{"as_of":"2026-08-16T14:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:18492ff682599d1b49e2cd91cdc8a48097c408457829f47566765e0c5e48bcd0","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:41:26.344455Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2506.19164/citation-record","integrity":"/paper/2506.19164/integrity","json":"/paper/2506.19164/citation-record.json","paper":"/paper/2506.19164"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.738732Z","title":"Language models are few-shot learners,","venue":null,"work_id":"e047cc17-2d75-44ca-b30e-32d18dfbc181","year":1901},"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":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.265914Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:58b44928dfa99a06b71809961d03fc7f6b27e1a640c32340cc2e71b1871d6803","observation_id":"e3a64cf3-5b66-488d-a3ff-5e8b09feb02d","resolution":{"observed_at":"2026-08-15T18:41:26.743311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.724497Z","title":"Domain-specific pretraining for natural language understanding,","venue":null,"work_id":"a04e69a4-4253-4539-9fd3-db64bb45a231","year":2021},"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":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.270842Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:1e21d5f98b9d6e9437bf8b02e3778a887bab84b577b0449030acd271acc269c2","observation_id":"2647cb45-6932-48df-a7d0-f19f3b379837","resolution":{"observed_at":"2026-08-15T18:41:26.728950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.709196Z","title":"Privacy challenges in large-scale language models,","venue":null,"work_id":"4d5bec44-ca8c-4148-a4fd-6bc2bd8a6169","year":2022},"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":3,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.275402Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:6f18e73d34ad292adf296904362d6e10f29c85c053771e0d9b9a0f35ad03a656","observation_id":"81ef46b2-7e7d-4771-893f-e7d0e3723656","resolution":{"observed_at":"2026-08-15T18:41:26.714212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.694129Z","title":"Health data security and privacy: Challenges and solutions for the future,","venue":null,"work_id":"879836a1-b2f8-4213-b034-4eb8eeac7db7","year":2022},"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":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.279142Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:f47a46e9731d248ff7cdef526774236cbf5e2aad4d9a7a4c9b5ac3c4b436564d","observation_id":"6b21e4b9-0663-4046-8f2d-85ad373b1f1f","resolution":{"observed_at":"2026-08-15T18:41:26.698729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.678833Z","title":"Communication-efficient learning of deep networks from de- centralized data,","venue":null,"work_id":"4b0d1b4c-c61d-4248-9c6e-1d25cf9c8f9b","year":2017},"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":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.283150Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:890ab7d622adf9afae3371716ea321efd768a4f8dd3a88bf7632b24bdba24ae9","observation_id":"b9511d5d-13a3-465d-85c0-5f5f994536de","resolution":{"observed_at":"2026-08-15T18:41:26.684088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.665731Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":"aa556539-2151-4b90-8eaa-8b04ae85da49","year":2021},"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":6,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.287238Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:0a15f48779c5bdf57dcdd834acacecde39113a0693496ebe2f161dca61b517f4","observation_id":"2b60f0d6-7a22-4166-a1b2-7217b898fa5b","resolution":{"observed_at":"2026-08-15T18:41:26.669783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.02733","last_updated":"2022-05-18T07:02:54Z","snapshot_observed_at":"2026-08-13T17:05:05.154867Z","submitted_at":"2022-01-03T00:27:05Z","title":"Testing the Robustness of a BiLSTM-based Structural Story Classifier","version":2},"cited_work":{"arxiv_id":"2201.02733","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.02733","snapshot_observed_at":"2026-08-15T18:41:26.505893Z","title":"Testing the Robustness of a BiLSTM-based Structural Story Classifier","venue":"cs.CL","work_id":"c7927bd3-b3f4-4705-a5c7-fdccf1f0d191","year":2022},"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":7,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.291386Z"},"links":{"cited_paper":"/paper/2201.02733","citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:7510c7aad7db5a0d0a9318b70e30c48e23b7fba4e1112e53698e5ac7b5521e91","observation_id":"9884f86e-ddc2-4fb7-b502-642f7a1121b6","resolution":{"observed_at":"2026-08-15T18:41:26.513698Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.652100Z","title":"Improving language understanding by generative pre-training,","venue":null,"work_id":"5aa85771-3de6-4245-8ce3-8ddc17454007","year":null},"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":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.295334Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:6b6d6d0ed99fffaefcdcd30f9c5bce3866a256afa65b724f8b28649fe1fb0f9e","observation_id":"8d5340ef-19ec-485e-acfc-9f2258b70de2","resolution":{"observed_at":"2026-08-15T18:41:26.656777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.623319Z","title":"Recurrent neural network based language model,","venue":null,"work_id":"74f122a1-c890-4724-97d4-e02fea07f175","year":2010},"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":9,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.302266Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:c59af57db7b9d9cb150a0bca95725982e66c4efd0253b02d92cb039b92313a54","observation_id":"d9eb3c9c-3e9d-441f-9665-fcc8e18ced1a","resolution":{"observed_at":"2026-08-15T18:41:26.630297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.603470Z","title":"Attention is all you need,","venue":null,"work_id":"51ea243f-09b7-4382-9217-3203dc9708d7","year":2017},"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":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.306075Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:012885b1428073b5fbf77c0426739ef5280c16d19e7cbab7edd4d7538c24bfe4","observation_id":"0515afd6-41ef-4b84-84bd-2b9b706f6625","resolution":{"observed_at":"2026-08-15T18:41:26.608690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.588232Z","title":"Lora: Low-rank adaptation of large language models,","venue":null,"work_id":"f15ed02e-9101-44c5-8c63-5ad884af42d9","year":2022},"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":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.309580Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:1ae6d6a49ccc5a104fffd2e33379557417a59795f44fdb01416e3fe17232e185","observation_id":"e4bcae10-9750-40ef-9256-02bfca5b4c61","resolution":{"observed_at":"2026-08-15T18:41:26.593011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.571401Z","title":"Aligner: Improving peft efficiency with alignment layers,","venue":null,"work_id":"09f65ef7-58e0-407f-82ea-467956e82e28","year":2023},"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":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.313449Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:fff86aeea4e028b5e080a2c7e92c10b97a1976dc6c0e4e32166ea17426911fb0","observation_id":"bbdafad5-8f9e-4594-b822-b06f90ca31d9","resolution":{"observed_at":"2026-08-15T18:41:26.576599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.552385Z","title":"Scaling language model size in cross-device federated learning,","venue":null,"work_id":"8aa74be1-c57c-45d2-bee3-63dcead083f6","year":2022},"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":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.317216Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:55b770942e7905729a5f3e2713cc5c28ab4e7eaa02b4883b44840c71a497bc24","observation_id":"b225bc3f-94fe-4cb3-a830-140e6aaf15a9","resolution":{"observed_at":"2026-08-15T18:41:26.557566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.537794Z","title":"Communication-efficient federated learning via knowledge distillation,","venue":null,"work_id":"0bdf29e6-3483-433f-b89c-3bd1a95b9525","year":2022},"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":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.320972Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:53c4ac172924d90b1d53ef109135b1b28f8917115d712cf67ae8b7104b8dca52","observation_id":"8dfc7941-6af8-47e7-ac5d-70419b035d10","resolution":{"observed_at":"2026-08-15T18:41:26.542504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.01708","last_updated":"2024-12-24T05:57:39Z","snapshot_observed_at":"2026-08-13T14:13:15.904423Z","submitted_at":"2022-10-04T16:08:54Z","title":"Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.01708","snapshot_observed_at":"2026-08-15T18:41:26.329524Z","title":"Conquering the communication constraints to enable large pre-trained models in federated learning,","venue":null,"work_id":null,"year":2022},"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":16,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.329524Z"},"links":{"cited_paper":"/paper/2210.01708","citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:8fba6225d1c861152855fc3b7e7d91c0fb2f7c5f32abb369cf2a5db9cdfb35e0","observation_id":"23f66982-f1e9-4336-a12e-8831eb811768","resolution":{"observed_at":"2026-08-15T18:41:26.329524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.05644","last_updated":"2024-01-29T17:13:04Z","snapshot_observed_at":"2026-08-13T11:45:42.697869Z","submitted_at":"2023-05-09T17:42:34Z","title":"Towards Building the Federated GPT: Federated Instruction Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05644","snapshot_observed_at":"2026-08-15T18:41:26.334803Z","title":"Towards building the federated gpt: Federated instruction tuning,","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":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.334803Z"},"links":{"cited_paper":"/paper/2305.05644","citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:bef6a848e6ce222a90378f0610ca8cf9ebdd0a42a2a728936517611d6bf0698f","observation_id":"d0390697-5be9-4830-aee1-ed0886bf5157","resolution":{"observed_at":"2026-08-15T18:41:26.334803Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.524444Z","title":"FLoRA: Federated fine-tuning large language models with heterogeneous low-rank adaptations,","venue":null,"work_id":"0cbff0bf-f892-4f88-b232-479f633bc5d1","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":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.340770Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:0ac382699c52a7c5990fc370295ccf3e927d13a2b758b4ccc2688b186ff7b682","observation_id":"ef3df64a-c9cb-42dd-a0d5-c67162750c76","resolution":{"observed_at":"2026-08-15T18:41:26.529307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:26.344455Z","title":"On the convergence of zeroth-order federated tuning for large language models,","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":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.344455Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:0cada2e182cffe851da96f8edef324370534964b6b24fcff469b08d442e33ebc","observation_id":"bad5690e-6895-4cbd-b89c-1fc47ec9f317","resolution":{"observed_at":"2026-08-15T18:41:26.344455Z","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":"2026-08-15T18:41:26.298803Z","title":"Available: https://cdn.openai.com/research-covers/ language-unsupervised/language understanding paper.pdf","venue":null,"work_id":null,"year":null},"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":2018,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:26.298803Z"},"links":{"citing_paper":"/paper/2506.19164"},"observation_digest":"sha256:8b7a480f4e2e58062ce39db7c75186b6d71fe0d99c9253c4f7c3a98cd94b4a29","observation_id":"36ff94f6-5697-41dd-a48e-6d989602058d","resolution":{"observed_at":"2026-08-15T18:41:26.298803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.19164","last_updated":"2025-06-23T22:03:21Z","latest_version":1,"primary_category":"cs.LG","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"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":20},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.19164."}