{"as_of":"2026-08-23T21:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a69ae826c9d6879e8697cd9267810552a2d2c2b2d3205186bb439b8aaba9865","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:29:23.181738Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T15:40:18.951655Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.10114","last_updated":"2025-04-01T15:53:12Z","snapshot_observed_at":"2026-08-16T13:09:47.283002Z","submitted_at":"2024-10-14T03:05:12Z","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10114","snapshot_observed_at":"2026-08-08T12:20:08.831376Z","title":"Mixture of experts made personalized: Federated prompt learn- ing for vision-language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07855","last_updated":"2025-06-13T12:20:30Z","snapshot_observed_at":"2026-08-13T21:16:14.932934Z","submitted_at":"2025-02-11T14:04:43Z","title":"Vision-Language Models for Edge Networks: A Comprehensive Survey","version":2},"reference_index":171,"source":"pdf_text","source_observed_at":"2026-08-08T12:20:08.831376Z"},"links":{"cited_paper":"/paper/2410.10114","citing_paper":"/paper/2502.07855"},"observation_digest":"sha256:301a6dc56042453ce79dce00d8678dfcd3db82d8c0731f29b69700dca5b8ef38","observation_id":"df2fc534-4c75-410d-80b9-d958d25ec915","resolution":{"observed_at":"2026-08-08T12:20:08.831376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10114","last_updated":"2025-04-01T15:53:12Z","snapshot_observed_at":"2026-08-16T13:09:47.283002Z","submitted_at":"2024-10-14T03:05:12Z","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10114","snapshot_observed_at":"2026-08-16T05:29:23.181738Z","title":"Mixture of experts made personalized: Federated prompt learning for vision-language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.21063","last_updated":"2025-04-29T11:06:03Z","snapshot_observed_at":"2026-08-20T16:57:08.394366Z","submitted_at":"2025-04-29T11:06:03Z","title":"Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:29:23.181738Z"},"links":{"cited_paper":"/paper/2410.10114","citing_paper":"/paper/2504.21063"},"observation_digest":"sha256:19ef69ccb7264b738c8f1629f7785af6b2a0f12cca20e61e4fd4e634358ada63","observation_id":"b5cbaf2f-0b8f-406f-baa9-c2508c2dd471","resolution":{"observed_at":"2026-08-16T05:29:23.181738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10114","last_updated":"2025-04-01T15:53:12Z","snapshot_observed_at":"2026-08-16T13:09:47.283002Z","submitted_at":"2024-10-14T03:05:12Z","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10114","snapshot_observed_at":"2026-08-16T05:16:30.658382Z","title":"arXiv preprint arXiv:2410.10114 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.21099","last_updated":"2025-04-29T18:18:39Z","snapshot_observed_at":"2026-08-18T01:11:18.803829Z","submitted_at":"2025-04-29T18:18:39Z","title":"A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-16T05:16:30.658382Z"},"links":{"cited_paper":"/paper/2410.10114","citing_paper":"/paper/2504.21099"},"observation_digest":"sha256:baa2e6fc9966f130dec8e362fc74a881457ff457a25ec919af39818dea8af5a0","observation_id":"e366e8f4-3869-4b5b-acd9-afb80d98e751","resolution":{"observed_at":"2026-08-16T05:16:30.658382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10114","last_updated":"2025-04-01T15:53:12Z","snapshot_observed_at":"2026-08-16T13:09:47.283002Z","submitted_at":"2024-10-14T03:05:12Z","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10114","snapshot_observed_at":"2026-08-06T19:22:59.589159Z","title":"Mixture of experts made personalized: Federated prompt learning for vision- language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05685","last_updated":"2025-07-08T05:30:37Z","snapshot_observed_at":"2026-08-15T11:33:09.209402Z","submitted_at":"2025-07-08T05:30:37Z","title":"Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:22:59.589159Z"},"links":{"cited_paper":"/paper/2410.10114","citing_paper":"/paper/2507.05685"},"observation_digest":"sha256:98932640e354dfb52a0706c102091089cdd6e75ab463b21a6b36b645774b20e1","observation_id":"24bc6e20-6904-4e19-9807-1f97a72c4bbe","resolution":{"observed_at":"2026-08-06T19:22:59.589159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10114","last_updated":"2025-04-01T15:53:12Z","snapshot_observed_at":"2026-08-16T13:09:47.283002Z","submitted_at":"2024-10-14T03:05:12Z","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models","version":4},"cited_work":{"arxiv_id":"2410.10114","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.10114","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B","venue":null,"work_id":"1315be9a-a9e9-4688-b61b-c6c243349b9f","year":null},"citing_paper":{"arxiv_id":"2512.23070","last_updated":"2026-05-17T02:59:26Z","snapshot_observed_at":"2026-08-13T04:35:22.180852Z","submitted_at":"2025-12-28T20:32:13Z","title":"FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T15:36:06.671533Z"},"links":{"cited_paper":"/paper/2410.10114","citing_paper":"/paper/2512.23070"},"observation_digest":"sha256:1f0471e006abeee2a1aa8085e6b7c55d50a59f9714b5236055821168dfecedbe","observation_id":"bff5b8e1-3782-4c74-b5d5-e6513d402562","resolution":{"observed_at":"2026-05-21T15:40:18.954185Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.10114/citation-record","integrity":"/paper/2410.10114/integrity","json":"/paper/2410.10114/citation-record.json","paper":"/paper/2410.10114"},"outbound":[],"paper":{"arxiv_id":"2410.10114","last_updated":"2025-04-01T15:53:12Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T13:09:47.283002Z","submitted_at":"2024-10-14T03:05:12Z","title":"Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.10114."}