{"as_of":"2026-08-16T05:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3abb3e6f73c9e98b67cf67fd5536cd622176f5e3fceb093b0ea7c735c22c5597","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:50:24.765210Z","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":"2026-05-11T00:30:51.339210Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13894","snapshot_observed_at":"2026-08-10T23:46:33.054773Z","title":"Federated fine-tuning of billion- sized language models across mobile devices","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.20004","last_updated":"2024-12-28T04:00:42Z","snapshot_observed_at":"2026-08-15T11:35:11.237682Z","submitted_at":"2024-12-28T04:00:42Z","title":"Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T23:46:33.054773Z"},"links":{"cited_paper":"/paper/2308.13894","citing_paper":"/paper/2412.20004"},"observation_digest":"sha256:5e327e234bc3c01cf3b55acb8c9cc199e654286b350d8af44a4fcaa38dd5b839","observation_id":"f3d3e325-e6e6-4b33-a7f5-d09e03963968","resolution":{"observed_at":"2026-08-10T23:46:33.054773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13894","snapshot_observed_at":"2026-08-15T23:50:24.765210Z","title":"Fwdllm: Efficient fedllm using forward gradient,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03556","last_updated":"2025-05-06T14:09:29Z","snapshot_observed_at":"2026-08-15T23:46:30.763978Z","submitted_at":"2025-05-06T14:09:29Z","title":"A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges","version":1},"reference_index":162,"source":"pdf_text","source_observed_at":"2026-08-15T23:50:24.765210Z"},"links":{"cited_paper":"/paper/2308.13894","citing_paper":"/paper/2505.03556"},"observation_digest":"sha256:3bbe7bef784840769565b54bab6f0273adfe8c929458a2000d1550e0d23b532f","observation_id":"cc12c04e-8ea8-4f99-b7ad-e76867acc726","resolution":{"observed_at":"2026-08-15T23:50:24.765210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13894","snapshot_observed_at":"2026-08-07T13:13:56.844214Z","title":"Fwdllm: Efficient fedllm using forward gradient,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22311","last_updated":"2025-05-28T12:54:07Z","snapshot_observed_at":"2026-08-09T00:47:56.376478Z","submitted_at":"2025-05-28T12:54:07Z","title":"From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications","version":1},"reference_index":131,"source":"pdf_text","source_observed_at":"2026-08-07T13:13:56.844214Z"},"links":{"cited_paper":"/paper/2308.13894","citing_paper":"/paper/2505.22311"},"observation_digest":"sha256:49c730958f1f1cd3592279e8fa697cfd14a2051c938b74ef19a94ae8379ddfad","observation_id":"1ce4cb5f-150c-4d59-95f9-54b575deff5e","resolution":{"observed_at":"2026-08-07T13:13:56.844214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13894","snapshot_observed_at":"2026-08-15T18:41:26.324825Z","title":"Fwdllm: Efficient fedllm using forward gradient,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19164","last_updated":"2025-06-23T22:03:21Z","snapshot_observed_at":"2026-08-15T18:33:14.332943Z","submitted_at":"2025-06-23T22:03:21Z","title":"GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.324825Z"},"links":{"cited_paper":"/paper/2308.13894","citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:dc4027e51ab6fac8c98ae16c3c531f5a1b681de3e33d6abd0051e0047d1748ae","observation_id":"dd9255eb-86e3-48bb-b4ac-837cfcfbb678","resolution":{"observed_at":"2026-08-15T18:41:26.324825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13894","snapshot_observed_at":"2026-08-05T15:41:01.017935Z","title":"Federated fine-tuning of billion-sized language models across mobile devices,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19620","last_updated":"2025-08-27T06:57:50Z","snapshot_observed_at":"2026-08-09T23:20:53.908210Z","submitted_at":"2025-08-27T06:57:50Z","title":"A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions","version":1},"reference_index":176,"source":"pdf_text","source_observed_at":"2026-08-05T15:41:01.017935Z"},"links":{"cited_paper":"/paper/2308.13894","citing_paper":"/paper/2508.19620"},"observation_digest":"sha256:1144f2f4e6a2b373e6a14ad455ca081001f83421d9a6e4db36123651f70bbb1a","observation_id":"73ca33f4-6d75-44aa-99c9-5f5938615464","resolution":{"observed_at":"2026-08-05T15:41:01.017935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient","version":2},"cited_work":{"arxiv_id":"2308.13894","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.13894","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c3ac2de4-7008-48a8-bd03-7a481b49537b","year":2023},"citing_paper":{"arxiv_id":"2604.06819","last_updated":"2026-04-08T08:37:17Z","snapshot_observed_at":"2026-08-11T14:48:52.671741Z","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":50,"source":"arxiv_source","source_observed_at":"2026-05-10T18:30:14.867025Z"},"links":{"cited_paper":"/paper/2308.13894","citing_paper":"/paper/2604.06819"},"observation_digest":"sha256:a09643d2089616e4d5743effcac542e11b3805664375839fc36b6b7fc7ea4b8a","observation_id":"25ce2e6f-ac24-42a2-b0af-d6de53413890","resolution":{"observed_at":"2026-05-11T00:30:51.345021Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2308.13894/citation-record","integrity":"/paper/2308.13894/integrity","json":"/paper/2308.13894/citation-record.json","paper":"/paper/2308.13894"},"outbound":[],"paper":{"arxiv_id":"2308.13894","last_updated":"2024-01-20T09:24:33Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-13T10:27:10.526180Z","submitted_at":"2023-08-26T14:36:30Z","title":"FwdLLM: Efficient FedLLM using Forward Gradient"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2308.13894."}