{"as_of":"2026-08-10T05:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f3cfc372031f2dc70e45cfe0a91c78c1b9cba0dc55ab2d953d796007867610f6","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:43:15.053074Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.22565/citation-record","integrity":"/paper/2507.22565/integrity","json":"/paper/2507.22565/citation-record.json","paper":"/paper/2507.22565"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:12.570822Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:12.570822Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:09d0d52eca19a053f68911134b0a4b8c66261712b61a498f62c01ccc238b7ab1","observation_id":"985350ad-256c-4aa8-8751-dc548b901d90","resolution":{"observed_at":"2026-08-06T11:43:12.570822Z","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":"2026-08-06T11:43:12.612617Z","title":"Deep learning with differential privacy","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:12.612617Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:730305b7d1b8c17662e83d4e95312ea4237701ab1e05e844fa138999e91340d6","observation_id":"8abdf2c0-ff0d-4328-b584-8b16e418028c","resolution":{"observed_at":"2026-08-06T11:43:12.612617Z","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":"2026-08-06T11:43:12.726662Z","title":"Differentially private learning with adaptive clipping","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:12.726662Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:5b9d9c92b3d4c3a0ff58f378e300794fd421239e4b12ff729e7be0f703dd88eb","observation_id":"c2ad7d1f-ad3d-4308-b436-93d57a34eef6","resolution":{"observed_at":"2026-08-06T11:43:12.726662Z","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":"2026-08-06T11:43:18.147981Z","title":"Automatic clipping: Differentially private deep learning made easier and stronger","venue":null,"work_id":"f9f9ef87-47f5-4f1c-bb4b-b76057e964d3","year":2023},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:12.800066Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:e646a147aa6e1332c72fef6d1f277ec816a021c2be1c0166e8ebe03bd3459564","observation_id":"7481d484-1591-426e-82c8-afeb2e7c19a9","resolution":{"observed_at":"2026-08-06T11:43:18.250651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:17.912110Z","title":"Membership inference attacks from first principles","venue":null,"work_id":"954bc27a-c252-4bf7-84ee-17e756becbb9","year":2022},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:12.862181Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:957bd4161ca7a92cc3ccb1212db7dc2e98a6c3b03e47a9636d3dc64b7198629e","observation_id":"3a0b9f5c-1b8c-47be-bf25-ec3c3eab7e36","resolution":{"observed_at":"2026-08-06T11:43:18.047519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-06T11:43:12.967584Z","title":"Albert q","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:12.967584Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:d01a472c3f389ed735843d2e3d855ab496e5e6ee793529b894eab575e16bbde6","observation_id":"ce092f6c-9284-4359-b047-a71a13aa548c","resolution":{"observed_at":"2026-08-06T11:43:12.967584Z","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":"2026-08-06T11:43:17.617459Z","title":"Multi-step reinforcement learning: A unifying algorithm","venue":null,"work_id":"f77d474f-6846-4ccb-b4e7-f619ed960783","year":2018},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.067238Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:fa02f50aa9865e5a700679c07a907c6c1d5a0fe4b885a38196f32d255a170482","observation_id":"70ff7309-dae0-4229-b72b-eeddfdf19d6a","resolution":{"observed_at":"2026-08-06T11:43:17.746341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:17.406826Z","title":"Gaussian differential privacy","venue":null,"work_id":"db16e8cd-14d9-4e91-98bc-8207a3b49097","year":2022},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.147548Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:0e00a1895f92aab3835979ae70d64e046e89030f707b7d6af5271c54bebd77b1","observation_id":"a06448f1-1099-4dbb-9e67-8fd87682c41c","resolution":{"observed_at":"2026-08-06T11:43:17.488113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:13.296935Z","title":"The algorithmic foundations of differential privacy","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.296935Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:bc6c226bcf793bff22f0bcc42bc9487686bb36885c8916149732abb4e25938ee","observation_id":"e95ed40a-534f-4ff4-b1cf-721211ad5858","resolution":{"observed_at":"2026-08-06T11:43:13.296935Z","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":"2026-08-06T11:43:13.372434Z","title":"Geoclip: Geometry-aware clipping for differentially private sgd","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.372434Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:72d9332e928b049df605b9398de5ab2ef01bdf4547d605fb61b67fd11daa7808","observation_id":"b31b0c60-b8fd-4864-b3e9-c0e71576566b","resolution":{"observed_at":"2026-08-06T11:43:13.372434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T11:43:13.448370Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.448370Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:433d44805d54624f0f1a9422a79fb35d7e9bb15f5dd419e6e3766d76ed7a934d","observation_id":"821d5bc7-de37-45f2-90a9-d26c1435b8e7","resolution":{"observed_at":"2026-08-06T11:43:13.448370Z","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":"2026-08-06T11:43:13.554740Z","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.554740Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:3b6f5947ffadcaa9d52ad8a534919ff0dd4fa3e400b05ee09b2b926a10951510","observation_id":"387ec0f5-b0c5-4e01-a474-b56639255daf","resolution":{"observed_at":"2026-08-06T11:43:13.554740Z","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":"2026-08-06T11:43:13.646796Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.646796Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:9a6bb00a88ba64a786860480a7d4ca1937a72d47231ed9d399ded498f3fe8dfd","observation_id":"7eb5c481-ebe8-4050-9e28-48272d85294a","resolution":{"observed_at":"2026-08-06T11:43:13.646796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.05679","last_updated":"2022-11-10T18:42:34Z","snapshot_observed_at":"2026-08-08T11:17:29.711698Z","submitted_at":"2021-10-12T01:45:27Z","title":"Large Language Models Can Be Strong Differentially Private Learners","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.05679","snapshot_observed_at":"2026-08-06T11:43:13.733634Z","title":"Large language models can be strong differentially private learners","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.733634Z"},"links":{"cited_paper":"/paper/2110.05679","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:f4a71af8ede5b70caf8d934766a79254ffaf99c2dbcdeba443f4a928354fa618","observation_id":"8bca0c17-51cb-458a-9840-25a16c71a938","resolution":{"observed_at":"2026-08-06T11:43:13.733634Z","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":"2026-08-06T11:43:17.068515Z","title":"Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach","venue":null,"work_id":"dddaf680-30ba-47bf-bbaf-41be57855a41","year":2023},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.811261Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:d4d013d4683137abbdf7f8a42641167f8be1825adf524057f337bda858bda7d4","observation_id":"010f1573-043d-4ca0-bf7c-7d0bb339cff2","resolution":{"observed_at":"2026-08-06T11:43:17.202689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:16.807430Z","title":"Differentially private low-rank adaptation of large language model using federated learning","venue":null,"work_id":"02d2ae76-85ba-4d83-a743-17ad9ba320cf","year":2025},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:13.962028Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:8b9981f4c6d571a4fded467babc7c57affc11ee106ba7f9fdbcd98a3a0081437","observation_id":"bc7500e7-d9b0-457f-bb50-8b587a07398e","resolution":{"observed_at":"2026-08-06T11:43:16.921676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:14.074621Z","title":"R \\'e nyi differential privacy","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.074621Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:896c333a10afb9a9a264909e94e8f6361b865984c8d26778837b94897c860ff4","observation_id":"c05df738-3665-4014-8263-831ce653ea69","resolution":{"observed_at":"2026-08-06T11:43:14.074621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.08908","last_updated":"2018-02-24T20:39:51Z","snapshot_observed_at":"2026-07-06T06:25:11.708379Z","submitted_at":"2018-02-24T20:39:51Z","title":"Scalable Private Learning with PATE","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.08908","snapshot_observed_at":"2026-08-06T11:43:14.189203Z","title":"Scalable private learning with pate","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.189203Z"},"links":{"cited_paper":"/paper/1802.08908","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:e7eb40635ff803a484de4ea1c4e75232366a47aab8f3c0893bb62e54a61e5cab","observation_id":"165e00b2-3671-43e1-8755-3b688ac4e943","resolution":{"observed_at":"2026-08-06T11:43:14.189203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07643","last_updated":"2019-10-23T19:02:00Z","snapshot_observed_at":"2026-08-09T02:16:29.487739Z","submitted_at":"2019-08-20T23:19:21Z","title":"AdaCliP: Adaptive Clipping for Private SGD","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07643","snapshot_observed_at":"2026-08-06T11:43:14.323680Z","title":"Adaclip: Adaptive clipping for private sgd","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.323680Z"},"links":{"cited_paper":"/paper/1908.07643","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:20a6e3fc7b89c574fd9ce74e10fd303a02b2102f0c5d4e6ae6b0482a66fc235f","observation_id":"69586a21-e7e8-4143-8853-2aa87e115a5b","resolution":{"observed_at":"2026-08-06T11:43:14.323680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.03888","last_updated":"2021-08-09T09:18:22Z","snapshot_observed_at":"2026-07-06T11:36:37.444791Z","submitted_at":"2021-08-09T09:18:22Z","title":"Efficient Hyperparameter Optimization for Differentially Private Deep Learning","version":1},"cited_work":{"arxiv_id":"2108.03888","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.03888","snapshot_observed_at":"2026-08-06T11:43:15.230327Z","title":"Efficient Hyperparameter Optimization for Differentially Private Deep Learning","venue":"cs.LG","work_id":"c8b8b25f-5f58-4c18-b137-68c21aaf98dd","year":2021},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.436089Z"},"links":{"cited_paper":"/paper/2108.03888","citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:6b5db3aa65522a662a0f1f241f07fe8e8f6567ba2a66b749e0cd7e4ee17adc17","observation_id":"4455fa88-68da-4aa4-874a-a79564985957","resolution":{"observed_at":"2026-08-06T11:43:15.351648Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:14.600661Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.600661Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:f63dd51054064dbab01e6dc7f12a657e67a23e315d2ec6fb25310043dbd56bc3","observation_id":"6978fb2f-690e-4daf-a197-fc808abe7abd","resolution":{"observed_at":"2026-08-06T11:43:14.600661Z","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":"2026-08-06T11:43:16.491459Z","title":"Dc-sgd: Differentially private sgd with dynamic clipping through gradient norm distribution estimation","venue":null,"work_id":"3ccad9cc-e3d0-4879-b9cd-e5f796df9dc0","year":2025},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.743165Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:54f13006836c5b05283116c8c39c49d0556d85aa6b757dba4dc694616e154530","observation_id":"aaedad78-ee72-428c-b9de-c52db8c6ffd9","resolution":{"observed_at":"2026-08-06T11:43:16.642301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:16.262232Z","title":"Differentially private learning with per-sample adaptive clipping","venue":null,"work_id":"facfd89a-b993-4a37-ac12-ace5585e9f4a","year":2023},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.852777Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:683aaabb306c28c678067ba09965dc12ba9dda94248df1b388d0008b8ac0dfea","observation_id":"bbb0f929-ebb7-45dc-b1cb-379a1eb11940","resolution":{"observed_at":"2026-08-06T11:43:16.374582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:16.029758Z","title":"A concurrent federated reinforcement learning for iot resources allocation with local differential privacy","venue":null,"work_id":"fefcd67c-9c9b-4d1c-b2aa-1a6d14944573","year":2023},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:14.931026Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:1d2f69e347da552bccdb71954badbae3fa8e094f192f8d392d0c3dad59596898","observation_id":"504d02dd-1eea-46d4-9f44-8abe8802c857","resolution":{"observed_at":"2026-08-06T11:43:16.137920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:43:15.780729Z","title":"Poission subsampled r \\'e nyi differential privacy","venue":null,"work_id":"60ba6ada-c4d0-4b66-8574-2ea436b3adb8","year":2019},"citing_paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T11:43:15.053074Z"},"links":{"citing_paper":"/paper/2507.22565"},"observation_digest":"sha256:e8c57d111515a0ce0f9cad96bcb9314a81e7ab6b7238690857219a692624e1b1","observation_id":"3aae371a-4be4-4f1a-a4ef-c866171c9f65","resolution":{"observed_at":"2026-08-06T11:43:15.871149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.22565","last_updated":"2025-07-30T10:46:53Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T14:48:33.237104Z","submitted_at":"2025-07-30T10:46:53Z","title":"Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":10},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.22565."}