{"as_of":"2026-08-10T13:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bf9c05ccbcb7dd6a4ad8e9768bae0be4faed5ea73966e1293dff7906fa490f31","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T07:33:07.725318Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2607.21417/citation-record","integrity":"/paper/2607.21417/integrity","json":"/paper/2607.21417/citation-record.json","paper":"/paper/2607.21417"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T07:33:03.993210Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:03.993210Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:3f0f9a189bd36e10a91545f96356c809e1a90d51bd0bb9060091783e4e734852","observation_id":"f04b6b89-6a88-4253-9c50-d48125eaf478","resolution":{"observed_at":"2026-08-01T07:33:03.993210Z","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":"2026-08-01T07:33:04.038962Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.038962Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:cb321383735a65d812e78203b4f6d78943c3eb5b26985a733bb2213e3ce7d616","observation_id":"78aca27a-545a-45c6-920a-515979f504d8","resolution":{"observed_at":"2026-08-01T07:33:04.038962Z","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":"2026-08-01T07:33:04.115387Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.115387Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:8ab4aaf3142a93fc70a32e3d36d367c26ea7377480c666cba31e4cb8d3b8e221","observation_id":"a02937db-d1bc-4820-8734-840794ae7800","resolution":{"observed_at":"2026-08-01T07:33:04.115387Z","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":"2026-08-01T07:33:04.173590Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.173590Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:7fa7f0dd98f0f28b38d9ae967b6712f1245bf026edbd9f740e815786da82c8db","observation_id":"547caf41-5b55-4840-9992-0a2663eb3fb7","resolution":{"observed_at":"2026-08-01T07:33:04.173590Z","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":"2026-08-01T07:33:04.257187Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.257187Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:65b7b1041b07ea80a076239c26121bf6ad2e6ac04f6ea80acfd46ffcb32d2465","observation_id":"d18e5115-28e0-43b5-95cb-f384ab01eb64","resolution":{"observed_at":"2026-08-01T07:33:04.257187Z","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":"2026-08-01T07:33:04.334687Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.334687Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:8bfbf347b1f2120c956c71d68ba11bdc82f434754aed5ce2243b13f483f97e75","observation_id":"2c5db5e5-1d39-4936-9c7c-b6627f182356","resolution":{"observed_at":"2026-08-01T07:33:04.334687Z","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":"2026-08-01T07:33:04.390535Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.390535Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:c9b7ae361fe5644c4a3a832e0ef44e3928cc58ebe615da02014d0bd760dae0c5","observation_id":"56b51cc7-4a9f-472d-8f9c-29547c7ebe1f","resolution":{"observed_at":"2026-08-01T07:33:04.390535Z","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":"2026-08-01T07:33:04.481548Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.481548Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:93c76e7e22cc0db637d17ae755ebe38e07efc05c1a7c156d857b78ecd45f57b2","observation_id":"071cf4e8-38ac-499a-aa89-fc099f409af3","resolution":{"observed_at":"2026-08-01T07:33:04.481548Z","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":"2026-08-01T07:33:04.531155Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.531155Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:6b684b11426377a1e71d225bcb6761a2a6935baca8685731f80a943970647668","observation_id":"23cdbb54-fca0-4d18-9714-285e223a7efc","resolution":{"observed_at":"2026-08-01T07:33:04.531155Z","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":"2026-08-01T07:33:04.605312Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.605312Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:84a113b3528f25a27faf3bcad240098dc8018d152667e8ac2bab748e9c97350b","observation_id":"6e54800c-558e-4fee-9944-d45a9b145cac","resolution":{"observed_at":"2026-08-01T07:33:04.605312Z","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":"2026-08-01T07:33:04.687258Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.687258Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:890883ffc9f17399aa8fe6004e67397cf3e997d107536c799e7bc89eb15d808f","observation_id":"f7823230-dde0-4d07-ac30-5494b4fc2b47","resolution":{"observed_at":"2026-08-01T07:33:04.687258Z","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":"2026-08-01T07:33:04.755185Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.755185Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:ede7503eacbdb34eada381bfbe8c36ae6329174f89caf5216767e8c7f0c3e403","observation_id":"d24b037e-64e6-4cca-86a2-b093e1481a77","resolution":{"observed_at":"2026-08-01T07:33:04.755185Z","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":"2026-08-01T07:33:04.817524Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.817524Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:6443a50332f606546127638fe7dae61bdd1d9e12b0b5b1153f69f67ddbbefec0","observation_id":"64bffbd7-c3a4-48e4-b648-3996ee21e2c3","resolution":{"observed_at":"2026-08-01T07:33:04.817524Z","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":"2026-08-01T07:33:04.905125Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.905125Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:2484efabe194a7763482c482603987f1452321f1a66e7061473d062dcf4e6b1d","observation_id":"5841e3be-a643-4011-8058-e14d5ad867d2","resolution":{"observed_at":"2026-08-01T07:33:04.905125Z","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":"2026-08-01T07:33:04.964954Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:04.964954Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:7f58bf337d016f703adbf81e63d4dff6144982beb526dc5d7acee4b49c615485","observation_id":"9c5a5225-e3f9-4e44-86e0-044d280c970c","resolution":{"observed_at":"2026-08-01T07:33:04.964954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22447","last_updated":"2025-05-28T15:09:56Z","snapshot_observed_at":"2026-08-09T16:54:23.434235Z","submitted_at":"2025-05-28T15:09:56Z","title":"Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22447","snapshot_observed_at":"2026-08-01T07:33:05.032359Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.032359Z"},"links":{"cited_paper":"/paper/2505.22447","citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:6c64b5f299dac408b4c8fdb004fb2b17cbd8cf502a8487de6bdc6f726aa57e23","observation_id":"92be24a2-b96f-4658-8b54-a7fc956fa0d0","resolution":{"observed_at":"2026-08-01T07:33:05.032359Z","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":"2026-08-01T07:33:05.099557Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.099557Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:f1e5c2e41e3938a769fd80cd6e63f9942dcbcfd28593ae727b8ef8b47054b851","observation_id":"6bc19a83-2993-44db-8d97-09053c4ea5a1","resolution":{"observed_at":"2026-08-01T07:33:05.099557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.06093","last_updated":"2023-08-25T14:30:45Z","snapshot_observed_at":"2026-08-10T04:19:52.774123Z","submitted_at":"2023-08-11T12:05:12Z","title":"Experts Weights Averaging: A New General Training Scheme for Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.06093","snapshot_observed_at":"2026-08-01T07:33:05.152405Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.152405Z"},"links":{"cited_paper":"/paper/2308.06093","citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:0cc7023e2ce9ce645701b518a564394872567ef96a5a19996bf7f774521c7e7a","observation_id":"16912c84-36d5-44bb-96f6-35527dfdbbde","resolution":{"observed_at":"2026-08-01T07:33:05.152405Z","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":"2026-08-01T07:33:05.234789Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.234789Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:03e435ccb564f4c548a6de9d835d874995ad0713cda15ca691d5b83f3162acd9","observation_id":"d8b42974-dc51-48e5-b703-c3c2fc4c250d","resolution":{"observed_at":"2026-08-01T07:33:05.234789Z","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":"2026-08-01T07:33:05.272800Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.272800Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:665544053890016d0cfcb4ac53b29203ef8c3d0bc968d1f991fb72934d031a7d","observation_id":"468a2a23-dae4-462c-917e-97065bdb845c","resolution":{"observed_at":"2026-08-01T07:33:05.272800Z","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":"2026-08-01T07:33:05.320557Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.320557Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:ed0abdd1cf718827aa7db7096675e4e377df44476bd8319628b0669533e9ab67","observation_id":"d275192e-8506-40f0-8912-648666222f3d","resolution":{"observed_at":"2026-08-01T07:33:05.320557Z","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":"2026-08-01T07:33:05.406093Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.406093Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:e78df329ef87146c2c969f8e026ce4f699726cef5139548337b8d681e178b15e","observation_id":"251a50f6-8f89-40c4-873b-5e781712f8ae","resolution":{"observed_at":"2026-08-01T07:33:05.406093Z","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":"2026-08-01T07:33:05.455047Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.455047Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:3ff50c2b236a5dd0c625b994a88804edb63030ee790e3cfb28b3689ad0c94329","observation_id":"37c36149-6bd0-4d80-bc51-4fc1a9ca8cd7","resolution":{"observed_at":"2026-08-01T07:33:05.455047Z","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":"2026-08-01T07:33:05.506729Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.506729Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:b7bf7adb57eb638b859cfa3bd75701fb2f5d117c6bcc7f739f6766047d46c926","observation_id":"dc8e6399-78bd-4436-9a9d-1b1c060e0935","resolution":{"observed_at":"2026-08-01T07:33:05.506729Z","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":"2026-08-01T07:33:05.554952Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.554952Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:ae719c7d541ca5153cb17c6577909ad95789e95daafd81e8cdac71c73737692a","observation_id":"a8ab002d-a105-40b7-9725-09a890e7ede3","resolution":{"observed_at":"2026-08-01T07:33:05.554952Z","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":"2026-08-01T07:33:05.594067Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.594067Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:bff6f3c82da45a5d17b8c814c7782eb8e65958b600164e9f1efc0a5c8b381bfe","observation_id":"3e7141a0-0049-4c18-847c-579fe5bc0a6c","resolution":{"observed_at":"2026-08-01T07:33:05.594067Z","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":"2026-08-01T07:33:05.659282Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.659282Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:93e977d7d437ddc210bed35b17059dca27df4e886ab3bd40fcc170b3bcebbdf4","observation_id":"b9976fe9-d035-4cc6-82da-1d2880f792d0","resolution":{"observed_at":"2026-08-01T07:33:05.659282Z","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":"2026-08-01T07:33:05.765252Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.765252Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:4e30031d2aa6bcba0c5bce71efe03200507639a1a4fdff761e77b4556762d487","observation_id":"ae03abde-9ae2-4f69-8cc4-c6b63d7c598c","resolution":{"observed_at":"2026-08-01T07:33:05.765252Z","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":"2026-08-01T07:33:05.835342Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.835342Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:513cded2f3baa3b950d87ecaf026fb86e78ec353ac48aa4ea7fe07fa7182cd76","observation_id":"b4fa25da-35d4-4056-8980-8a508fb3f5eb","resolution":{"observed_at":"2026-08-01T07:33:05.835342Z","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":"2026-08-01T07:33:06.067544Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.067544Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:36575eb4c7a76f9137cacc4fec6687b70b68de56048b7cfe7fb7932fb8b5de54","observation_id":"53fa4a52-31cd-4a32-aa89-264a7c02790c","resolution":{"observed_at":"2026-08-01T07:33:06.067544Z","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":"2026-08-01T07:33:06.179341Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.179341Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:e3616315fec2b651894f88b054bda9e7cdc0b3d317ee896aea23e3d76e96c3c5","observation_id":"93324b6c-5844-4956-a7f4-74ae50f04efa","resolution":{"observed_at":"2026-08-01T07:33:06.179341Z","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":"2026-08-01T07:33:06.334065Z","title":"Roy-Chowdhury, Srikanth V","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.334065Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:91a07a0b54da25d28e17bfd1a31349fe54cc4f641e4881689b2540984677e748","observation_id":"ca7bc967-b2cb-49a2-844e-1b071eb7bf3c","resolution":{"observed_at":"2026-08-01T07:33:06.334065Z","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":"2026-08-01T07:33:06.436138Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.436138Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:974e87c98b8f67bbc6f09fde32af5f4a6d3a58892e49002b6fb0ef719b250142","observation_id":"b50f17dd-78c9-4759-895b-599815093a04","resolution":{"observed_at":"2026-08-01T07:33:06.436138Z","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":"2026-08-01T07:33:06.574028Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.574028Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:3961ea53092044041487929f87d94ef81d142484828ad1350bbff89763f294df","observation_id":"fe3d8983-53a0-49d2-b891-a275c638491c","resolution":{"observed_at":"2026-08-01T07:33:06.574028Z","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":"2026-08-01T07:33:06.743102Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.743102Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:4b0f05187f245b9dab74194d5c455498d06fbef980b67ab9b8d8175c0e7d02eb","observation_id":"65917a67-f072-457c-9d25-edf52c78bb03","resolution":{"observed_at":"2026-08-01T07:33:06.743102Z","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":"2026-08-01T07:33:06.855320Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.855320Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:27660d9cc5277364fcc73d39c969bd8166c8f5f5d653ab881359e491f1bd34e6","observation_id":"76b2154f-149e-49da-9b85-21af48da8cf7","resolution":{"observed_at":"2026-08-01T07:33:06.855320Z","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":"2026-08-01T07:33:06.955421Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:06.955421Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:ccf865efb6f152fe01fc6432147c4b39f472e03eee2f4f55a6b5b4585569be72","observation_id":"9b4bc4c0-8fb6-48b6-a243-8f042dae1ccd","resolution":{"observed_at":"2026-08-01T07:33:06.955421Z","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":"2026-08-01T07:33:07.118578Z","title":"Vincent Poor","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.118578Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:5250d235d96fc97327fbc8c9326ca299b5d9d1235ae7cfb4cddbfe37d101aaf1","observation_id":"2f945a10-1b67-49da-8cc1-9355212249a4","resolution":{"observed_at":"2026-08-01T07:33:07.118578Z","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":"2026-08-01T07:33:07.217381Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.217381Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:db11171c483fa736bd0a4de0f8673ae8b97d1cd23de677f1f3e14a8b198232f9","observation_id":"3adf87bc-4e61-423e-a1b9-5a3a8f2e866a","resolution":{"observed_at":"2026-08-01T07:33:07.217381Z","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":"2026-08-01T07:33:07.376920Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.376920Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:e10899c3437ebc448180328f63452c2ab8e16fc0902abadbc92266990175d3ea","observation_id":"e6d88fb3-0a3a-4b65-a2b8-9d1047a149d9","resolution":{"observed_at":"2026-08-01T07:33:07.376920Z","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":"2026-08-01T07:33:07.391302Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.391302Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:696125db88ac4eda5ff28415deab45d29e1eda49f3b1ed1316788ddb03a0970b","observation_id":"9913b131-e342-41a9-87bd-d64890533833","resolution":{"observed_at":"2026-08-01T07:33:07.391302Z","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":"2026-08-01T07:33:07.418337Z","title":"Yuen, and Dacheng Tao","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.418337Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:a8401b6aeefaf24acc6d1d20958477aa855a46921020fba4bb519a2b3208bf71","observation_id":"5138d27d-9927-4ae1-835d-ebcc91747e1a","resolution":{"observed_at":"2026-08-01T07:33:07.418337Z","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":"2026-08-01T07:33:07.531120Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.531120Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:fe95e27546dd7e412b9150ae8703873f4f1a8cc3f3f2a443c80c431994266f04","observation_id":"f67b7302-0f11-4b05-ae77-53cb7771a596","resolution":{"observed_at":"2026-08-01T07:33:07.531120Z","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":"2026-08-01T07:33:07.674325Z","title":"Rajapakse","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.674325Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:3adc08d28cb73131028a70a123800d41a0627e8795a2a80d6c454d51aa4bbdcc","observation_id":"48a1bf1c-e7a1-4c93-a888-e073465895b2","resolution":{"observed_at":"2026-08-01T07:33:07.674325Z","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":"2026-08-01T07:33:07.684816Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.684816Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:5e4a2a2774b555ff8ddc1298ac22d5ec49285f02bf82c9a8450cc5c4d5c6e809","observation_id":"8f53d218-36da-4529-9f42-611829a1c664","resolution":{"observed_at":"2026-08-01T07:33:07.684816Z","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":"2026-08-01T07:33:07.712439Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.712439Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:c972891d7baa48e4b560d866e4fd5aa04c58a0fe0d4c64a881b3ee32ea92c922","observation_id":"0b240b56-1e71-4122-a0bd-35179880d71a","resolution":{"observed_at":"2026-08-01T07:33:07.712439Z","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":"2026-08-01T07:33:07.719045Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.719045Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:7026cd8d51df584886e9c0b20278dab04dfd7106841fb456b4a31caf5699dcaf","observation_id":"30b7fee1-f45a-42ca-922f-f9eec248bed9","resolution":{"observed_at":"2026-08-01T07:33:07.719045Z","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":"2026-08-01T07:33:07.725318Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:07.725318Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:f3bc2d54226edad7797a24359a6b257cc2e1ff7508cbab8d0d0dc0720e154754","observation_id":"dc11d1e3-c6f5-4433-92b6-631f8949b08d","resolution":{"observed_at":"2026-08-01T07:33:07.725318Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T07:33:05.949597Z","title":"InProceedings of the IEEE/CVF international conference on computer vision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-01T07:33:05.949597Z"},"links":{"citing_paper":"/paper/2607.21417"},"observation_digest":"sha256:93db12e4972ca24b09f957c4a8338f66fb8a3ca98e557b023aae394f011ba2d7","observation_id":"6165e35d-7651-451a-a56b-79b22ed0b4a8","resolution":{"observed_at":"2026-08-01T07:33:05.949597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.21417","last_updated":"2026-07-23T15:22:48Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T01:16:32.139360Z","submitted_at":"2026-07-23T15:22:48Z","title":"Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":48,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":49},"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 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2607.21417."}