{"as_of":"2026-08-08T01:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3e542ed84771de14b26222d7481fe8799f2ef2935a6b536c4250e556f13b9d95","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:57:24.925571Z","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":"2026-07-02T13:16:58.669038Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-08-07T11:57:24.925571Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources.arXiv preprint arXiv:2402.11505, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.01194","last_updated":"2025-06-01T22:07:00Z","snapshot_observed_at":"2026-08-07T11:47:50.794770Z","submitted_at":"2025-06-01T22:07:00Z","title":"FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:57:24.925571Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2506.01194"},"observation_digest":"sha256:2f1b96403105bb2121984b0d6a83c9b25d650792de529a5c7ca9a537b8f0153e","observation_id":"6f57010f-4df7-434a-bbc3-2893d74b5313","resolution":{"observed_at":"2026-08-07T11:57:24.925571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":"2402.11505","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-07-02T13:16:58.669038Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources","venue":null,"work_id":"4f973fbd-7fcc-4f1c-a056-f98e6059c467","year":2024},"citing_paper":{"arxiv_id":"2506.05640","last_updated":"2026-05-19T03:36:42Z","snapshot_observed_at":"2026-07-06T21:37:42.063760Z","submitted_at":"2025-06-06T00:05:05Z","title":"FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-22T01:43:44.406488Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2506.05640"},"observation_digest":"sha256:4a96cac61143cfd9166aa56c58a65d09f8f1dee630b63e0261b6a0ed94baba99","observation_id":"ae1ff209-c84f-459b-b12b-8d7d97bcbfec","resolution":{"observed_at":"2026-05-22T01:44:30.393366Z","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":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-08-06T14:49:56.588435Z","title":"Federated fine-tuning of large language models under heterogeneous language tasks and client resources,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13162","last_updated":"2025-07-23T15:38:14Z","snapshot_observed_at":"2026-08-06T14:43:01.793241Z","submitted_at":"2025-07-23T15:38:14Z","title":"FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T14:49:56.588435Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2508.13162"},"observation_digest":"sha256:f2abbc85d9b5b6f1e3eda17891d78d0767a915d863e11dc50357bfd22419eb0d","observation_id":"37add713-bd64-480a-ac98-ad05fa1a5df1","resolution":{"observed_at":"2026-08-06T14:49:56.588435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-08-05T05:38:49.539833Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05213","last_updated":"2025-09-05T16:15:22Z","snapshot_observed_at":"2026-08-05T05:38:46.023678Z","submitted_at":"2025-09-05T16:15:22Z","title":"An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T05:38:49.539833Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2509.05213"},"observation_digest":"sha256:4b2adece7791e1605488a6b488e3ac19bccd7cdc91cbc319beda246981a968b2","observation_id":"bd136f6e-2fc7-418f-8590-f6fd666fe6bd","resolution":{"observed_at":"2026-08-05T05:38:49.539833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":"2402.11505","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-07-02T13:16:58.669038Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources","venue":null,"work_id":"4f973fbd-7fcc-4f1c-a056-f98e6059c467","year":2024},"citing_paper":{"arxiv_id":"2605.16690","last_updated":"2026-05-15T23:06:59Z","snapshot_observed_at":"2026-07-06T23:27:48.303599Z","submitted_at":"2026-05-15T23:06:59Z","title":"UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:bc62e1ce93c026229ad870fb9d2a65bf3a59b8c99fed8fdf68f779d930b5843b","observation_id":"d4bbea2e-6bd4-497b-8ca5-f0bd630085e9","resolution":{"observed_at":"2026-05-20T19:08:54.369245Z","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":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":"2402.11505","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-07-02T13:16:58.669038Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources","venue":null,"work_id":"4f973fbd-7fcc-4f1c-a056-f98e6059c467","year":2024},"citing_paper":{"arxiv_id":"2605.18028","last_updated":"2026-05-18T08:18:22Z","snapshot_observed_at":"2026-08-01T16:16:20.727121Z","submitted_at":"2026-05-18T08:18:22Z","title":"FedSDR: Federated Self-Distillation with Rectification","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-20T12:18:10.362573Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2605.18028"},"observation_digest":"sha256:4cb413700ab28bf604d9ef153a4df300d8ba56b66aa52f658678c6de4f19a75b","observation_id":"18271e7b-d035-4402-8168-a5e94dde559d","resolution":{"observed_at":"2026-05-20T12:18:16.229864Z","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":"2402.11505","last_updated":"2024-05-30T15:46:10Z","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources","version":2},"cited_work":{"arxiv_id":"2402.11505","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.11505","snapshot_observed_at":"2026-07-02T13:16:58.669038Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources","venue":null,"work_id":"4f973fbd-7fcc-4f1c-a056-f98e6059c467","year":2024},"citing_paper":{"arxiv_id":"2606.06154","last_updated":"2026-06-04T13:28:48Z","snapshot_observed_at":"2026-08-03T18:23:02.874306Z","submitted_at":"2026-06-04T13:28:48Z","title":"Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-28T01:27:04.241484Z"},"links":{"cited_paper":"/paper/2402.11505","citing_paper":"/paper/2606.06154"},"observation_digest":"sha256:8e4eca9ba2a624898cc398bd125760fefe11b2040fbca24d35268c1accf907c0","observation_id":"e730c1e1-d646-45e4-b77d-54135d1902c0","resolution":{"observed_at":"2026-07-02T13:16:58.670594Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2402.11505/citation-record","integrity":"/paper/2402.11505/integrity","json":"/paper/2402.11505/citation-record.json","paper":"/paper/2402.11505"},"outbound":[],"paper":{"arxiv_id":"2402.11505","last_updated":"2024-05-30T15:46:10Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:31:45.847395Z","submitted_at":"2024-02-18T08:32:59Z","title":"Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources"},"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 7 inbound Pith citation observations for arXiv:2402.11505."}