{"as_of":"2026-08-14T18:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e93e51d37b7cf5d0b3ab9740e14797d71e1cb160c50c59ae954ed506b741a1ea","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:53:06.379998Z","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":"2026-05-22T13:54:52.907275Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-12T18:53:06.379998Z","title":"Fedtune: A deep dive into ef- ficient federated fine-tuning with pre-trained transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11912","last_updated":"2025-03-30T10:30:03Z","snapshot_observed_at":"2026-08-12T18:47:12.448998Z","submitted_at":"2024-11-17T21:54:57Z","title":"F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:53:06.379998Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2411.11912"},"observation_digest":"sha256:2951a2629110c8d00a44db31c4a2a6f0e6fac2a437cdd793dcd1c9f36c00d672","observation_id":"7b60971e-eed2-490f-ade7-c05fe7a0394e","resolution":{"observed_at":"2026-08-12T18:53:06.379998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-12T13:29:09.197619Z","title":"Fedtune: A deep dive into efficient federated fine-tuning with pre-trained transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16796","last_updated":"2025-08-25T16:33:35Z","snapshot_observed_at":"2026-08-14T08:54:49.160328Z","submitted_at":"2024-11-25T09:58:51Z","title":"HeteroTune: Efficient Federated Learning for Large Heterogeneous Models","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-12T13:29:09.197619Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2411.16796"},"observation_digest":"sha256:9ba7742165a83e9575f2e962beb2332955c08ab40bfe20c6e11691df5d0a5477","observation_id":"b385b422-3f71-4c8b-993a-dac94b1ba29d","resolution":{"observed_at":"2026-08-12T13:29:09.197619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-11T12:20:30.280526Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.14424","last_updated":"2024-12-19T00:24:00Z","snapshot_observed_at":"2026-08-13T16:46:31.834754Z","submitted_at":"2024-12-19T00:24:00Z","title":"FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T12:20:30.280526Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2412.14424"},"observation_digest":"sha256:3197b1afa045b0ca43f6e5f73377a84db515a4cb3898a8af0c24f0fb15906e29","observation_id":"ff6cd8b9-8433-4500-8d30-89f3b55f0fd2","resolution":{"observed_at":"2026-08-11T12:20:30.280526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-10T21:57:15.833799Z","title":"Fedtune: A deep dive into efficient federated fine-tuning with pre-trained transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.03223","last_updated":"2025-01-06T18:57:18Z","snapshot_observed_at":"2026-08-12T02:35:48.230870Z","submitted_at":"2025-01-06T18:57:18Z","title":"Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:15.833799Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2501.03223"},"observation_digest":"sha256:cc2b64c90f710a532b64ad35cd0701f7f3a6a82af8861506d586129512fb0e6c","observation_id":"7aa7c0e3-f89b-467c-bffd-b6ad71e14d3b","resolution":{"observed_at":"2026-08-10T21:57:15.833799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-10T18:03:48.481932Z","title":"Fedtune: A deep dive into efficient federated fine-tuning with pre-trained transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.11706","last_updated":"2025-01-20T19:38:50Z","snapshot_observed_at":"2026-08-14T11:32:36.472905Z","submitted_at":"2025-01-20T19:38:50Z","title":"Trustformer: A Trusted Federated Transformer","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T18:03:48.481932Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2501.11706"},"observation_digest":"sha256:473ec1ced66c1081428b42fe22c3103035c188393a695eb03f69617d937a55fb","observation_id":"5aff27dd-f850-4e17-b8d7-81b744396350","resolution":{"observed_at":"2026-08-10T18:03:48.481932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":"2211.08025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fedtune: A deep dive into efficient federated fine-tuning with pre-trained transformers","venue":null,"work_id":"72a0eac4-c5ab-4e07-8010-139477e560f8","year":2022},"citing_paper":{"arxiv_id":"2505.12318","last_updated":"2026-04-09T20:36:53Z","snapshot_observed_at":"2026-08-02T11:54:02.140047Z","submitted_at":"2025-05-18T09:19:13Z","title":"Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-22T13:54:38.393967Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2505.12318"},"observation_digest":"sha256:e79964ca187f06f95c528468b4d3e45e8f498a488dd4266bcd8e6ef7198b0de3","observation_id":"6da4d65d-5c10-43bf-9185-a8527996d4d1","resolution":{"observed_at":"2026-05-22T13:54:52.910543Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-07T12:05:54.371195Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00743","last_updated":"2025-05-31T23:09:26Z","snapshot_observed_at":"2026-08-12T23:47:23.024651Z","submitted_at":"2025-05-31T23:09:26Z","title":"Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:05:54.371195Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2506.00743"},"observation_digest":"sha256:570cd1ca10943ebe0c3afac92170967715e941e0569572498fec6a1f556746ae","observation_id":"a5bcb026-c3c3-4c70-950c-75691a8f8a4e","resolution":{"observed_at":"2026-08-07T12:05:54.371195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.08025","snapshot_observed_at":"2026-08-07T11:57:25.007012Z","title":"Fedtune: A deep dive into efficient federated fine-tuning with pre-trained transformers.arXiv preprint arXiv:2211.08025, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01194","last_updated":"2025-06-01T22:07:00Z","snapshot_observed_at":"2026-08-13T18:56:13.691484Z","submitted_at":"2025-06-01T22:07:00Z","title":"FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:57:25.007012Z"},"links":{"cited_paper":"/paper/2211.08025","citing_paper":"/paper/2506.01194"},"observation_digest":"sha256:8d92460cc972ebbf3ff4fe8b42334eb461e5494eea3bbf0f319724337218f6ce","observation_id":"5198c946-8f49-44c9-b065-9309e5e477bf","resolution":{"observed_at":"2026-08-07T11:57:25.007012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2211.08025/citation-record","integrity":"/paper/2211.08025/integrity","json":"/paper/2211.08025/citation-record.json","paper":"/paper/2211.08025"},"outbound":[],"paper":{"arxiv_id":"2211.08025","last_updated":"2022-11-15T10:16:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T13:42:42.475732Z","submitted_at":"2022-11-15T10:16:13Z","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2211.08025."}