{"as_of":"2026-08-08T05:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6adadc25b5c15553371474f2ce37dda86132ca05cbde483af41d58acb3154b0f","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:20:46.186692Z","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":"2026-05-23T17:08:12.542881Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":"2205.10162","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2205.10162 (2022)","venue":null,"work_id":"f1e37578-752b-42bd-bc02-7e3ae4ef09de","year":2022},"citing_paper":{"arxiv_id":"2411.11707","last_updated":"2026-04-23T04:42:47Z","snapshot_observed_at":"2026-07-06T19:52:03.121993Z","submitted_at":"2024-11-18T16:34:58Z","title":"Federated Co-tuning Framework for Large and Small Language Models","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-23T17:08:05.240432Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2411.11707"},"observation_digest":"sha256:646677dd6793a8b15ca7a800a82f22cf7b61a31bf7c2e97a2d29ba7b1b0a08e9","observation_id":"245ee8b5-da37-44c5-93b5-896ecec8852f","resolution":{"observed_at":"2026-05-23T17:08:12.544845Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-08-07T12:20:46.186692Z","title":"Fedadapter: Efficient federated learning for modern nlp,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24773","last_updated":"2025-08-20T08:08:03Z","snapshot_observed_at":"2026-08-07T17:34:11.678905Z","submitted_at":"2025-05-30T16:35:32Z","title":"AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:20:46.186692Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2505.24773"},"observation_digest":"sha256:d60ecf927f9b8c60541daa63c2b2138d61accd731e7b7b07d7f7b59cfc66255f","observation_id":"3cc42069-44e4-4dab-b979-d1cc4cae243a","resolution":{"observed_at":"2026-08-07T12:20:46.186692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-08-07T11:57:57.393881Z","title":"Fedadapter: Efficient federated learning for modern nlp,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01001","last_updated":"2025-06-01T13:13:20Z","snapshot_observed_at":"2026-08-07T11:50:58.394133Z","submitted_at":"2025-06-01T13:13:20Z","title":"FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:57:57.393881Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2506.01001"},"observation_digest":"sha256:31bb281beee7f37e691586306a90fecad861924a6c2ec89a82c87a842def0fe0","observation_id":"a491e300-2bd8-441a-bf3d-bb94fca54438","resolution":{"observed_at":"2026-08-07T11:57:57.393881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-08-07T10:20:41.314544Z","title":"Fedadapter: Efficient federated learning for modern nlp,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05977","last_updated":"2025-06-06T10:59:11Z","snapshot_observed_at":"2026-08-08T01:33:57.460864Z","submitted_at":"2025-06-06T10:59:11Z","title":"Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:20:41.314544Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2506.05977"},"observation_digest":"sha256:3dc4b13eee8032badc55ebe575853b06bb11719f007b2ab46b207e363d45c069","observation_id":"89a7bf9b-f8fd-4e85-8206-4f94cdac08d7","resolution":{"observed_at":"2026-08-07T10:20:41.314544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-08-06T23:43:10.734745Z","title":"Fedadapter: Efficient federated learning for modern nlp","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16600","last_updated":"2025-07-14T21:49:53Z","snapshot_observed_at":"2026-08-07T00:00:39.120705Z","submitted_at":"2025-06-19T21:02:19Z","title":"FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:43:10.734745Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2506.16600"},"observation_digest":"sha256:2b709f33fd083244f35e807b0b269df28d8470f58699a242f79c634da5a914ec","observation_id":"3717be6b-0776-42b3-b5ab-db9df47284fd","resolution":{"observed_at":"2026-08-06T23:43:10.734745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-08-06T19:20:55.575261Z","title":"arXiv preprint arXiv:2205.10162 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05852","last_updated":"2025-07-08T10:30:08Z","snapshot_observed_at":"2026-08-06T21:49:56.286996Z","submitted_at":"2025-07-08T10:30:08Z","title":"Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:20:55.575261Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2507.05852"},"observation_digest":"sha256:9e506b5a275823ebd901f1ca47d0b2f9fcde8000fa5a7e03b7c0eccdabfdef69","observation_id":"2075ca81-afd5-42e3-9c69-c53b37d86125","resolution":{"observed_at":"2026-08-06T19:20:55.575261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":"2205.10162","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2205.10162 (2022)","venue":null,"work_id":"f1e37578-752b-42bd-bc02-7e3ae4ef09de","year":2022},"citing_paper":{"arxiv_id":"2604.06297","last_updated":"2026-04-07T17:19:54Z","snapshot_observed_at":"2026-08-02T17:56:36.424408Z","submitted_at":"2026-04-07T17:19:54Z","title":"FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T19:04:41.807582Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2604.06297"},"observation_digest":"sha256:b49cc78e69d0ef5f1dc2a40b9815dce126c49196e24f774b84ca245e0f75c4e0","observation_id":"a916bef0-00f0-447a-8170-53aac933f0d1","resolution":{"observed_at":"2026-05-10T23:30:51.361532Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":"2205.10162","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2205.10162 (2022)","venue":null,"work_id":"f1e37578-752b-42bd-bc02-7e3ae4ef09de","year":2022},"citing_paper":{"arxiv_id":"2604.06819","last_updated":"2026-04-08T08:37:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-08T08:37:17Z","title":"Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-10T18:30:14.867025Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2604.06819"},"observation_digest":"sha256:fee392835ac22b83aa32bb1a0a06f7a1e652cf28045c95df509e678a487076dd","observation_id":"bf8ed1b3-d059-46ef-b8b2-17f2d8f33d52","resolution":{"observed_at":"2026-05-11T00:30:51.455670Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP","version":2},"cited_work":{"arxiv_id":"2205.10162","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.10162","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2205.10162 (2022)","venue":null,"work_id":"f1e37578-752b-42bd-bc02-7e3ae4ef09de","year":2022},"citing_paper":{"arxiv_id":"2604.19015","last_updated":"2026-04-21T03:06:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-21T03:06:24Z","title":"FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-10T02:35:40.593397Z"},"links":{"cited_paper":"/paper/2205.10162","citing_paper":"/paper/2604.19015"},"observation_digest":"sha256:4d3e785e5f865f3437639ee92d893162a7a9f6008264429bd2a2a2a2938128c7","observation_id":"ef28c61d-301b-4861-bbe1-759b72c0a03e","resolution":{"observed_at":"2026-05-11T12:56:06.048457Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2205.10162/citation-record","integrity":"/paper/2205.10162/integrity","json":"/paper/2205.10162/citation-record.json","paper":"/paper/2205.10162"},"outbound":[],"paper":{"arxiv_id":"2205.10162","last_updated":"2023-05-08T19:50:56Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T13:12:05.091944Z","submitted_at":"2022-05-20T13:10:43Z","title":"FedAdapter: Efficient Federated Learning for Modern NLP"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2205.10162."}