{"as_of":"2026-08-08T10:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f794c929a37d4849ea88a61a9cb277db025195863f5d8f0a9a111ba21df61de2","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T08:38:31.472369Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2607.21074/citation-record","integrity":"/paper/2607.21074/integrity","json":"/paper/2607.21074/citation-record.json","paper":"/paper/2607.21074"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T08:38:28.834920Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:28.834920Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:9d095408b85900a81ba30264ff98647b0a117bdac9fcf9ba4812e90646df108e","observation_id":"ec860c12-03da-4181-8a4d-95d1e7c00dbc","resolution":{"observed_at":"2026-08-01T08:38:28.834920Z","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":"2026-08-01T08:38:28.924826Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:28.924826Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:4457852fb32142a78696c6b2b3f8459ec169389ded23e964219049192e483fcf","observation_id":"f125565e-2608-4a05-b2b0-bff916200260","resolution":{"observed_at":"2026-08-01T08:38:28.924826Z","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":"2026-08-01T08:38:29.054746Z","title":"Dinov2: Learning robust visual features without supervision.TMLR, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.054746Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:9e1d570d31403e7828527fea0cf184637ba73a0d02013dc14a4327427ab52a6c","observation_id":"be75d79f-b6a6-4f61-a21c-b34e9d33093b","resolution":{"observed_at":"2026-08-01T08:38:29.054746Z","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":"2026-08-01T08:38:29.116501Z","title":"Parameter-efficient transfer learning for nlp","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.116501Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:2fb0c1597dc71c28dfb91b1d4e41bda8ab8c9308e73af8553c9c2fef856de1be","observation_id":"73397116-bf33-4528-863e-c31b6ee20c6c","resolution":{"observed_at":"2026-08-01T08:38:29.116501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-07T07:43:16.294957Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-01T08:38:29.248290Z","title":"Lora: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.248290Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:88021245875352f579637b9507c0edfd9338b2bb0449ea6b86f8d2984f46c315","observation_id":"5301611b-6a50-423e-8310-0c8a9dadf1d9","resolution":{"observed_at":"2026-08-01T08:38:29.248290Z","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":"2026-08-01T08:38:29.364748Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.364748Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:db5e2525bb3d05554de311926aaee3f2c7ccb5ba9320a1634575be570d903fb7","observation_id":"9ed7c7cc-25bc-41a4-868c-b5d41b3735f2","resolution":{"observed_at":"2026-08-01T08:38:29.364748Z","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":"2026-08-01T08:38:29.421252Z","title":"Federated optimization in heterogeneous networks.MLSys, 2:429–450, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.421252Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:6ff4f4958656669af9659812ec1ddb5a263e0364f04ac3cc275e376aa1d5d325","observation_id":"5db6b3a9-2cb4-4978-992b-d53f8dbad03f","resolution":{"observed_at":"2026-08-01T08:38:29.421252Z","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":"2026-08-01T08:38:29.534441Z","title":"Scaffold: Stochastic controlled averaging for federated learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.534441Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:6c5a0b18449c2bc00e415d85cdb50639a91c2cbeedf00a8fc33bcbaab3a9d29c","observation_id":"6476e56f-67a1-43ed-baae-5a14a3500490","resolution":{"observed_at":"2026-08-01T08:38:29.534441Z","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":"2026-08-01T08:38:29.677438Z","title":"Model-contrastive federated learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.677438Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:bedd04bd16927fb6b0f5bd7843f48fb34fc03cc155dd7cc40491341a68bcac26","observation_id":"7462d839-cca7-47dc-8536-48bef1928086","resolution":{"observed_at":"2026-08-01T08:38:29.677438Z","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":"2026-08-01T08:38:29.816318Z","title":"Federated learning based on dynamic regularization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.816318Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:00b7b03259b972799681ba063b30ecc6a9198d10b53ffc2442b8e67a7edf4100","observation_id":"8ec4b3f7-9100-4867-93a3-4f8760a1fc25","resolution":{"observed_at":"2026-08-01T08:38:29.816318Z","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":"2026-08-01T08:38:29.932649Z","title":"Heterogeneous lora for federated fine-tuning of on-device foundation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:29.932649Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:5d20f63e6c958c97be9343a9c8371591eabd0f526df3edb50876418d8440f7fb","observation_id":"449fd285-e7d8-4876-b619-67c5d8183a92","resolution":{"observed_at":"2026-08-01T08:38:29.932649Z","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":"2026-08-01T08:38:30.012966Z","title":"Federated learning with buffered asynchronous aggregation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.012966Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:d99ce4c42b35bfa233f36cede430babc6a21bbd4a75a71a07b58c7266644a017","observation_id":"37289da6-3445-4b04-90a0-96c20b0d4412","resolution":{"observed_at":"2026-08-01T08:38:30.012966Z","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":"2026-08-01T08:38:30.081175Z","title":"Heterofl: Computation and communication efficient federated learning for heterogeneous clients","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.081175Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:0a8f099362a7a07af093eaa645303e0bfbe400963fdc4b1c884629729977269a","observation_id":"f8f31b26-bbf4-43c4-99fb-b49de5a29a72","resolution":{"observed_at":"2026-08-01T08:38:30.081175Z","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":"2026-08-01T08:38:30.159263Z","title":"Towards building the federatedgpt: Federated instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.159263Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:848b591e238a52d5ce4ef4a00c0236e7f47ba0e137e13eed7f54109980fb33c9","observation_id":"b8e5e9cd-4996-41c0-9548-f212c428874b","resolution":{"observed_at":"2026-08-01T08:38:30.159263Z","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":"2026-08-01T08:38:30.284744Z","title":"Improving lora in privacy-preserving federated learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.284744Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:523de81d67f3ffca6f8d35d6cebb6fa7f16780548ba886060947bbc197a3f62b","observation_id":"69d4704c-8968-4641-8d30-75aefcc9a9d4","resolution":{"observed_at":"2026-08-01T08:38:30.284744Z","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":"2026-08-01T08:38:30.424741Z","title":"Flora: Federated fine-tuning large language models with heterogeneous low-rank adaptations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.424741Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:5c601fc43b18dff72e279cb9852f78c1a6ba6a3458f21a7e1aee5bb54a7a6f2d","observation_id":"c40c2b91-5654-43f9-8804-210ea0cb8ce7","resolution":{"observed_at":"2026-08-01T08:38:30.424741Z","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":"2026-08-01T08:38:30.518643Z","title":"Federated fine-tuning of large language models under heterogeneous tasks and client resources","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.518643Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:470c45d93cc68989b45742956f5b708ae72ed0b8a5896ed95965485ccaef2038","observation_id":"37afa640-fb74-4086-b0f9-f5924aa6529f","resolution":{"observed_at":"2026-08-01T08:38:30.518643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09199","last_updated":"2026-05-22T21:44:16Z","snapshot_observed_at":"2026-08-07T04:52:12.619953Z","submitted_at":"2025-06-10T19:36:36Z","title":"FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.09199","snapshot_observed_at":"2026-08-01T08:38:30.594411Z","title":"Florist: Singular value thresholding for efficient and accurate federated fine-tuning of large language models.arXiv preprint arXiv:2506.09199, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.594411Z"},"links":{"cited_paper":"/paper/2506.09199","citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:1b54652310afff83bbc19b511f080267e0cea380ce70f0c790ca2719c788ae4c","observation_id":"03ab38f2-3a36-43cb-a0db-114da5e99825","resolution":{"observed_at":"2026-08-01T08:38:30.594411Z","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":"2026-08-01T08:38:30.689797Z","title":"Lora-fair: Federated lora fine-tuning with aggregation and initialization refinement","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.689797Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:9519519c4005a07307104f3d32c9038974b5e162a63ad4afa82729314de4c758","observation_id":"97d360a3-3308-4aae-be7f-79f605d196b3","resolution":{"observed_at":"2026-08-01T08:38:30.689797Z","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":"2026-08-01T08:38:30.764984Z","title":"Screenot: Exact mse-optimal singular value thresholding in correlated noise.Annals of Statistics, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.764984Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:4b803f9fcf02a318ee1e7172169df6410e80d25c274ddff7644a7fad8f3cd59a","observation_id":"a56367f4-fbb1-4e0b-b8a7-19fb407a445d","resolution":{"observed_at":"2026-08-01T08:38:30.764984Z","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":"2026-08-01T08:38:30.854281Z","title":"Golub and Charles F","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.854281Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:46733e8b7d840a2e560a4ac4f0006f485153fb2c9f10accb1591b81bf90a5147","observation_id":"f2cb1361-6a3d-4f6e-b410-7d9159755884","resolution":{"observed_at":"2026-08-01T08:38:30.854281Z","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":"2026-08-01T08:38:30.959323Z","title":"On the distribution of the largest eigenvalue in principal components analysis","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:30.959323Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:ffe7e702a88e0b171671f1002496666c6b895021d083ddad69aa31c48b889d08","observation_id":"e77d2958-6157-4704-a548-5a77af9d45f2","resolution":{"observed_at":"2026-08-01T08:38:30.959323Z","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":"2026-08-01T08:38:31.083840Z","title":"Pytorch image models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:31.083840Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:e242a376bbfceec1ffe1ab64bcb83f4c973149ad2d7d5d17ecc85539540b9f58","observation_id":"32687a6e-78b7-4e7d-a918-2b9724b57a9e","resolution":{"observed_at":"2026-08-01T08:38:31.083840Z","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":"2026-08-01T08:38:31.209510Z","title":"Moment matching for multi-source domain adaptation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:31.209510Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:35c6a57f8bdc241536bf08020adfabd5bfb26db60ffb8cb551e65caf1244733d","observation_id":"1710422e-a866-4aa4-ab5b-38ca786d54aa","resolution":{"observed_at":"2026-08-01T08:38:31.209510Z","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":"2026-08-01T08:38:31.345994Z","title":"Towards non-iid image classification: A dataset and baselines.Pattern Recognition, 110:107383, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:31.345994Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:b605ba782c0e99b73acd382f9409f178d6ae22be7f1532a74cb1d21349f65f43","observation_id":"1f69bbff-bf48-46fb-9527-a50a1cb14acc","resolution":{"observed_at":"2026-08-01T08:38:31.345994Z","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":"2026-08-01T08:38:31.472369Z","title":"Federated learning on non-iid data silos: An experimental study","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:31.472369Z"},"links":{"citing_paper":"/paper/2607.21074"},"observation_digest":"sha256:d69caf7f5b96735785d3ad555bd63389d691c074dbd6abdc4b655838369c5233","observation_id":"73c3a835-ac70-4c63-bc7d-b8a3b6ec6cf0","resolution":{"observed_at":"2026-08-01T08:38:31.472369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.21074","last_updated":"2026-07-23T09:07:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-01T08:38:27.844747Z","submitted_at":"2026-07-23T09:07:56Z","title":"Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":26},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2607.21074."}