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.504407Z","title":"B., Vassilvitskii, S., Chien, S., and Thakurta, A","venue":null,"work_id":"d922c431-2059-42b6-ac47-f86dd683ee59","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.626275Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:d5776999c87c1759e1e429a2caf7b4a32a0d338e37eedee6d36bdd641fb00f73","observation_id":"1267a439-115e-465a-82ed-fb596b3bfa8a","resolution":{"observed_at":"2026-08-09T22:05:15.507387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17746","last_updated":"2025-05-07T18:36:12Z","snapshot_observed_at":"2026-07-06T18:36:51.761148Z","submitted_at":"2024-06-25T17:32:16Z","title":"Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17746","snapshot_observed_at":"2026-08-09T22:05:02.628551Z","title":"S., Deng, A., O'Brien, K., SV, J., Khan, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.628551Z"},"links":{"cited_paper":"/paper/2406.17746","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:f44f58cdd44127daf328b800c7aecef894f1fac4ee7eabfe8c7939ed85cdc037","observation_id":"3a068439-a5ae-4f84-beb5-c6c501f4d11d","resolution":{"observed_at":"2026-08-09T22:05:02.628551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.496748Z","title":"K., Charles, Z., Garrett, Z., Augenstein, S., and Mitchell, N","venue":null,"work_id":"6710414e-ec24-4fe8-8117-b3aeb93ceef1","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.631380Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:08535a7d065d793bfecdde31fae53a74211849fd50a8d9819029ee2916bcdf09","observation_id":"328571d9-525e-4b7b-9ea7-457250121db5","resolution":{"observed_at":"2026-08-09T22:05:15.499398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.489513Z","title":"TAN without a burn: Scaling laws of DP-SGD","venue":null,"work_id":"68206d91-73c2-4991-b12c-1b84947acc6f","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.633800Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:20910dd6b2e086a889843152c28b48206cd9660552098f337643a13c598e3f46","observation_id":"6464e725-0401-46ea-8f17-25a1de3c0133","resolution":{"observed_at":"2026-08-09T22:05:15.492205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.482142Z","title":"Differentially private representation learning via image captioning","venue":null,"work_id":"a2ab899f-4bb0-4f49-80cf-982f2396bab5","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.636162Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:76c5bd7fe44921250c195677743cf9233ae60a41315b0eed8b8830b3957ae282","observation_id":"d0cfe463-e430-4f28-90a4-893e72b6e568","resolution":{"observed_at":"2026-08-09T22:05:15.484688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.474455Z","title":"J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G","venue":null,"work_id":"64ec02d5-4028-4f76-a222-dcfdd6a0ecc6","year":2019},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.639070Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:486ad2c04b84a4d174b0ad974e8301d414878baa04c986eeb33d386a72e2bc40","observation_id":"3008b1f9-25be-4af8-bd40-f9250f46eccd","resolution":{"observed_at":"2026-08-09T22:05:15.477492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.466184Z","title":"M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P","venue":null,"work_id":"4d1d00de-4daf-4818-82e0-bb6700a5d160","year":2020},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.642090Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:60d5bb731339da7f082298e04eb55c5108a0724e0ed5c2dae5e45f75286ce4d2","observation_id":"1384ed1c-bed2-48aa-9f17-5136093606e1","resolution":{"observed_at":"2026-08-09T22:05:15.469264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.456644Z","title":"Enabling fast differentially private SGD via just-in-time compilation and vectorization","venue":null,"work_id":"b1db21d5-d6b1-4ecb-92a1-dae34df281a9","year":2021},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.644878Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:ab355c86bde93fb0fcbe5ee1645394439060874abed2a903109539ab0a81a92c","observation_id":"3ce99226-749d-4e4b-b4d1-a8f26831a0ce","resolution":{"observed_at":"2026-08-09T22:05:15.459922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.448350Z","title":"A., Manoel, A., Mireshghallah, F., Lin, Z., Gopi, S., Kulkarni, J., and Sim, R","venue":null,"work_id":"51a530fc-5221-49e2-9ea2-3a88ac42f725","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.647860Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:ca28b3f893c39d153ee0202155808eebf8fcbaa3c0321b1f82cc7b7700c33914","observation_id":"adb3c8f8-4dc9-48b7-a369-33646690c82d","resolution":{"observed_at":"2026-08-09T22:05:15.451500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15551","last_updated":"2025-09-08T18:47:35Z","snapshot_observed_at":"2026-08-06T08:12:28.242579Z","submitted_at":"2023-12-24T21:46:14Z","title":"On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift","version":5},"cited_work":{"arxiv_id":"2312.15551","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.15551","snapshot_observed_at":"2026-08-09T22:05:15.186049Z","title":"On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift","venue":"cs.LG","work_id":"46b28c39-5883-4384-bd2f-3abfed331a58","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.650727Z"},"links":{"cited_paper":"/paper/2312.15551","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:6ce8c1e692e4d6c9a1ebe51aa1862facd2b9198ef7bc2c51bafe2540f8fe4a24","observation_id":"f48ce78d-8dc2-4d2d-b154-1fe5bc47d4c0","resolution":{"observed_at":"2026-08-09T22:05:15.190444Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.439758Z","title":"E., and Honkela, A","venue":null,"work_id":"9f70c6ab-11ee-4d65-a48d-0eb354a8c8be","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.653994Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:fbe2df1ee3269a5ba473dd08df6fc6e45d04e122f81f42e91196d27ab436cab2","observation_id":"8d59107f-16a4-483d-a043-de32d8e405f4","resolution":{"observed_at":"2026-08-09T22:05:15.442738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06470","last_updated":"2024-07-17T06:53:58Z","snapshot_observed_at":"2026-07-06T14:29:57.303212Z","submitted_at":"2022-12-13T10:41:12Z","title":"Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06470","snapshot_observed_at":"2026-08-09T22:05:02.656929Z","title":"Considerations for differentially private learning with large-scale public pretraining","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.656929Z"},"links":{"cited_paper":"/paper/2212.06470","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:568e2af24ae59c6028ef97b043dc8f6d635ca1cd26558fa5bc842e2bf2baba6e","observation_id":"3e6b039f-e0e9-4307-8682-b74301c7a1d4","resolution":{"observed_at":"2026-08-09T22:05:02.656929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.431057Z","title":"Can public large language models help private cross-device federated learning? In NAACL (Findings), pp.\\ 934--949, 2024","venue":null,"work_id":"e6f7eddb-adc4-4f00-8e0f-28315e97efd0","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.660157Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:93e98970f585d52c901a858b3fededee2514bf5600036bce3730b95adc5bb327","observation_id":"4de23078-6895-4f6e-8bdd-7e7ede5bdd97","resolution":{"observed_at":"2026-08-09T22:05:15.434369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.422460Z","title":"A., Backurs, A., Chandrasekaran, V., Kulkarni, J., and Sim, R","venue":null,"work_id":"89f2044e-4c4b-46f3-a4ce-75afb1bd7004","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.662921Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:4c0f88e5c68f650d35222c08ef7060ba08363eface0f31bbf83451b36ea2e060","observation_id":"fcc6f820-d944-42ff-8df4-a20d53e86eeb","resolution":{"observed_at":"2026-08-09T22:05:15.425696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.414013Z","title":"T., and Mittal, P","venue":null,"work_id":"b0ceed5e-b782-4d99-9d60-0b171866cb4e","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.665791Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:f858e1106e262f2d260082e6905b51e56cf700d00e4f84dc2304cb1188ca9279","observation_id":"1456e916-fcb1-4490-8c84-c2353aaf0761","resolution":{"observed_at":"2026-08-09T22:05:15.417064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04663","last_updated":"2021-12-23T21:29:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-05-10T20:54:58Z","title":"GSPMD: General and Scalable Parallelization for ML Computation Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04663","snapshot_observed_at":"2026-08-09T22:05:02.668521Z","title":"GSPMD : general and scalable parallelization for ML computation graphs","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.668521Z"},"links":{"cited_paper":"/paper/2105.04663","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:20fbed2ec609894ca1c6dda31ce5f3699e16eb39e0f625dbdcb287ee180a0d52","observation_id":"785a2d0d-66e1-4d3c-9ee6-5d663f5d3811","resolution":{"observed_at":"2026-08-09T22:05:02.668521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.405091Z","title":"Privacy risk in machine learning: Analyzing the connection to overfitting","venue":null,"work_id":"67b85b4e-8caa-46a6-8ab1-72b5af3847c7","year":2018},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.671533Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:e9ab91571a634ec582b265ae6601c3524f2ebfd16a9e8caf67e2342140eb920c","observation_id":"acad16a9-4871-4dad-ae95-9e512b23cf0e","resolution":{"observed_at":"2026-08-09T22:05:15.408488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.00962","last_updated":"2020-01-03T06:53:00Z","snapshot_observed_at":"2026-07-06T07:43:06.427641Z","submitted_at":"2019-04-01T16:53:35Z","title":"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.00962","snapshot_observed_at":"2026-08-09T22:05:02.674730Z","title":"Large batch optimization for deep learning: Training bert in 76 minutes, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.674730Z"},"links":{"cited_paper":"/paper/1904.00962","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:5e63c35278fd6a2b479bc623179c9f976e2f75bb18223a9181013c42ab7fe893","observation_id":"ae6a4999-2331-4545-b12d-2f53c6e34b32","resolution":{"observed_at":"2026-08-09T22:05:02.674730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.395763Z","title":"Large scale private learning via low-rank reparametrization","venue":null,"work_id":"770dcb9b-169e-4284-9544-20138f016171","year":2021},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.678274Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:93ee59aaf73ff4118babee953651e9c279e1e7e79368c03ddcad5b879af9c59c","observation_id":"9bda54f3-4a31-461f-9aab-46c8fabe2c16","resolution":{"observed_at":"2026-08-09T22:05:15.399002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.386946Z","title":"A., Kamath, G., Kulkarni, J., Lee, Y","venue":null,"work_id":"01c146cc-7047-467c-973b-b67116b51534","year":2022},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.681016Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:9a36d79531bdb1b966c5d978beff4f7d659bbebc7b1e7d6e8acc3fdd732f246a","observation_id":"e1567c43-320b-452b-afee-0783d63cbdaa","resolution":{"observed_at":"2026-08-09T22:05:15.390083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21676","last_updated":"2025-04-21T04:19:56Z","snapshot_observed_at":"2026-07-06T19:41:15.267950Z","submitted_at":"2024-10-29T02:54:06Z","title":"How Does Critical Batch Size Scale in Pre-training?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21676","snapshot_observed_at":"2026-08-09T22:05:02.683938Z","title":"How does critical batch size scale in pre-training? arXiv:2410.21676, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.683938Z"},"links":{"cited_paper":"/paper/2410.21676","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:bbae775ca83558c8b2e7bde34a7756e5418dfc4ae23fedd3e9cda3cfd5eb90ad","observation_id":"57bdad84-9478-4da6-a432-c3db2ea83848","resolution":{"observed_at":"2026-08-09T22:05:02.683938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.378061Z","title":"K., Oh, S., and He, N","venue":null,"work_id":"bc1b529b-a99e-41d8-9d5b-9e9768b9ac28","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.687014Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:ea41874b0bffeaf856b69df8f7595588846342bd29d56de74c8c00e8359f4d52","observation_id":"5b729103-da47-480e-b2cd-3dd4e3d335e0","resolution":{"observed_at":"2026-08-09T22:05:15.381145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.14632","last_updated":"2024-04-17T04:16:15Z","snapshot_observed_at":"2026-07-06T16:52:00.070739Z","submitted_at":"2023-11-24T17:56:44Z","title":"Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach","version":2},"cited_work":{"arxiv_id":"2311.14632","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.14632","snapshot_observed_at":"2026-08-09T22:05:15.141428Z","title":"Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach","venue":"cs.LG","work_id":"25453eea-a3d9-4860-9c37-4eb9e4cb24c7","year":2023},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.689970Z"},"links":{"cited_paper":"/paper/2311.14632","citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:c517114a8d0fde12a018c3088be18298b0712b5e1053975b15f9f3c1c2d7c5de","observation_id":"abae21ff-797f-45b8-98fd-2434ea18dcb5","resolution":{"observed_at":"2026-08-09T22:05:15.146944Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.369862Z","title":"S., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S","venue":null,"work_id":"7d92e224-a7ff-45b7-9cc3-f7bc2731c179","year":2015},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.693008Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:ee67ef29636a89fcb7d6dad7daed2b0bc17e55393f8aac1258a7a290568971ed","observation_id":"44fbaa5d-8587-44e2-bccd-921f83b75673","resolution":{"observed_at":"2026-08-09T22:05:15.372689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:15.361949Z","title":"T., Stieger, S., Feiner, L","venue":null,"work_id":"6ac70c19-a0cb-49c1-ba02-70d78aafce89","year":2024},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.695877Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:44d0fbef5008957ce0ad3d2fd24e0e23df7e635f3400ad1a09ce581b06924978","observation_id":"c0cf2b95-fa0a-4822-aff9-116a852b705b","resolution":{"observed_at":"2026-08-09T22:05:15.364570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.698766Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.698766Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:ac282478c8e09f28c8dab954e45ab1a9af84a532b92eb66a72302d6d3e624661","observation_id":"b3df63b0-23a1-4706-8995-da539e38e24a","resolution":{"observed_at":"2026-08-09T22:05:02.698766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.702159Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.702159Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:e9dd15e470d615a385442317dcbab04b8ce73bd40be63ec46efed6dc02987ec5","observation_id":"e0080499-9f74-4ca0-b91a-d6e54c28abda","resolution":{"observed_at":"2026-08-09T22:05:02.702159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:05:02.705232Z","title":"bG g6b嗍 3kQI @k /h m?hlKJڅ:| 4 j 2M^ ; Z ݄ hT2 !; & ȯ ɾD :] q u ` bcߩ -@n- e5 h v Vb?SHP r! 5 ШEw7wlQ # `K","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-09T22:05:02.705232Z"},"links":{"citing_paper":"/paper/2501.18914"},"observation_digest":"sha256:a72fcf91cd93aaa86162c2b798dc9a35c63e2609bb68677af52692c9448e1289","observation_id":"dd26519d-dd4f-4072-a1eb-9650c2524066","resolution":{"observed_at":"2026-08-09T22:05:02.705232Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.18914","last_updated":"2025-01-31T06:32:46Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T09:04:20.044564Z","submitted_at":"2025-01-31T06:32:46Z","title":"Scaling Laws for Differentially Private Language Models"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":2,"verified_fuzzy":46},"total_outbound_references":83},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 3 inbound Pith citation observations for arXiv:2501.18914."}