{"as_of":"2026-08-24T00:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:570f61bdbefdbbd9a597998ef4ddf15e0ae7b7adb4d66f58b61a7ee4a5ddd52a","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:17:00.903461Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2505.05130/citation-record","integrity":"/paper/2505.05130/integrity","json":"/paper/2505.05130/citation-record.json","paper":"/paper/2505.05130"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.716559Z","title":"Vision-language models for vision tasks: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.716559Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:71a3b52227f1745b78f931306bdd59251fa6119918eda0111b923a754e7fe32e","observation_id":"e687dbad-db3d-40f7-be80-7f25cd028e0a","resolution":{"observed_at":"2026-08-15T23:17:00.716559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.722110Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.722110Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:b12657f200511fe87d391f0f80efa8869120c56634856dcf6e3861e4f3838ea0","observation_id":"100a0b10-5e50-4d03-a6e0-02beff4459f2","resolution":{"observed_at":"2026-08-15T23:17:00.722110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.726672Z","title":"Maple: Multi-modal prompt learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.726672Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:f529d50ac9051324edcf97021d7aaa37fbd21be5a135b2e7897d83a2a2bf56d1","observation_id":"77792f7e-eb7f-48eb-b0c3-12b61a3bdc78","resolution":{"observed_at":"2026-08-15T23:17:00.726672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.731262Z","title":"Edge intelligence: Paving the last mile of artificial intelligence with edge computing,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.731262Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:03052d2b8eb51d16f6cd6f7afe2af06d7232f23c646980db2fbf9aee48b350c8","observation_id":"c650d6c4-8e8c-4e11-b8c5-75ca8fe018d6","resolution":{"observed_at":"2026-08-15T23:17:00.731262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.736061Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.736061Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:b046ea76eaeb5ea527286c2d49b77e1d1db70052bb8897a13a5fcd96a446c322","observation_id":"011ff154-755a-4ee3-a466-2b2ffaf44b95","resolution":{"observed_at":"2026-08-15T23:17:00.736061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.372746Z","title":"Adaptive asynchronous federated learning in resource-constrained edge computing,","venue":null,"work_id":"7ba23663-e614-49a0-ac8c-f64df0c40e45","year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.741127Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:45605f2ae596833d4a4a041aa4d2892cd5534a8ff5a27ca0c0f6e9c7ed85fde8","observation_id":"8cda77dd-eebd-473e-8b02-a056825da072","resolution":{"observed_at":"2026-08-15T23:17:01.383396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.746407Z","title":"Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.746407Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:43d8e297a608a7143b65b4a825ef4966309cf4434e9f4c1e2674129423c2d086","observation_id":"14a5b1c9-187f-4d5a-afd6-397e49927ad5","resolution":{"observed_at":"2026-08-15T23:17:00.746407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.750533Z","title":"Communication-efficient federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.750533Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:9afdf1f89d3e425edb2f553bb3e3015a643c7392c9c954596fe72621684fb331","observation_id":"7472be03-7a86-48fc-ab7d-74053fc02cfb","resolution":{"observed_at":"2026-08-15T23:17:00.750533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.343190Z","title":"Heterogeneity-aware cooperative federated edge learning with adaptive computation and communication compression,","venue":null,"work_id":"822c9e51-2267-42b6-a8e6-aba74e9b5add","year":2024},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.754810Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:7c74c5945fa35abec92b0ec9498c2b473f8f3bf3e1686fc8bafa7b7d6f33e87f","observation_id":"cd96e179-cf78-4d60-8287-4dddb0ae5d8c","resolution":{"observed_at":"2026-08-15T23:17:01.347674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.760849Z","title":"Com- putation and communication efficient federated learning with adaptive model pruning,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.760849Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:1741b956b6556a4f2e3775fc91cfc038d2d2a639dcfc3b548fa1559be1240596","observation_id":"9b1d5650-bf3a-4bb8-b4a6-e063160f5bf8","resolution":{"observed_at":"2026-08-15T23:17:00.760849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.765289Z","title":"Federated learning on non-iid data silos: An experimental study,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.765289Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:2092abdf4701f52fa833a0736995fbc090bf9c803c9cba2cb087d6dec56b39d6","observation_id":"3a972efe-9cf2-4252-9244-946a08aad67b","resolution":{"observed_at":"2026-08-15T23:17:00.765289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.316154Z","title":"Fedsiam- da: Dual-aggregated federated learning via siamese network for non-iid data,","venue":null,"work_id":"3aed3d8f-8a9a-4390-b2be-1395c4dc3498","year":2024},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.770190Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:08c96e5efcfca0e311b8dc089945c2d9ffef52970738a01ee921cef6668fcda4","observation_id":"9053e639-37ba-43e6-86be-b700b9022bed","resolution":{"observed_at":"2026-08-15T23:17:01.320676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.302833Z","title":"Overcoming noisy labels and non-iid data in edge federated learning,","venue":null,"work_id":"c36844a5-2629-4332-a3ae-ae023fc0549b","year":2024},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.775099Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:b0c813f142a0e16133570d8bc1505da2f1eba2e984c9c24f65d83089bb8d4bd8","observation_id":"eb13d718-a01e-45b1-82fb-24130d4b9f64","resolution":{"observed_at":"2026-08-15T23:17:01.307885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.779898Z","title":"Zero-shot text-to-image generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.779898Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:b2e8f626735e3bf1264e5cd983c916b8fdfa5887bcca585239bc9731a6f22101","observation_id":"0c6c0027-f2b2-482b-b9d1-31fae5c619be","resolution":{"observed_at":"2026-08-15T23:17:00.779898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.03930","last_updated":"2021-11-15T04:58:28Z","snapshot_observed_at":"2026-08-18T14:02:33.902650Z","submitted_at":"2021-11-06T18:09:22Z","title":"Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.03930","snapshot_observed_at":"2026-08-15T23:17:00.784408Z","title":"Tip-adapter: Training-free clip-adapter for better vision-language modeling,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.784408Z"},"links":{"cited_paper":"/paper/2111.03930","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:b82e5e272cf9c0e6352c7c62e20c8338357304ef2b310b8c0922fe6701a4107d","observation_id":"6ef43fb4-60f7-49ea-846a-44bb53bf2b2e","resolution":{"observed_at":"2026-08-15T23:17:00.784408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.789568Z","title":"Learning to prompt for vision- language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.789568Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:0632826b851d78d45edc09cb3242dbcd19f664eda03df615ee55a386338c127f","observation_id":"c267e6c8-4a9c-4693-84ae-3baa28f97fa0","resolution":{"observed_at":"2026-08-15T23:17:00.789568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-15T23:17:00.793715Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.793715Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:7c37d553372fca2df2d359bcdef59921d96933cd6ecc769122bbe6bf5825383a","observation_id":"46fd5043-3cf9-4ec9-8427-cf087214df44","resolution":{"observed_at":"2026-08-15T23:17:00.793715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.798526Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.798526Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:93e3632908037f9ac67c2fdbaa1c4555571aeff788764bd71cffbc8980312886","observation_id":"f0bea161-a0cf-4806-ad2f-5d95d6bc9ef9","resolution":{"observed_at":"2026-08-15T23:17:00.798526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.802684Z","title":"Parameter-efficient fine-tuning of large- scale pre-trained language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.802684Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:bdecb5d17a01c4c7be169a9bf3c13587f2ddedd4c951155a0ef7c32a868f2ae4","observation_id":"16a1d0eb-44ad-485f-8f04-202419271f1b","resolution":{"observed_at":"2026-08-15T23:17:00.802684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03164","last_updated":"2021-06-06T16:10:12Z","snapshot_observed_at":"2026-08-16T18:19:19.019925Z","submitted_at":"2021-06-06T16:10:12Z","title":"On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.03164","snapshot_observed_at":"2026-08-15T23:17:00.807289Z","title":"On the effectiveness of adapter-based tuning for pretrained language model adaptation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.807289Z"},"links":{"cited_paper":"/paper/2106.03164","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:31119491d03ebaec74a6217f8cddaf1a69ea6ae1a752b9385128812b8286d674","observation_id":"e27b41e0-bd9f-4078-9b6e-49644f051cda","resolution":{"observed_at":"2026-08-15T23:17:00.807289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08691","last_updated":"2021-09-02T17:34:41Z","snapshot_observed_at":"2026-08-16T20:01:36.160048Z","submitted_at":"2021-04-18T03:19:26Z","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08691","snapshot_observed_at":"2026-08-15T23:17:00.812500Z","title":"The power of scale for parameter-efficient prompt tuning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.812500Z"},"links":{"cited_paper":"/paper/2104.08691","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:773ab281d974fc0f60003f8397d391f6e609da47e035e42c25a7575709dc6798","observation_id":"68a1c031-0679-44f2-89c3-90b3a9c105d8","resolution":{"observed_at":"2026-08-15T23:17:00.812500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.816907Z","title":"A review of applications in federated learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.816907Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:f6140f2d2e0a4601a37af45c827c409817a0e4edbbc568aef80f416b439f6266","observation_id":"d4c0dbc5-b4b0-49d1-92af-ac4177474fa5","resolution":{"observed_at":"2026-08-15T23:17:00.816907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.821039Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.821039Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:0715106f6e20a185134c364b35c030a0886682d6b48b8f8d061903f44f0fb34e","observation_id":"2c7dab51-37a7-40e4-a257-c6d093cbec8d","resolution":{"observed_at":"2026-08-15T23:17:00.821039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.232984Z","title":"Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model,","venue":null,"work_id":"738d7d79-9e4e-41e2-9da2-f58918e339ff","year":2023},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.825335Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:e86de7f15801b828b99be806698a0608dd8a6b5466616597420a69ef2dbb9f3a","observation_id":"f073a088-b3c3-433a-a683-f1d0c2eb967f","resolution":{"observed_at":"2026-08-15T23:17:01.237822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.829436Z","title":"Federated optimization in heterogeneous networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.829436Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:0a52c0ce252dc28e2cdb44ee3f8f196f598bfc488fe304ac569bf4b070021e5c","observation_id":"0d35d450-8d62-4807-99d7-e8360fb6c657","resolution":{"observed_at":"2026-08-15T23:17:00.829436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.209767Z","title":"Generative adversarial networks,","venue":null,"work_id":"27c99d98-4451-46d8-b795-1c6ca657cf67","year":2020},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.833317Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:fe2ea72a4e440f897e9c32e2a93ada544a3a51a51987ac236a49da8c6c239a2b","observation_id":"b6cd0f8c-13ed-4746-a5e6-ce2467997adc","resolution":{"observed_at":"2026-08-15T23:17:01.214066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.837356Z","title":"Catastrophic forgetting and mode collapse in gans,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.837356Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:896c773287ac123432a69454099c5981449e14a503d258eb567d1993e5a9468b","observation_id":"f0b9b02a-5896-4fb3-a6fb-8c294a9d1d6e","resolution":{"observed_at":"2026-08-15T23:17:00.837356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.841371Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.841371Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:5838a473132fc90f40bd9d8bb2a14073ce0cad015cc7d7251900cc933281db30","observation_id":"16b9fd9c-3b21-45f7-951f-8a6f06c9071c","resolution":{"observed_at":"2026-08-15T23:17:00.841371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.845765Z","title":"Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.845765Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:a2f9b73883bfb4add5937d4396b609a3751e2099c3aa5397bddcfed08522207f","observation_id":"6223fe1b-23af-4765-a08a-ff75d137ad85","resolution":{"observed_at":"2026-08-15T23:17:00.845765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.850332Z","title":"Describing textures in the wild,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.850332Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:0416f0349cd5fcd78c5912ec9611bb7fb31d2b71566c25b74e90f60733ba78ef","observation_id":"613a5372-6286-45ce-b72a-ad810bd56a41","resolution":{"observed_at":"2026-08-15T23:17:00.850332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.854932Z","title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.854932Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:83e7eb0c8e9ee34d2fed52d14dc194c3e2a3a76d083c0e81d33758e3d27f3c7f","observation_id":"82706942-632b-4883-8747-5bb7f2a12547","resolution":{"observed_at":"2026-08-15T23:17:00.854932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1306.5151","last_updated":"2013-06-21T14:31:57Z","snapshot_observed_at":"2026-08-12T17:35:23.022229Z","submitted_at":"2013-06-21T14:31:57Z","title":"Fine-Grained Visual Classification of Aircraft","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1306.5151","snapshot_observed_at":"2026-08-15T23:17:00.859594Z","title":"Fine- grained visual classification of aircraft,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.859594Z"},"links":{"cited_paper":"/paper/1306.5151","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:299562c07c0a838a9886ffab58eaf405bf3a81834e70e448abafed86022c74f7","observation_id":"d3b53f9e-40b2-45f7-a648-67c9935946b8","resolution":{"observed_at":"2026-08-15T23:17:00.859594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:01.142444Z","title":"Food-101–mining discriminative components with random forests,","venue":null,"work_id":"fa69178b-3aef-46d2-b2b5-052d21910ca6","year":2014},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.864438Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:fe02bad58ddc56a71fb06e7c8c7584f22fa35921ad90ec8db439dafcc425e3a3","observation_id":"65712cd7-9b01-4025-863a-3888c0c61564","resolution":{"observed_at":"2026-08-15T23:17:01.149282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.868810Z","title":"Automated flower classification over a large number of classes,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.868810Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:b034e5f97aad49fd1a4f58a7c9fe22f702a44a68db0ebf0174e1fd60f0f0c439","observation_id":"650ef104-f71b-405d-9530-bc0d1d2902ff","resolution":{"observed_at":"2026-08-15T23:17:00.868810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.873144Z","title":"Cats and dogs,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.873144Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:38df0619161ba1593b7c052321e837c6bfc784d6f0390dfe60b36f1e881d86b4","observation_id":"3911cf60-6655-4340-8b4c-6dec43ba6a7a","resolution":{"observed_at":"2026-08-15T23:17:00.873144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.877288Z","title":"3d object representations for fine-grained categorization,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.877288Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:a92ab59f52f2cb338a862992a7d3541a19e9554bb79f9b2c7a1f11c37978d3a9","observation_id":"c1fd8835-dde8-4720-a3b9-239d2e8fc74e","resolution":{"observed_at":"2026-08-15T23:17:00.877288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.881646Z","title":"Sun database: Large-scale scene recognition from abbey to zoo,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.881646Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:be5bd8d8dfeeed05c6645fe8f48146ec952b9086b9ef782371165ca6855ed382","observation_id":"bb0777b3-01c4-46fa-8009-f043f5d0851c","resolution":{"observed_at":"2026-08-15T23:17:00.881646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1212.0402","last_updated":"2012-12-03T14:45:31Z","snapshot_observed_at":"2026-08-16T21:21:44.768787Z","submitted_at":"2012-12-03T14:45:31Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1212.0402","snapshot_observed_at":"2026-08-15T23:17:00.886201Z","title":"Ucf101: A dataset of 101 human actions classes from videos in the wild,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.886201Z"},"links":{"cited_paper":"/paper/1212.0402","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:9ce8cdd59d575850e12eb1b1396f423476eda6ca376c06ce9d1cecffcc3ca943","observation_id":"1d835d99-f2e7-44cd-bc04-91b3a1921a53","resolution":{"observed_at":"2026-08-15T23:17:00.886201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.891857Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.891857Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:052dd734fd6e6256caaf63c8fbc8c3d7f80a36a6f3614ec4cd507713ff04fb24","observation_id":"d5cfaa4f-8ea3-442d-b77a-9e5f9c80e680","resolution":{"observed_at":"2026-08-15T23:17:00.891857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:17:00.898829Z","title":"Deep leakage from gradients,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.898829Z"},"links":{"citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:dd7fa970a8febc02515b2bd7d45ee9b9f68d7bbaf64762908e38ac683ad73f3e","observation_id":"03da803f-c1e8-4712-a560-d777e112a26d","resolution":{"observed_at":"2026-08-15T23:17:00.898829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-15T23:17:00.903461Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:00.903461Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.05130"},"observation_digest":"sha256:f06808990bac9adafa1e5f742cdd148553217df4fcce8257f6b6f39c78440795","observation_id":"fab5d250-cd87-431d-b5dd-ee45cdbe27ec","resolution":{"observed_at":"2026-08-15T23:17:00.903461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.05130","last_updated":"2025-05-24T09:33:35Z","latest_version":2,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-17T13:58:39.667759Z","submitted_at":"2025-05-08T11:07:35Z","title":"CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":41},"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 24 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.05130."}