{"as_of":"2026-08-07T17:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5076976399374bad673a3ddbb66be5e618a60b5b7e91d8abac9b6ef96ec4a0ed","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-20T19:06:08.951475Z","state":"measured"},{"denominator":78,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":78,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2605.16690/citation-record","integrity":"/paper/2605.16690/integrity","json":"/paper/2605.16690/citation-record.json","paper":"/paper/2605.16690"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T16:46:21.672159Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":"34ddc4cd-b661-4d8d-82b9-7eca76b6ae02","year":null},"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":1,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:846bcc32d28e08ea2f34153840560ee4e249a4c7b98e5c73b2c1a06ba9e15a05","observation_id":"0be6d88a-4465-4de0-b242-5b55d4e730d7","resolution":{"observed_at":"2026-05-20T19:13:41.319297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International Conference on Machine Learning , pages=","venue":null,"work_id":"e1a35be6-bd69-4880-a2e3-47f130e18967","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":2,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:c9bfcd5f6b5a6bc7c6a4d23b42028eb31fecdbe7be12b2a19d55e0598fdaf47e","observation_id":"788ebb1c-10ae-458a-8de8-fc2f7d334469","resolution":{"observed_at":"2026-05-20T19:13:41.321025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nature Machine Intelligence , volume=","venue":null,"work_id":"5a6cac2e-74c9-4b6f-b9f1-26f0d3f850db","year":2023},"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":3,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:cb048b681c52cc48ef5fbb80b4b1c221ffe6ce43d0465c58df6016ceca83eaa1","observation_id":"4f7f25e1-fdec-45e3-b41e-3f66c231c415","resolution":{"observed_at":"2026-05-20T19:13:41.315803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The Twelfth International Conference on Learning Representations , year=","venue":null,"work_id":"0997b7f0-8821-411e-ba25-31d459ab0f70","year":null},"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":4,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:f2bff51cce9c1fd7be385779ac6b6ddbf8e6b365ddce3a436318ba903530e904","observation_id":"7d7c9e38-0687-43ba-8790-276d0a283d53","resolution":{"observed_at":"2026-05-20T19:13:41.310423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2504.21099","last_updated":"2025-04-29T18:18:39Z","snapshot_observed_at":"2026-08-07T15:58:05.645002Z","submitted_at":"2025-04-29T18:18:39Z","title":"A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning","version":1},"cited_work":{"arxiv_id":"2504.21099","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.21099","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2504.21099 , year=","venue":null,"work_id":"3ad729e5-8479-470f-95c3-e51f19779d3f","year":2025},"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":5,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2504.21099","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:e34d6839e706ec9715d58429a741db16ecb806cb9d571cc72f818dfd8f83c6a6","observation_id":"cd70e0c6-056d-490d-a86e-0b8daa13a7c7","resolution":{"observed_at":"2026-05-20T19:08:54.372572Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":"f934faa7-4cb2-4499-a288-ae4ba43954cf","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":6,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:68bdad175b81cfe29a8e20909df2fcf6175f76b21cb2a0337e489e9d8b646424","observation_id":"c41694d9-374f-472e-9386-ff1792d9a228","resolution":{"observed_at":"2026-05-20T19:13:41.312233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2605.16690","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":null,"cited_work":{"arxiv_id":"2502.15436","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2502.15436 , year=","venue":null,"work_id":"b7c495ce-7c7d-4b90-baf1-9c13183f2429","year":null},"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":8,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:b5cd1fb13fe9477f34ce90a7f164cb7138306daccba3a5004e94c57e730fcb4c","observation_id":"9fbee1ad-623b-460f-91a1-52ab536e977f","resolution":{"observed_at":"2026-05-20T19:08:54.380874Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=","venue":null,"work_id":"a7ad1da1-69da-47d8-9899-e73633da33f5","year":null},"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":9,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:c8ad168c4b343a97556f24556bf021a9ed368472c21f38fd5f2632939f80c604","observation_id":"ad37c185-6726-407e-b744-7e685a17b710","resolution":{"observed_at":"2026-05-20T19:13:41.314107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":"d29ac165-6b45-4324-982b-aa4c6f68228e","year":null},"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":10,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:86da829cc0e40093b52f18c8117bac1efec3afbd0782ae83cf1e9ac767a3c05e","observation_id":"2c659bcc-a2c5-49a5-94de-b0b897be20b8","resolution":{"observed_at":"2026-05-20T19:13:41.293601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2506.09199","last_updated":"2026-05-22T21:44:16Z","snapshot_observed_at":"2026-08-07T04:52:12.619953Z","submitted_at":"2025-06-10T19:36:36Z","title":"FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models","version":2},"cited_work":{"arxiv_id":"2506.09199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.09199","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2506.09199 , year=","venue":null,"work_id":"6a017074-bc8e-4b8c-a671-59ecf0565f65","year":null},"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":11,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2506.09199","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:fd7c837af6f59a883b6ff5a36dee8c19d1db5b143222564cd9ec0f175b7a5b08","observation_id":"be448eba-8363-4a24-848a-077687a8e927","resolution":{"observed_at":"2026-05-26T02:02:26.506599Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ICLR 2025 Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning , year=","venue":null,"work_id":"50128f36-f7ff-485c-903a-28005604f1dd","year":2025},"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":12,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:56419c74cb5c6c9f0f3d059f200c34b97672449dde4eb8a62b766c8f9e9c5445","observation_id":"e108583a-fbf2-4f5e-b581-e4c8c711e19c","resolution":{"observed_at":"2026-05-20T19:13:41.295448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T02:17:55.911587Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":"2ba2a79a-03a9-497f-a7d0-ad563b6a2ee7","year":null},"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":13,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:ad664cae508993d0cd9e95e097949f77065db84d4c9c733d6aa89ac849698959","observation_id":"f2d7e63e-f49d-46f7-ae85-5e13564ac428","resolution":{"observed_at":"2026-05-20T19:13:41.299046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International Conference on Artificial Intelligence and Statistics , pages=","venue":null,"work_id":"840991b6-c441-40f4-8233-07a72c78fc78","year":2017},"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":14,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:4ca965548314c543703925244fb5115bbff2a5cf6b0abeb54170c0ed432af3b8","observation_id":"530a5beb-1318-4b1b-8093-bc719ab2f754","resolution":{"observed_at":"2026-05-20T19:13:41.301201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2202.08906","last_updated":"2022-04-29T23:24:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-02-17T21:39:10Z","title":"ST-MoE: Designing Stable and Transferable Sparse Expert Models","version":2},"cited_work":{"arxiv_id":"2202.08906","doi":"10.48550/arxiv:2202.08906","metadata_source":"pith","pith_arxiv_id":"2202.08906","snapshot_observed_at":"2026-07-11T03:27:46.863255Z","title":"ST-MoE: Designing Stable and Transferable Sparse Expert Models","venue":"cs.CL","work_id":"b7581741-3f43-4528-a7d0-3af9e51a4d9f","year":2022},"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":15,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2202.08906","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:2f01d1b9049b7858d38d05106db78fb490760a9abf82d70cb613c5565b974020","observation_id":"30a1f700-48dc-442b-8706-5c3d466a7a87","resolution":{"observed_at":"2026-05-20T19:08:54.316621Z","resolver_source":"local_arxiv","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":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"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":16,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:b49df903a149996342e7d9bdf307e97d4f6cd2138734a0b060db1982b1dea3dd","observation_id":"099bf210-82e4-4240-8e4b-af340a8dc30a","resolution":{"observed_at":"2026-05-20T19:08:54.313335Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ NeurIPS 2023) , year=","venue":null,"work_id":"be5f7f20-b86c-4126-8813-a7857f85a26c","year":2023},"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":17,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:f5b1d401bd4dfd4810b1c7c213e8efa1ea20f2eac0aab3515b39460593c0b822","observation_id":"9b97d7af-2852-4897-996f-29b670c48b0e","resolution":{"observed_at":"2026-05-20T19:13:41.285845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Workshop on Machine Learning and Compression, NeurIPS 2024 , year=","venue":null,"work_id":"0027013c-286b-4b6d-8136-d89d77adb230","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":18,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:ac72ba7f1136f61bf55f4a363d526c60e74a16b8d5998d933b2d8e2c8d0a9935","observation_id":"92de40b9-6631-48f6-be98-1b9f52cba233","resolution":{"observed_at":"2026-05-20T19:13:41.287936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"1998 , publisher=","venue":null,"work_id":"2a321f75-2829-436a-b8a1-fcdd1f7c4baa","year":1998},"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":19,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:756838d3c14fbf8e472bffc5cb2963fdc32d4759fc246664067b4fc2c0779d8b","observation_id":"8fbec5a0-811c-4030-b8d0-b948662fbd06","resolution":{"observed_at":"2026-05-20T19:13:41.289896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Illinois Journal of Mathematics , volume=","venue":null,"work_id":"8770b8fa-738f-4cba-b93e-0b51620192ce","year":1963},"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":20,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:faac55d14091d21fca98fb5789e12200736978d34e6f2836aa5e818abc5ff503","observation_id":"af9f68f8-ca57-441c-90de-15ad16826636","resolution":{"observed_at":"2026-05-20T19:13:41.291710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":"ef663991-3eff-4d86-b099-7297b06772df","year":2023},"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":21,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:a31f5ebd6b9ebc58bbaf5763c51c5250fd281f0fe20e89ef39d625d32bcb9506","observation_id":"bcd3b76a-22f8-4d89-9c08-34c1be5a9750","resolution":{"observed_at":"2026-05-20T19:13:41.299411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2501.13985","last_updated":"2025-01-23T07:49:24Z","snapshot_observed_at":"2026-07-06T20:25:12.962104Z","submitted_at":"2025-01-23T07:49:24Z","title":"Pilot: Building the Federated Multimodal Instruction Tuning Framework","version":1},"cited_work":{"arxiv_id":"2501.13985","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.13985","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2501.13985 , year=","venue":null,"work_id":"b91512c9-5b2c-4b89-a969-4bb6d5df78ae","year":null},"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":22,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2501.13985","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:9b7f3e854180343f65d0d862d593baf22c4ccb7e3f4a2c368ce3ae80939c630d","observation_id":"a3271561-71fa-490d-9e06-bc31441ac24c","resolution":{"observed_at":"2026-05-20T19:08:54.359877Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T04:20:40.370853Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"6f2c3b13-dbdc-4452-84af-8d6e58c2d776","year":null},"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":23,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:172ab583994ea50650335135970ab846e691def0e45d927f6940c6f6e36c3348","observation_id":"0a59f653-660d-43f7-8446-5eb8ed33d7b0","resolution":{"observed_at":"2026-05-20T19:13:41.324321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the ACM Web Conference 2023 , pages=","venue":null,"work_id":"2b306166-bc47-45dc-a382-ae6e9190fddd","year":2023},"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":24,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:101c8701456792207f3def909fc805ca5dac2f67b87c04828be383e7c9b267fa","observation_id":"6f6e53f5-26f6-49f3-b140-0d92d66131aa","resolution":{"observed_at":"2026-05-20T19:13:41.317593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Mathematics , volume=","venue":null,"work_id":"b2f7045d-61cd-4ee3-801e-05b1d08d205a","year":1976},"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":25,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:470a8495cae08e77d681af07e0d394ea56bc6795a8f6a7413aaf738e4361bc70","observation_id":"dfa39df2-e3ea-40b6-b036-9364c5357d44","resolution":{"observed_at":"2026-05-20T19:13:41.322651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"a9523e15-c69b-457a-86a6-b142b15bf7e4","year":null},"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":26,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:a5bab5f9380881d18318141db7c826806f5ab1fdc144053662187b2fcb9bf463","observation_id":"f6210934-c273-49d0-8616-872b169a8d5a","resolution":{"observed_at":"2026-05-20T19:13:41.293094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards Building the","venue":null,"work_id":"098976bd-371d-4aa4-8c08-fcb62ac8fef8","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":27,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:0b5df9796669bb1a54521c3581e2863324fca24d2c917c63930c9f4c3cc7424d","observation_id":"a85c2485-7406-47ef-b9a4-f79638724629","resolution":{"observed_at":"2026-05-20T19:13:41.296921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2405.17267","last_updated":"2024-05-27T15:25:32Z","snapshot_observed_at":"2026-07-06T18:20:39.922334Z","submitted_at":"2024-05-27T15:25:32Z","title":"FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation","version":1},"cited_work":{"arxiv_id":"2405.17267","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17267","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2405.17267 , year=","venue":null,"work_id":"62f2b0fd-b174-4f5b-b6b3-2b4ebffe43e9","year":null},"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":28,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2405.17267","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:30a30832b0341a02b1b016e5b5ed9fa65d008f6ff5f68dcccf17082a827432d9","observation_id":"1d3ae7dd-673c-4f3c-a5f0-d5f1ecbf6ed8","resolution":{"observed_at":"2026-05-20T19:08:54.378054Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":"091c05c0-d18e-49fe-8360-c782a3521bf6","year":null},"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":29,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:fefd64c713f456cc45881d67f7ed5f560a4b523b0998b9921c53f9f135f362df","observation_id":"5a75d7d1-3f63-4f74-b1ab-9a95655a6a22","resolution":{"observed_at":"2026-05-20T19:13:41.300793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities , year=","venue":null,"work_id":"132f9f29-6b85-4361-914c-469fda60c38b","year":null},"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":30,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:bab54148aa8f52cca923466f3ef2208cbf3c2acf0866a2262dc5049753a57ae9","observation_id":"828f8b86-3c6d-47b3-9ebe-0990ccff23f6","resolution":{"observed_at":"2026-05-20T19:13:41.304449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T02:17:56.671207Z","title":"International conference on machine learning , pages=","venue":null,"work_id":"89a43c97-e710-4eeb-a864-c46098f67455","year":2020},"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":31,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:e3a52c64c216da4e53b5e9c47f297628d4ea500d4faa22abd7ac918179653d43","observation_id":"33b81e0b-7f1c-4e4a-bdc8-857e11736968","resolution":{"observed_at":"2026-05-20T19:13:41.308471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":"08a71917-8568-4739-8318-a0c6257b2a3c","year":null},"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":32,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:b63609a335ef3115ba41a00801fe336f786c0ee1484378472bf49b6035922e5f","observation_id":"4f7d5529-7ff4-4f99-8c24-cc6089dadc78","resolution":{"observed_at":"2026-05-20T19:13:41.287448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"d9c4fa7c-c5c0-4f59-a71c-704975510ae5","year":null},"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":33,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:ad1acb996c83e95933ea3ccdbfa4ed437075038a7b737514ab93d7699ac574c8","observation_id":"4916eed1-e3dc-4a6d-8146-fc63476409fb","resolution":{"observed_at":"2026-05-20T19:13:41.310810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International conference on machine learning , pages=","venue":null,"work_id":"6cace22a-3523-4e75-b234-02b7945b1fe3","year":2020},"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":34,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:bd96b01fac1448f04ae5514e7df5bb1d5abe65463edfbc3167ed0c62ec21d48f","observation_id":"d0377284-899e-47cd-8bd4-72e63dece601","resolution":{"observed_at":"2026-05-20T19:13:41.285396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2008.00051","last_updated":"2021-05-09T19:49:46Z","snapshot_observed_at":"2026-07-06T09:43:35.929296Z","submitted_at":"2020-07-31T19:37:59Z","title":"On the Convergence of SGD with Biased Gradients","version":2},"cited_work":{"arxiv_id":"2008.00051","doi":"10.48550/arxiv.2008.00051","metadata_source":"arxiv_reference","pith_arxiv_id":"2008.00051","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"and Stich, S","venue":"arXiv (Cornell University)","work_id":"058e05a0-2926-4e9a-ac32-aaf38f06aa5f","year":2008},"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":35,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2008.00051","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:690328092af37357f7870f1bac16761278361b8f34b3a05c3c0c8b779b856808","observation_id":"a58217d0-0695-421a-92ab-0f69610fc47d","resolution":{"observed_at":"2026-05-20T19:08:54.375386Z","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":"2026-05-24T01:23:18.877967+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-24T01:23:18.877967+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"ed8f7890-52b4-40fa-995a-5402f70a26c0","year":null},"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":36,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:500fead454d01e729e309a4f40fbc65d9d05a265196b1e62331f3b7e032a572a","observation_id":"a8476326-96b0-4777-a95e-d237aab6d682","resolution":{"observed_at":"2026-05-20T19:13:41.283382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Conference on Learning Theory , pages=","venue":null,"work_id":"789594d8-19e5-4efe-be52-fb23776d9055","year":2019},"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":37,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:a799bab6f95fdc732073da7f6e33244bb15edcb2957faa01d384669a3eebb2ad","observation_id":"302a0591-2bb1-40ad-a057-010812a728aa","resolution":{"observed_at":"2026-05-20T19:13:41.291335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T07:33:30.643557Z","title":"SIAM review , volume=","venue":null,"work_id":"b94896e1-3712-42e0-8835-95f311e03e18","year":2018},"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":38,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:49f5903c6119da3963827182f770b49bade3850936c3511fb0bb621f06dc3a89","observation_id":"9f3dfacc-dfb2-4ba2-a4f1-0c97b3f32217","resolution":{"observed_at":"2026-05-20T19:13:41.283703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International conference on machine learning , pages=","venue":null,"work_id":"b5180c76-f977-4c60-8ad3-a8485a74dc26","year":2019},"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":39,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:4596e717c4b5ac599052beb6d9e3695f6fa8291a90f79f236b15ff8aa49eafac","observation_id":"b2e59f68-96aa-4215-8901-65774eeb30b7","resolution":{"observed_at":"2026-05-20T19:13:41.305178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"d88bae02-19ef-4c22-b90e-1d40f751e3fa","year":null},"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":40,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:733fd8191c61e65f3b7d0f3e935f87d20a21a25f903397901ee47dfa773f9b99","observation_id":"5548305d-a693-4bfa-ad65-8c180dcb9a2d","resolution":{"observed_at":"2026-05-20T19:13:41.281534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2013 , publisher=","venue":null,"work_id":"5265be96-c802-434b-9b04-cf8150aa2447","year":2013},"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":41,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:400ba6a79f009e2ceddf2bc5233634062e296b96bd23c6e312e49a9973ee66b6","observation_id":"9160cf71-bd22-40c3-9a55-5f9cba5a5fe5","resolution":{"observed_at":"2026-05-20T19:13:41.269891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-06T05:02:01.313626Z","title":"Linear convergence of gradient and proximal-gradient methods under the polyak-","venue":null,"work_id":"d655c9a8-469a-4fbe-8fb1-2f91788fee84","year":2016},"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":42,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:e3683754565226c2e8a3c917e0306f4d813813fa9861b0b7152e240467d41904","observation_id":"0be94494-e079-4c7c-8d9b-74462a5a564f","resolution":{"observed_at":"2026-05-20T19:13:41.271820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-07-06T05:27:13.416519Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":"1701.06538","doi":"10.48550/arxiv.1701.06538","metadata_source":"pith","pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","venue":"cs.LG","work_id":"2c6b3f6d-54e4-4df7-baa7-475a490799af","year":2017},"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":43,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:a1950efb71ce52f0065305c5faba874ea634312888324b19cb7d114686afc3ed","observation_id":"21e117f8-261e-4a5b-a9d6-48851d79bd44","resolution":{"observed_at":"2026-05-20T19:08:54.362960Z","resolver_source":"local_arxiv","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":"2026-08-01T08:08:24.174744+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:24.174744+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T00:36:39.740772Z","title":"Journal of Machine Learning Research , volume=","venue":null,"work_id":"98474244-687e-4868-9f6d-df3cdb9ae9ed","year":null},"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":44,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:8a2787c61b9bb7fe13e6afbf81bc7d988c95f9525cb2bdb062a553b5104bf61c","observation_id":"1d0dbdd0-c458-4c2d-a4db-f8c3041bf1f0","resolution":{"observed_at":"2026-05-20T19:13:41.263634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T19:01:19.697886Z","title":"International conference on machine learning , pages=","venue":null,"work_id":"48bb1cd0-44bb-47ff-a421-9c434a121795","year":2022},"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":45,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:cbd4067d5036fb8cca5bb4ec74953ada61c5a6b684d735796c88679716e7516f","observation_id":"1121e0b5-a651-4086-a7c2-167c314be330","resolution":{"observed_at":"2026-05-20T19:13:41.265878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2401.04088","last_updated":"2024-01-08T18:47:34Z","snapshot_observed_at":"2026-08-07T13:04:50.040909Z","submitted_at":"2024-01-08T18:47:34Z","title":"Mixtral of Experts","version":1},"cited_work":{"arxiv_id":"2401.04088","doi":"10.48550/arxiv.2401.04088","metadata_source":"pith","pith_arxiv_id":"2401.04088","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mixtral of Experts","venue":"cs.LG","work_id":"0de8c352-9daa-4e1e-8c7b-3d0dec69f369","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":46,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2401.04088","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:4d98fa83e4037b60ccd2c03926ce00630968229cb3616a8aef6d31bf1bfe599b","observation_id":"739253a8-c944-4e04-8402-5144d038db50","resolution":{"observed_at":"2026-05-20T19:08:54.357101Z","resolver_source":"local_arxiv","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":"2026-07-09T08:48:39.110013+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T08:48:39.110013+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04434","last_updated":"2024-06-19T06:04:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-07T15:56:43Z","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","version":5},"cited_work":{"arxiv_id":"2405.04434","doi":"10.1145/3593013.3594097","metadata_source":"pith","pith_arxiv_id":"2405.04434","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","venue":"cs.CL","work_id":"1e1df141-cac8-47fd-b068-c4c96e51e331","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":47,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2405.04434","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:5140b38e8fc1072e89c3602484f42b7ffa4daf2988a09cb04418cd09cc797d3c","observation_id":"5e337c8c-6438-40c5-8d15-5318006157c3","resolution":{"observed_at":"2026-05-20T19:08:54.366268Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2021 , url=","venue":null,"work_id":"bc6b97c7-6500-4b9f-b633-d94381d13f64","year":2021},"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":48,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:36ad3147e3239f17fc48c5f741e1725fa577c62c4c9593ec5d78dfb5333da193","observation_id":"2333b3ba-f8cf-4b12-b2a4-739b24f2b67b","resolution":{"observed_at":"2026-05-20T19:13:41.267938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2408.15664","last_updated":"2024-08-28T09:31:09Z","snapshot_observed_at":"2026-08-03T02:22:36.564849Z","submitted_at":"2024-08-28T09:31:09Z","title":"Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts","version":1},"cited_work":{"arxiv_id":"2408.15664","doi":"10.48550/arxiv.2408.15664","metadata_source":"pith","pith_arxiv_id":"2408.15664","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts","venue":"cs.LG","work_id":"267500ca-1512-478f-8a1b-6ecbdb09771d","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":49,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2408.15664","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:cd40b8d38b71988eb5288008a2d01e89a0227f6a0582f25bcb83015646fdca25","observation_id":"3c7e1a1c-a805-4b5a-b2d6-6eaecace31aa","resolution":{"observed_at":"2026-05-20T19:08:54.353999Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Han and Yuan Zhong , booktitle=","venue":null,"work_id":"d043c706-ca60-4096-88ca-e248fd7d5f76","year":2025},"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":50,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:8ad55a248e9930560f1025f0a803b3d075f329955b1b69a08cef14e0859e6504","observation_id":"cd1c89e0-d197-48c2-adca-e800a072b1d9","resolution":{"observed_at":"2026-05-20T19:13:41.277755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2409.12136","last_updated":"2024-09-18T17:00:20Z","snapshot_observed_at":"2026-08-07T11:17:05.501623Z","submitted_at":"2024-09-18T17:00:20Z","title":"GRIN: GRadient-INformed MoE","version":1},"cited_work":{"arxiv_id":"2409.12136","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12136","snapshot_observed_at":"2026-07-04T06:39:36.837359Z","title":"2409.12136 , archivePrefix=","venue":null,"work_id":"06417e42-da05-4a53-9f13-9d31b815ca28","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":51,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2409.12136","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:a35c565f6927f0e32f2bbb9f2d26747ce7c5a8c2b681b858e2ac03bca951a6b0","observation_id":"cb635fac-eafd-454c-b463-e89018c06de9","resolution":{"observed_at":"2026-05-20T19:08:54.351169Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ReMoE: Fully Differentiable Mixture-of-Experts with Re","venue":null,"work_id":"94678a2d-0a1a-4504-9169-84ebaac432b5","year":2025},"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":52,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:06cb2cdaa770d9334462312c865563e5e2735d828500436a30312bdccd15d9b0","observation_id":"8b2755e0-4ebf-4c6e-8c89-10c19ee862b4","resolution":{"observed_at":"2026-05-20T19:13:41.279711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":"2504.12463","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2504.12463 , year=","venue":null,"work_id":"38510bf9-ffd4-4f94-807b-b9d1f4e66195","year":null},"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":53,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:cd7adc7e044408cf2fa000027f122b326058873cab467ba25681d40d58b666f8","observation_id":"0ed57a94-6d29-4ded-8a1a-5f906232a695","resolution":{"observed_at":"2026-05-20T19:08:54.348182Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning , volume =","venue":null,"work_id":"b7a6b1bf-f3d8-42cb-9857-8f37cbcebec1","year":null},"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":54,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:0e1c505d13844ec503fa87dffd77644fad539bf067a4e01db8ceca88bb2832b3","observation_id":"111dde8f-5a31-469f-bb4f-b4296464b8cb","resolution":{"observed_at":"2026-05-20T19:13:41.306633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T11:36:12.790467Z","title":"Proceedings of the AAAI conference on artificial intelligence , volume=","venue":null,"work_id":"b02622aa-75de-4289-92a8-bd2b7de82f38","year":null},"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":55,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:7fd1a29275a93db65757ed0574c85fd4a87b9e79677fcb4fd90a1bf6ba38cc0f","observation_id":"7cdb1bc1-abb4-4d6d-8369-09f465eac2c7","resolution":{"observed_at":"2026-05-20T19:13:41.254649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":"3526d82e-8e61-4ee2-905a-6c46bab69b09","year":2018},"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":56,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:932bb15db52e3781a24815b1e559528cb60d316b0daee76b5c3473ec9f393f93","observation_id":"2501a5c9-34e5-48bd-8306-ed22f80dea79","resolution":{"observed_at":"2026-05-20T19:13:41.259223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=","venue":null,"work_id":"025236f4-fb38-49fc-aa1b-43825f6f7a7a","year":null},"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":57,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:151347c6653c328017033b53ea5698faae3f107ee643bfda0400269a5a4f76b3","observation_id":"7d8ed8ed-6606-492a-8a0a-72bd075d2f2f","resolution":{"observed_at":"2026-05-20T19:13:41.261483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1904.09728","last_updated":"2019-09-09T17:29:55Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-04-22T05:36:37Z","title":"SocialIQA: Commonsense Reasoning about Social Interactions","version":3},"cited_work":{"arxiv_id":"1904.09728","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.09728","snapshot_observed_at":"2026-07-08T01:44:26.198243Z","title":"SocialIQA: Commonsense Reasoning about Social Interactions","venue":"cs.CL","work_id":"3f93670e-0ae7-40e5-bed5-74c216638dd1","year":2019},"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":58,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/1904.09728","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:6d4dd5846d35cc6c475dbbc6b6e942bb1521d0379d20f718fa34e5e7915cc4c3","observation_id":"e7968a1d-84ec-4d34-802e-e6d59a4d4f84","resolution":{"observed_at":"2026-05-20T19:08:54.342333Z","resolver_source":"local_arxiv","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":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":"1803.05457","doi":"10.1162/tacl_a_00448.https://aclanthology.org/2022.tacl-1.5","metadata_source":"pith","pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","venue":"cs.AI","work_id":"28ea1282-d657-4c61-a83c-f1249be6d6b1","year":2018},"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":59,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:696bd99957597334fac5f59d9dc29b5926d96545f23d75fa8cede37861bf1538","observation_id":"2af03fca-4c38-4dc9-84b8-a5a948dc16fc","resolution":{"observed_at":"2026-05-20T19:08:54.336488Z","resolver_source":"local_arxiv","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":"1905.10044","last_updated":"2019-05-24T05:48:49Z","snapshot_observed_at":"2026-07-06T07:55:12.121264Z","submitted_at":"2019-05-24T05:48:49Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","version":1},"cited_work":{"arxiv_id":"1905.10044","doi":"10.48550/arxiv.1905.10044","metadata_source":"pith","pith_arxiv_id":"1905.10044","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","venue":"cs.CL","work_id":"511eeb84-4b95-46d5-b14f-50da43f4f19f","year":2019},"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":60,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/1905.10044","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:f4744df9916ebd052cbfaf584cd3e49ca005f3984a7b4336926a67a0e3de5fba","observation_id":"d4f69285-3bd1-4cc3-ac40-f3d5d55461a1","resolution":{"observed_at":"2026-05-20T19:08:54.339517Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3474381","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Winogrande: an adversarial winograd schema challenge at scale","venue":"Communications of the ACM","work_id":"4c068e31-e489-48e6-9d4b-4482f06309f2","year":2021},"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":61,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:3faecc5803735392176a4cbfbffd9ef56b1a1a6812a55b4596c3501facd03858","observation_id":"1cd6b953-8e08-43ae-99e6-91b289700005","resolution":{"observed_at":"2026-05-20T19:08:53.846994Z","resolver_source":"doi","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":"2026-06-01T20:56:26.970279+00:00","source":"crossref_status_cache"},{"observed_at":"2026-06-01T20:56:26.970279+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"First Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models , year=","venue":null,"work_id":"5f5c030a-849d-4de3-8e40-246938f8370f","year":null},"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":62,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:bc0d83d6428d2773bfdca772a92f8ccee3215a8d8f864551c06c46123007d905","observation_id":"198924b5-c47e-489e-81d1-1a045eaa8bc5","resolution":{"observed_at":"2026-05-20T19:13:41.257098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":"2411.19557","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2411.19557 , year=","venue":null,"work_id":"6bf1e6c1-5bb9-434f-8b54-2db367ac1dcf","year":null},"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":63,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:ef745637652ac94f9dbab63e6c45495f18ae3672cbf2d620233992dee659d9b3","observation_id":"6586d771-f44c-4354-a064-aca2d4411dc4","resolution":{"observed_at":"2026-05-20T19:08:54.345328Z","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":"2409.02060","last_updated":"2025-03-03T01:25:46Z","snapshot_observed_at":"2026-07-29T16:11:06.082798Z","submitted_at":"2024-09-03T17:08:20Z","title":"OLMoE: Open Mixture-of-Experts Language Models","version":2},"cited_work":{"arxiv_id":"2409.02060","doi":"10.48550/arxiv.2409.02060","metadata_source":"pith","pith_arxiv_id":"2409.02060","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"OLMoE: Open Mixture-of-Experts Language Models","venue":"cs.CL","work_id":"91fa6600-4fda-4022-b2c7-205e8a242a49","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":64,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2409.02060","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:d490b57c132e6953b34a48d1ff4e0dba2c75c6791dccad7aea3470ae59de9cf9","observation_id":"4c0d49ba-77c1-48c3-9222-de35874c6805","resolution":{"observed_at":"2026-05-20T19:08:54.332348Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=","venue":null,"work_id":"fe559a7a-bfaa-444f-9e99-7b9c3d2ee09c","year":null},"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":65,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:d9461e6eec1eea1ad8612c88f70ac98db8ac46cd327f8be03864a3eecf54f75a","observation_id":"283f820d-70d6-4f9e-bd6c-3c53a298cff9","resolution":{"observed_at":"2026-05-20T19:13:41.302668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"56b1d8d2-afbd-42ef-b6d7-dcfab13c5fb8","year":null},"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":66,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:862656ebe6e0d2a4d4f3839c392764596b0e42108f0a204485e7cad0b2dd8824","observation_id":"d1bdd1b0-fc42-408b-a9c5-3f9127dc773a","resolution":{"observed_at":"2026-05-20T19:13:41.270817Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"5726976e-044b-4336-a0f2-45d0b8a9c6cb","year":null},"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":67,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:da108ec7424694749e66a51de30d7f2ee8abad5be8298f2597fa1089aaa424b2","observation_id":"ae4e41d6-ac5a-4966-8aad-918f8b8cda39","resolution":{"observed_at":"2026-05-20T19:13:41.307054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neurocomputing , volume=","venue":null,"work_id":"8aa9bbca-e0b3-430c-b6d7-a999244bcc29","year":2025},"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":68,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:c756d302307d6daaeadd21bdd9b41bc2ddf1e666a3233ad4f3467923c24d5f8b","observation_id":"0421c08b-3207-488b-85e5-f861cc712ee4","resolution":{"observed_at":"2026-05-20T19:13:41.227211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":"00d2cc2b-452c-4dfd-811a-c0f68ab0811f","year":null},"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":69,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:7c12abd9856da0062d412fe63b942031fb0314e48fc9f40818425caaa2221fb6","observation_id":"4c0111a5-7e11-4a7c-aef3-cc7bc8327b59","resolution":{"observed_at":"2026-05-20T19:13:41.219487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"5ba6c4cf-ea7d-4b64-aa28-1b871fc2a00e","year":null},"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":70,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:48baca07203532e32d90c4d7d75dc3d95c0b43e30f3709c8f8cddf00c63647a4","observation_id":"cc8777d2-e39a-49d4-b87f-1df536db6b88","resolution":{"observed_at":"2026-05-20T19:13:41.221530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"b3168508-147b-4231-87f1-852559cbc4de","year":null},"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":71,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:eefa7d55d05449428fa8e4bc3f2655fc04026bace390c591b8ab7c682f9f7637","observation_id":"8efab690-f784-4eaf-b7ea-d64432b9697f","resolution":{"observed_at":"2026-05-20T19:13:41.226572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2404.08003","last_updated":"2025-01-23T23:33:53Z","snapshot_observed_at":"2026-07-06T17:59:00.124173Z","submitted_at":"2024-04-09T04:21:13Z","title":"Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis","version":5},"cited_work":{"arxiv_id":"2404.08003","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.08003","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2404.08003 , year=","venue":null,"work_id":"61dc934a-7edb-4b22-86d9-0a41416a3f8e","year":null},"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":72,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2404.08003","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:11d3c62c28392bd9a332f04d1180711bbedb82be2a31dc4b202fdc2e159e05b5","observation_id":"9cdfcf5a-5b13-41ff-8ccc-2fc865e40bc0","resolution":{"observed_at":"2026-05-20T19:08:54.326466Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ACM Transactions on Knowledge Discovery from Data , volume=","venue":null,"work_id":"e70ab093-17fb-48dc-b63c-52f79cf102be","year":2025},"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":73,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:dd3384d7db603917ad3aa17ecb3789b86d07be09e6aefc3a8dade8557e935781","observation_id":"1906faf3-83ab-4ae4-8141-2052b90945f7","resolution":{"observed_at":"2026-05-20T19:13:41.225534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-06T16:52:40.529973Z","title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":"be14d58e-a731-463c-9741-5c11489ead96","year":2021},"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":74,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:b2ca3ba3f773f81a36648d6aa208e855c541ceddf0e26375d7f0fd7ba33f128c","observation_id":"75802fc7-d566-412f-af6b-43f848ab3f94","resolution":{"observed_at":"2026-05-20T19:13:41.217500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1909.06335","last_updated":"2019-09-13T17:26:20Z","snapshot_observed_at":"2026-08-02T11:40:53.964079Z","submitted_at":"2019-09-13T17:26:20Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","version":1},"cited_work":{"arxiv_id":"1909.06335","doi":"10.48550/arxiv.1909.06335","metadata_source":"pith","pith_arxiv_id":"1909.06335","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","venue":"cs.LG","work_id":"1e8d2981-bd90-4c86-8676-223498b6d816","year":2019},"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":75,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/1909.06335","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:663dfae7ed78f9e71b05828be4f7a783e3a73d603dde9fdf1a7200331486ec87","observation_id":"21e8f002-1eb0-4eb5-8f5e-b627ceb3e16f","resolution":{"observed_at":"2026-05-20T19:08:54.319388Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International conference on machine learning , pages=","venue":null,"work_id":"d190c866-7096-44c6-a6e9-3efc6de9af34","year":2019},"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":76,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:d68d782fee78172a62b6f5b6d13685161f88e68ccea2c58c1fa3ed53cfe76cf8","observation_id":"e69fd90e-c282-47b3-8c1f-93881b39da85","resolution":{"observed_at":"2026-05-20T19:13:41.233065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ACM Computing Surveys , volume=","venue":null,"work_id":"61dd3b7c-1a9d-4028-917f-ce38e8a9d37e","year":2023},"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":77,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:c56d9619dfd8055a5fd19e909340430f7ca81e18c4db366087f3139b3d74af3f","observation_id":"55d6af90-e920-4a3b-aace-95697d2e65bb","resolution":{"observed_at":"2026-05-20T19:13:41.244519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":"2001.08361","doi":"10.1145/3616855.3635845","metadata_source":"pith","pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Scaling Laws for Neural Language Models","venue":"cs.LG","work_id":"b7dd8749-9c45-4977-ab9b-64478dce1ae8","year":2020},"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":78,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:b5f81dd056a87b98bad522ae893d593b82faad6fafc60a52eefaf437aa5ef2d6","observation_id":"d3aca156-f4b6-4144-baca-829511edd8aa","resolution":{"observed_at":"2026-05-20T19:08:54.322622Z","resolver_source":"local_arxiv","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"}}],"paper":{"arxiv_id":"2605.16690","last_updated":"2026-05-15T23:06:59Z","latest_version":1,"primary_category":"cs.LG","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"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":0,"metadata_mismatch":14,"parse_uncertain":0,"unresolved":1,"verified_exact":10,"verified_fuzzy":53},"total_outbound_references":78},"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 7 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2605.16690."}