{"as_of":"2026-08-09T16:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:48853a4c843d02e7a5cad92fa0513554fc8940813193acc73c1934a227bd6ebb","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T10:43:31.650407Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2606.22383/citation-record","integrity":"/paper/2606.22383/integrity","json":"/paper/2606.22383/citation-record.json","paper":"/paper/2606.22383"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:a638b81f2224f9f58f5cf3b04f872bfb740576fbfbf249c7cbfd3090c3824f75","observation_id":"709c1540-dd9f-4862-a16f-c28406fbfa25","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: European Conference on Computer Vision (2014)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:2e9fc6ff2f472676e03ea0898b5a6208e1c1e498b9b951f9750b3789708d7398","observation_id":"d9c273ea-3eea-40a3-9de2-3a80c950ebd5","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Advances in Neural Information Processing Systems35, 16664–16678 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:37acbd3e04f01ce7259f79bcc973dfd481a24e289e1cb445def251717b9cee71","observation_id":"f064f95e-d9b2-4ed6-9635-f46d8094929b","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the Computer Vision and Pattern Recognition Conference","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:dec894eab0974f5d4aae49fca6e5636d48053ae772dbdabe20e9c864023a50c7","observation_id":"5f962fc0-130b-44b3-a1a2-3543ba8d69c6","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Advances in Neural Information Processing Systems 37, 102056–102077 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:39ed3462da390b3cbf8f2f686e0f5c2bf5780c6bdea4e2861c905433f24540ba","observation_id":"f3d90411-b9e6-4cbc-96ea-494417841471","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Advances in Neural Information Processing Sys- tems36, 52548–52567 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:2e0008a28898e687ce72d398d4557690037f714d3b56423c9e5e1afa50c2445e","observation_id":"bc6039a8-fb9d-428c-a15f-2e9ae51f825e","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-07-06T02:11:23.670680Z","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":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:ad0f75e66d4619d9b6a58092a7c91ff0af793c4ccb6eb3d1d49d007a9d4d84ab","observation_id":"f2b80b59-e5cb-43d6-aa94-037d4826b657","resolution":{"observed_at":"2026-07-04T08:59:42.694477Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the AAAI conference on artificial intelligence","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:a8dbc44359a8000720e933684f2c46bc398736225687b573a42ed9b30de934a4","observation_id":"1112668c-3137-493c-9a4d-2866e13525ea","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:c50c41bcdeca5968d20d2d6e823201e1b03584e7f61622afaef5378fd40d5486","observation_id":"8dd9133c-7ff0-408f-85f1-4153b91324f9","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Tenagyei et al","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:ac470c4818bcbe5733d813fe46864adc23bbf3a9100b2220c4bdb9baf3d5bafc","observation_id":"2b7df28c-3619-46ff-9aac-d7821c66414e","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Advances in neural information processing systems30(2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:4b72b9b5786ed8c7a93e66f825f03325f157486d2b468efa9de19e96ad55a75b","observation_id":"25a6ee31-3084-49f0-b8ef-e45e9f89441e","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.13770","last_updated":"2023-07-25T19:03:21Z","snapshot_observed_at":"2026-07-06T15:58:31.756298Z","submitted_at":"2023-07-25T19:03:21Z","title":"E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning","version":1},"cited_work":{"arxiv_id":"2307.13770","doi":"10.48550/arxiv.2307.13770","metadata_source":"arxiv_reference","pith_arxiv_id":"2307.13770","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Eˆ 2vpt: An ef- fective and efficient approach for visual prompt tuning","venue":"arXiv (Cornell University)","work_id":"e1b24874-863a-4eab-ac6b-50902b009f6b","year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2307.13770","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:d0bb5dcbd6b6b9d7f9bd7254e08e7542d3cdc1c1027f06bb22bdc35fcb0d5f3a","observation_id":"e3ad8cc2-fbd0-4dab-9c9f-f730021edb71","resolution":{"observed_at":"2026-07-04T08:59:42.692189Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"Advances in neural information processing systems35, 8291–8303 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:130c06da064e056fc09d39933603b5eb717ab6a1716c3a18acbb094f9648bc28","observation_id":"a4befb2a-871c-4d73-8eec-2929f0500863","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:c3e7893075b4a3ec500f600b87794d9cad3356553d1db21932b828cab9476443","observation_id":"02eae86f-ea1e-44df-88cd-854925d87b99","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:6c9dc8a120f174050affd8d89981dc92472089ccf0c77abbf0986d156cb90a2a","observation_id":"cfbbcc48-510d-4fd1-b40f-3a4333d74487","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the AAAI Conference on Artificial Intelligence","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:57bed45da3d43215534c31a8502a21f5d6122ee1d33fe0a2bb76615b4e210ef9","observation_id":"51f7c27d-eaef-43a7-809d-a98bbae26b7b","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: International conference on machine learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:7f9e20fa567c0801a986ec119c8f558c69622f7f4a303065555976742c929dc2","observation_id":"b21ff94f-28b3-4b52-90f6-3ae9c1a3a8c7","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Iclr1(2), 3 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:9339670ee63ffa905c305020e39116eb55106e4798452e51a805cc1c9bb3afee","observation_id":"6549f5b3-3242-4e81-9845-ada13b0d1506","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: 2009 IEEE conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:11aeafb2d3159c7d6ed1151533ae49c89b074fc3b5396212a69dc9e0b90d8120","observation_id":"423cb693-89de-4562-99a7-a9a5f7a0db30","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19243","last_updated":"2025-03-17T04:11:01Z","snapshot_observed_at":"2026-08-07T13:40:21.511554Z","submitted_at":"2024-03-28T08:58:20Z","title":"Efficient Learning With Sine-Activated Low-rank Matrices","version":5},"cited_work":{"arxiv_id":"2403.19243","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.19243","snapshot_observed_at":"2026-07-04T08:59:42.701726Z","title":"Efficient learn- ing with sine-activated low-rank matrices","venue":null,"work_id":"fa1ad185-f438-489b-9ca0-efde35e81d2a","year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2403.19243","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:e32222dd25a0886986aa6ec0339baa06dc57fe555f2d00a1fa60da53ec39330e","observation_id":"07ea70e9-fcde-4cd7-bd1a-cbfa21147f2c","resolution":{"observed_at":"2026-07-04T08:59:42.703320Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: European conference on computer vision","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:9b0b36fba250d468141b4b2c55e5a8c79314f3905d37ba88e9ad5e0a82e5a808","observation_id":"9ed687e9-bf6d-4be2-baad-0004a4145080","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.07039","last_updated":"2022-08-09T10:40:06Z","snapshot_observed_at":"2026-08-09T04:45:09.129535Z","submitted_at":"2022-07-14T16:32:28Z","title":"Convolutional Bypasses Are Better Vision Transformer Adapters","version":3},"cited_work":{"arxiv_id":"2207.07039","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.07039","snapshot_observed_at":"2026-07-04T08:59:42.703372Z","title":"Convolutional bypasses are better vision transformer adapters","venue":null,"work_id":"813441b3-7a4b-45e8-a30c-596893c305b6","year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2207.07039","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:24cf5fcb54998d2408b62e304b157aa9744c0b2d46c3bb854de7b3b4c5af161b","observation_id":"9c41c4c4-1f1d-4e85-997e-8f51433a8230","resolution":{"observed_at":"2026-07-04T08:59:42.704885Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the AAAI conference on artificial intelligence","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:6abe07824bfcd0986eca3614e348d16bad0e745dfd7bcbb9fffd0a20b1e0b915","observation_id":"03f8d45a-8630-4fee-8c24-645be1c7393c","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Advances in neural information processing systems 34, 1022–1035 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:c22b07d4c4a331fdde6e70d7383c67f558ef57c1a707e7e84ff1365c6d5fbade","observation_id":"2bda9bfe-5523-4635-ad36-fedefb10c970","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"2013 IEEE International Conference on Computer Vision Workshops pp","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:85ae092c916961953d20472ae34ba80c577cfd25386215b7cb540db99192c01d","observation_id":"0d7906e8-3792-4fab-b002-278e072cf4ae","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Advances in Neural Information Processing Systems35, 109–123 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:2300e9c32f13f8aeefd22e4bad11f06dfc7e632572b3f064c328609fbdbb5ef2","observation_id":"ba0e586d-b9af-4dc9-919a-eac4b8aa0e4e","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Pattern Recognit.165, 111607 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:4e265dd18c49ec2f937d56a0996fd77ae7bfc08df12941fdaa1c1d939b479d07","observation_id":"0c75c0f7-e578-4988-bc7d-78ac5da36f2f","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06243","last_updated":"2024-04-28T20:05:02Z","snapshot_observed_at":"2026-07-06T16:45:51.193825Z","submitted_at":"2023-11-10T18:59:54Z","title":"Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization","version":2},"cited_work":{"arxiv_id":"2311.06243","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.06243","snapshot_observed_at":"2026-07-04T08:59:42.685412Z","title":"Parameter-efficient orthogonal finetuning via butterfly factorization","venue":null,"work_id":"457513e5-ceb2-4fdd-9ef4-e97072828922","year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2311.06243","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:a667006ee4ed0bb12daeab6c41a187145232cea9bbfbcc42a1a88c0432941f9f","observation_id":"bf275f28-1425-4577-b714-30eca7434090","resolution":{"observed_at":"2026-07-04T08:59:42.686902Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:3314faa53d213106353933b2f6023ece63a5ae7221f15d8c4c72aa9eb6feea62","observation_id":"57bcc9bc-fe08-4f99-8437-8c037922faae","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:9009b97819a40e3398e6adbef6908c7cd4c8650b7866eabdc39a8d8539d2211e","observation_id":"35c29fe6-a363-45d2-bbfd-065d8530f19a","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08106","last_updated":"2023-03-21T02:51:27Z","snapshot_observed_at":"2026-08-06T16:57:03.696195Z","submitted_at":"2023-02-16T06:14:15Z","title":"Towards Efficient Visual Adaption via Structural Re-parameterization","version":2},"cited_work":{"arxiv_id":"2302.08106","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.08106","snapshot_observed_at":"2026-07-04T08:59:42.682827Z","title":"Towards efficient visual adaption via structural re-parameterization","venue":null,"work_id":"a9f28e3c-d491-4176-9d8a-52149fdd1e87","year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2302.08106","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:733dc7a351188b5daed7d0841853191cd2116276edb09aab1ff6df6f1573b3fd","observation_id":"a9f7a72f-781e-4e5f-a764-f6fcd67bee04","resolution":{"observed_at":"2026-07-04T08:59:42.684285Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: 2018 25th IEEE Interna- tional Conference on Image Processing (ICIP)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:3d081b7659fd613210830b2772bdf26618e04513237014b832ea528e056783d4","observation_id":"1abc7eea-f8eb-4bdc-bff3-26316602381f","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04316","last_updated":"2024-06-07T03:54:01Z","snapshot_observed_at":"2026-07-06T17:56:18.931340Z","submitted_at":"2024-04-05T15:28:44Z","title":"Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation","version":2},"cited_work":{"arxiv_id":"2404.04316","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.04316","snapshot_observed_at":"2026-07-04T08:59:42.695941Z","title":"Parameter efficient quasi-orthogonal fine-tuning via givens rotation.arXiv preprint arXiv:2404.04316","venue":null,"work_id":"53b49b42-0570-4038-b25e-24ae336b19fc","year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2404.04316","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:39df62e87e77b9e6e95dc4aaf014738726ac40cc940f16507e140fedafcbe448","observation_id":"b355a1d7-4786-4bfd-8f7f-9c40704d2cbf","resolution":{"observed_at":"2026-07-04T08:59:42.697546Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1306.5151","last_updated":"2013-06-21T14:31:57Z","snapshot_observed_at":"2026-07-06T03:16:28.287173Z","submitted_at":"2013-06-21T14:31:57Z","title":"Fine-Grained Visual Classification of Aircraft","version":1},"cited_work":{"arxiv_id":"1306.5151","doi":null,"metadata_source":"pith","pith_arxiv_id":"1306.5151","snapshot_observed_at":"2026-07-11T03:07:53.092922Z","title":"Fine-Grained Visual Classification of Aircraft","venue":"cs.CV","work_id":"ed360110-3ce4-4959-8c74-1785cd9e537d","year":2013},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/1306.5151","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:e6b36b9f57b7c093042f72f483fbd8b9a3ceea44e98c722bebf7d63a8f96764d","observation_id":"bba04bbb-501d-460c-9a76-ebb77ccefab6","resolution":{"observed_at":"2026-07-04T08:59:42.699926Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:dffd364f12627de92c3a6bf4c014ae62bce3196d2dd5f0aec41e9e5e317eca60","observation_id":"1c0b5515-022d-4fc6-a746-774d5bf420ff","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:51508717f1635d2e4df4721d88e8ecec85601893e1af40aa1f4795bde285088e","observation_id":"215091cd-ca95-4e90-b8dd-a8a36d1dfa70","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06)2, 1447–1454 (2006)","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:ffeaa8d43018862ae59c79e426056c08838371aa48f8cdab6550ef445431c3fe","observation_id":"bf9b33fa-4b18-4cc5-92a4-f3f520aa09a7","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: 2012 IEEE conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:e7457b12c7ed0c31aefdfecb869edc212470a9bcca128e1c396dd71b0d1176aa","observation_id":"021ff170-3ca7-4671-b38b-f853ae65ca30","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the AAAI conference on artificial intel- ligence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:841929a38adb9bae083da943287321ad5712c031f39539bf6e4a0fb65965297b","observation_id":"89f07967-edde-49a3-946c-9ddecac6178f","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the Computer Vision and Pattern Recognition Conference","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:d56b669a4365195ab77975ce41605a40269915220d45a4f795734cdfe389450c","observation_id":"dad3841f-4f05-45bb-b4a8-f07cabdabbdf","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"International Journal of Computer Vision115, 211 – 252 (2014)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:ae70e0ac9a70a9cf91ccffa60a89b16a2dc7003f8f36a8a5368a4fd621aac664","observation_id":"d7edb8e9-413d-4a5a-9413-d5f446cb36c2","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"AI magazine29(3), 93–93 (2008)","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:cbe01710663586abc1eb49a533cea116c6c0a91195053e82eab16d3b7495cf2f","observation_id":"6935a2a3-5053-429c-9b9b-b08025c25661","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11351","last_updated":"2024-08-21T05:36:53Z","snapshot_observed_at":"2026-07-06T19:03:52.342045Z","submitted_at":"2024-08-21T05:36:53Z","title":"Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes","version":1},"cited_work":{"arxiv_id":"2408.11351","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.11351","snapshot_observed_at":"2026-07-04T08:59:42.676846Z","title":"arXiv preprint arXiv:2408.11351 (2024)","venue":null,"work_id":"25a0f2d0-b0d6-4df0-9ef6-54872bade7b7","year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2408.11351","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:1722a9ee29a5187877b3754e6ccb63e25b80dc7c22cb04b3f53d731bb4a60127","observation_id":"7120978f-e343-4853-90bf-c32ea64e52a7","resolution":{"observed_at":"2026-07-04T08:59:42.678294Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: International conference on machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:a798fbe4d1a5f52376431c9eb747255880cd293f3b60424cb9f2da8cb9c3bb75","observation_id":"563c5e78-df9f-4e40-babc-f715d7ee5483","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"Knowledge and Information Systems14(3), 347–375 (2008) 18 E.K","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:ff6af3b9fa1f97f6fbe1fed69230eb43e4ab4e80aeddfc4404fb540e6b7189ad","observation_id":"9dafa373-618d-4238-86a7-30be12f7fa2f","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the AAAI conference on artificial intelli- gence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:af4c2b448f84cfe2cdd8e8b0365129877977b46e630bc9c28c57b9727e2520ab","observation_id":"99a26d7e-ff4b-498d-8288-7921a1541f10","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2505.11235","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T08:59:42.681233Z","title":"arXiv preprint arXiv:2505.11235 , year=","venue":null,"work_id":"d32b7b79-d983-46a6-b526-c24285686168","year":2025},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:114c93364809ae326ac8536633560bcded3de6aa1069749c3711cb5c67621360","observation_id":"13c01876-9a53-426c-a856-ad035101c321","resolution":{"observed_at":"2026-07-04T08:59:42.682996Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: International Conference on Machine Learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:8512d60c6753061a87e42ac232ff92bd381edffcea25ac1867e76b294fbb8b0f","observation_id":"c26e4b2f-698f-49bc-8369-fbb54259f45b","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00788","last_updated":"2023-03-02T03:00:36Z","snapshot_observed_at":"2026-07-06T13:58:56.329236Z","submitted_at":"2022-10-03T09:54:39Z","title":"Towards a Unified View on Visual Parameter-Efficient Transfer Learning","version":2},"cited_work":{"arxiv_id":"2210.00788","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.00788","snapshot_observed_at":"2026-07-04T08:59:42.698812Z","title":"arXiv preprint arXiv:2210.00788 (2022)","venue":null,"work_id":"8ef8238d-1d9e-4434-88f9-0ecae53117b4","year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/2210.00788","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:61a0458d3b728d95b7a4b41eb1eef132c6ed91a47cedf366d7e4d18127cffd45","observation_id":"b4c72103-c48c-4f72-86a9-b9cf676689d7","resolution":{"observed_at":"2026-07-04T08:59:42.700584Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:b995d12050d859f604d75f06fb03046ee0aa020470a6a4100fdd67c87906da0e","observation_id":"2f215009-5109-4681-a3bf-31bcedbb444b","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04867","last_updated":"2020-02-21T13:36:15Z","snapshot_observed_at":"2026-08-09T06:48:42.729935Z","submitted_at":"2019-10-01T17:06:29Z","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","version":2},"cited_work":{"arxiv_id":"1910.04867","doi":"10.48550/arxiv.1910.04867","metadata_source":"pith","pith_arxiv_id":"1910.04867","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","venue":"cs.CV","work_id":"eb743d69-6704-47c1-bb0f-76520dd00c3d","year":2019},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"cited_paper":"/paper/1910.04867","citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:23ce2125f261481c049ead3582a53a3336dccf6a842d5018eacd319ba2e3449d","observation_id":"3d0885e2-ac33-44d8-822c-f9684e12ab98","resolution":{"observed_at":"2026-07-04T08:59:42.702282Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-26T10:43:31.650407Z","title":"IEEE Transactions on Pattern Analysis and Machine Intelligence47(7), 5268–5280 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:6260a181c8cd0241a3308d7e20a47be7849b4eb7da868716866d6bd85a2992bc","observation_id":"173e33ba-b630-492f-b050-5b745a32ae4e","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","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":"2026-06-26T10:43:31.650407Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-26T10:43:31.650407Z"},"links":{"citing_paper":"/paper/2606.22383"},"observation_digest":"sha256:36a4a30de7f2f3e05b86c2318d473fa9fa6bc5449f3c80d96ceadebd91439a25","observation_id":"84e36979-2898-47fb-a561-285e10795cf2","resolution":{"observed_at":"2026-06-26T10:43:31.650407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.22383","last_updated":"2026-06-21T08:09:08Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T05:31:02.340896Z","submitted_at":"2026-06-21T08:09:08Z","title":"Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":8,"parse_uncertain":0,"unresolved":41,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":53},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2606.22383."}