{"as_of":"2026-08-22T04:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:055d1fd40a650ece2b4e2298383d8ed91a412ff7ce988b98f70a6d95fc068fe7","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:46:19.953428Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.00580/citation-record","integrity":"/paper/2505.00580/integrity","json":"/paper/2505.00580/citation-record.json","paper":"/paper/2505.00580"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.774172Z","title":"Lamda: Large model fine-tuning via spectrally decomposed low-dimensional adaptation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.774172Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:3265815291b71df53f28663dcc4e14c855ce324f0ce52d5604f49da6b32fe815","observation_id":"449e1c5d-e915-40ef-90bf-b65af332a3d8","resolution":{"observed_at":"2026-08-16T04:46:19.774172Z","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":"2026-08-16T04:46:19.796981Z","title":"An exploration of parameter redundancy in deep networks with circulant projections","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.796981Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:d1750f799fb815086822bb20737472acf0e4869a99754e28fb0c9a131f041cd7","observation_id":"d9db21bc-172d-402f-8fcc-d021bf849f94","resolution":{"observed_at":"2026-08-16T04:46:19.796981Z","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":"2026-08-16T04:46:19.809128Z","title":"The pascal recognising textual entailment challenge","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.809128Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:97988e039eb732e48cb55f73ee951b24a2471795881468255bf76c8d64a3bdf5","observation_id":"30e282b7-320e-46ea-ae38-7bfb91e42c88","resolution":{"observed_at":"2026-08-16T04:46:19.809128Z","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":"2026-08-16T04:46:19.826249Z","title":"Automatically constructing a corpus of sentential para- phrases","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.826249Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:f23ad29a7c43dd94238cf8904a8c7cf8a80cfc034279adcb0968f58ac9c50bfb","observation_id":"a36b049f-6970-47c7-aa87-6b6f160416d4","resolution":{"observed_at":"2026-08-16T04:46:19.826249Z","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":"2026-08-16T04:46:19.831089Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.831089Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:bb5f30ad3ed72ec114a08d9be5d5206b6ec9ef0379d466cb0b6e785fe306029b","observation_id":"51fa7349-80ed-4269-838a-1c3dbf860220","resolution":{"observed_at":"2026-08-16T04:46:19.831089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.373030Z","title":"Parameter-efficient fine-tuning with discrete fourier trans- form","venue":null,"work_id":"a317868f-0417-4d9a-82ac-577a57864d4a","year":2024},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.835464Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:74e58f140b41f04525611b4762e94b9b1bfbd02d7ff7c2f58dae7409ee03a7be","observation_id":"43525bad-5213-48af-a776-68789be1111e","resolution":{"observed_at":"2026-08-16T04:46:20.378822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.840077Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.840077Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:9463741d1e83242c00e22eaca2e602c9fd98622d958eacf1644015c9bf9eeb87","observation_id":"9af9fa19-70be-4d8a-87ec-6ea6155ccd86","resolution":{"observed_at":"2026-08-16T04:46:19.840077Z","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":"2026-08-16T04:46:19.858665Z","title":"Kopiczko, Tijmen Blankevoort, and Yuki M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.858665Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:34f770a6e9fb073c3dd7c1fc31264919ce8fd4203ef6fe4cf2d2ab28a0604b4f","observation_id":"c5079ce0-3b50-4445-a1de-b3d759a08b0a","resolution":{"observed_at":"2026-08-16T04:46:19.858665Z","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":"2026-08-16T04:46:19.864231Z","title":"Learning multiple layers of features from tiny im- ages","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.864231Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:6f2b6e2c35164673c302345242716e1d4c63ae1364e18cdd2aceac4b3b5e0200","observation_id":"9aefcffd-4c76-4e17-86e8-c8cc8a097a45","resolution":{"observed_at":"2026-08-16T04:46:19.864231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.257997Z","title":"Prefix- tuning: Optimizing continuous prompts for generation,","venue":null,"work_id":"92bb00c7-ec1d-4191-9f13-d59117987176","year":2021},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.874319Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:d9dffbd3cdbf4f19069f7e7b92ab4a314c3c4ae354eb2a466aa45cb0734891fc","observation_id":"8412c2c5-4a19-42f0-bd27-2c32419edb71","resolution":{"observed_at":"2026-08-16T04:46:20.262897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06784","last_updated":"2024-05-10T19:22:24Z","snapshot_observed_at":"2026-08-21T02:11:01.207985Z","submitted_at":"2024-05-10T19:22:24Z","title":"Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06784","snapshot_observed_at":"2026-08-16T04:46:19.879170Z","title":"Open challenges and opportunities in fed- erated foundation models towards biomedical healthcare","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.879170Z"},"links":{"cited_paper":"/paper/2405.06784","citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:18ff4b3de450ac749a723c07ef6d9d9cf942b3f231a7593948bfd1ab1efeee3e","observation_id":"2c976661-bff1-4b48-ad87-156e54085378","resolution":{"observed_at":"2026-08-16T04:46:19.879170Z","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":"2026-08-16T04:46:19.884267Z","title":"Roberta: A robustly optimized bert pretraining approach,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.884267Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:5dacbfac26d2e9ab5941b2a848e796c0354ad8d693832273b0172f8a6fb5cfdf","observation_id":"6c074a4d-0691-4b3a-83be-9d20ac692f76","resolution":{"observed_at":"2026-08-16T04:46:19.884267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-16T04:46:19.888591Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.888591Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:13bf123154199d88bb4df141d6aa483d393b44397fe58753cc2f56497d5242ef","observation_id":"e65bbf73-9df7-40ca-96b9-55d832ac6f0f","resolution":{"observed_at":"2026-08-16T04:46:19.888591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.233897Z","title":"Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks","venue":null,"work_id":"ee2eed44-b30c-4371-b168-ada779513e9e","year":2021},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.893222Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:9b66c4440a326fc1381e9e5df8100acec607e4c42af962249d04b4355056dd11","observation_id":"6f81c30e-746f-4f57-ada3-8a59a0e3d602","resolution":{"observed_at":"2026-08-16T04:46:20.238206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.219442Z","title":"Knowledge acquired by foundation models","venue":null,"work_id":"0407c25a-55c2-4146-8556-cc807bab91fd","year":2023},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.897768Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:c610fdceeb9f5d1120034072fb11f056181339a0c146ef913aaa4b8e204c4331","observation_id":"42481f8a-21a8-4770-b61f-98847755288f","resolution":{"observed_at":"2026-08-16T04:46:20.224274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.901510Z","title":"Pytorch: An imperative style, high- performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.901510Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:4528971aa4be54fd77a58f26e3a1248b3ba3fae66bfce9f3186466bd7382944b","observation_id":"3aa68d7b-f4f2-4119-b5d3-4891032db9f0","resolution":{"observed_at":"2026-08-16T04:46:19.901510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.193421Z","title":"Improving language understanding by gen- erative pre-training","venue":null,"work_id":"8e324215-01cd-4077-856a-2a7e42758ef5","year":2018},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.907147Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:3a6861cd93d90f1e815dd426a63d2a080a3235b3dffc856056d231fa63e4a735","observation_id":"eb9f3b4c-1954-419f-bfb4-19197ebd66cd","resolution":{"observed_at":"2026-08-16T04:46:20.197634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05250","last_updated":"2016-10-11T02:42:36Z","snapshot_observed_at":"2026-08-17T14:32:22.468812Z","submitted_at":"2016-06-16T16:36:00Z","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05250","snapshot_observed_at":"2026-08-16T04:46:19.913204Z","title":"Squad: 100,000+ ques- tions for machine comprehension of text","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.913204Z"},"links":{"cited_paper":"/paper/1606.05250","citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:efff323e87854794b20d29e820c94d7c6c6f1336de983ebd4f10a65801c9554a","observation_id":"09d456ec-8103-4d45-9053-6be8479e016d","resolution":{"observed_at":"2026-08-16T04:46:19.913204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.148502Z","title":"C-lstm: Enabling efficient lstm using structured com- pression techniques on fpgas","venue":null,"work_id":"29805abd-5eec-4175-8580-9102ddb0ef8a","year":2018},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.934096Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:48162e06dbd750daa3d6a274f07f63101a5c6f35ccd4a6db06aa0be5e7f0b22b","observation_id":"378f54eb-054a-4b6f-8df8-e974cdc89a48","resolution":{"observed_at":"2026-08-16T04:46:20.157946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.128988Z","title":null,"venue":null,"work_id":"50be48c7-6419-4d87-b9c0-b79c3e07b3e0","year":2019},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.941013Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:719d76f134f773d1da17e75ddc5c6786dee076200008e9e78bc4299f88427bb3","observation_id":"c2d4a92b-6eb8-46fa-b92d-ff6756bb6cda","resolution":{"observed_at":"2026-08-16T04:46:20.136478Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.945485Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.945485Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:2c69b07f4218bcb35f1af163edcf838b679190a3a2d497175458f32fcc9ceece","observation_id":"bd3b5a2f-52a6-494b-aa7e-fa55ea68785f","resolution":{"observed_at":"2026-08-16T04:46:19.945485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.101042Z","title":"Adaptive budget allocation for parameter- efficient fine-tuning","venue":null,"work_id":"06c4b375-c4fc-4189-8cae-359f4aaf5825","year":2023},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.949729Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:e40141c2e661f8261eb5c337f848a6602b78d51759de2721fd765e0a3ac6f132","observation_id":"550a8836-5383-4e28-8f66-2c8f433fafa8","resolution":{"observed_at":"2026-08-16T04:46:20.107396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.085015Z","title":"Xing, Hao Zhang, Joseph E","venue":null,"work_id":"a0b4b89d-5c35-4988-b642-6d13ce67b501","year":2023},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.953428Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:25b8f5bd9f7c5230fa46b23115032afe0959deb4f820227a8e1e41ee745a4543","observation_id":"b7a2d187-41a2-4121-bb7e-6afecaeaa286","resolution":{"observed_at":"2026-08-16T04:46:20.091222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-16T04:46:19.816950Z","title":"Bert: Pre-training of deep bidi- rectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.816950Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:8b1ee3598d23d9866655b89034803b50333f4bd3048cb05394d315e5079f5cf0","observation_id":"4d526488-329e-40de-ac17-5528fddbb380","resolution":{"observed_at":"2026-08-16T04:46:19.816950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.282400Z","title":"Conditional adapters: Parameter-efficient transfer learning with fast in- ference","venue":null,"work_id":"96dfa2a6-89e6-49bf-b6fd-384207b1b08d","year":2023},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.870365Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:b0f384c967bbfd9ba55f2f807b101ca71e2c9f7b4ed777246f08430b9e21580f","observation_id":"0fdbcb29-5492-4811-b7db-27715761d61e","resolution":{"observed_at":"2026-08-16T04:46:20.287632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.925394Z","title":"Lst: Ladder side-tuning for parameter and mem- ory efficient transfer learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.925394Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:980e02c6ffec9482a4018471c950ffbf92bee9af4f6c54ae008ff0381c605c4b","observation_id":"1b927262-0607-46c6-9c48-da0fc6673094","resolution":{"observed_at":"2026-08-16T04:46:19.925394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.435751Z","title":"Remote sensing image scene classification: Benchmark and state of the art","venue":null,"work_id":"41e98a50-0e7c-4ff0-a6a8-3ee7177a4da3","year":2017},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.803297Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:9836fcddadcde656ca9b2cfd51f0120f8d6fa5596ee39bcea144aa8a3870b92d","observation_id":"8d72eda9-44f3-4a31-af49-6ea0832e6829","resolution":{"observed_at":"2026-08-16T04:46:20.444642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.920703Z","title":"Recursive deep models for semantic compositionality over a sentiment treebank","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.920703Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:0fd68a35423bc42888aaa9b663e95c0b5525bdbcda40193eba15ee32c024eeef","observation_id":"966bcf9f-8aae-4f3e-b621-11aabb5d31ec","resolution":{"observed_at":"2026-08-16T04:46:19.920703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.470979Z","title":"End- to-end autonomous driving: Challenges and frontiers","venue":null,"work_id":"5045be6e-bd72-40a3-8314-63331fc50491","year":2024},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.791376Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:d36f2957dda2d5cca7e4b09ef2bd303a0d1774f7ce827f31246643f8df308eb5","observation_id":"b3c4d245-8f24-4e5d-ab72-624da6fbeb5f","resolution":{"observed_at":"2026-08-16T04:46:20.476171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.821785Z","title":"Circnn: accelerat- ing and compressing deep neural networks using block- circulant weight matrices","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.821785Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:7dbec04c593bc8d5f4fd0b7d4feaa29c401332ab332eecbac7e49ddcf58d7194","observation_id":"cd6dd483-afb1-4b3e-bacd-5f239294fdca","resolution":{"observed_at":"2026-08-16T04:46:19.821785Z","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":"2026-08-16T04:46:19.848897Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.848897Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:cdfd801ba19202e60b49ad82947a359f0d0e0a86e6662a812a4886ca59b4b85f","observation_id":"5c0f7f39-7a1d-475d-9fb5-94b6093f17b9","resolution":{"observed_at":"2026-08-16T04:46:19.848897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00055","last_updated":"2017-07-31T20:12:06Z","snapshot_observed_at":"2026-08-14T20:44:07.152809Z","submitted_at":"2017-07-31T20:12:06Z","title":"SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00055","snapshot_observed_at":"2026-08-16T04:46:19.785978Z","title":"Semeval- 2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.785978Z"},"links":{"cited_paper":"/paper/1708.00055","citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:08a14be7031b34e389939b90b9b6c75e1738fa44cead6822f7ea3c4fd1d86b5a","observation_id":"565a33b9-2125-479c-89ce-dddbd8093e91","resolution":{"observed_at":"2026-08-16T04:46:19.785978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:20.324664Z","title":"Factoring matrices into the product of circulant and diagonal matrices","venue":null,"work_id":"442a9264-f064-443d-a5d5-389a0140ace6","year":2015},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.853503Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:37a1d4853f23c156e8db8ed3355cce1fb4d7d334038448bd68463b562bda27c1","observation_id":"e839b39d-4046-4f99-9411-8ceff2abe695","resolution":{"observed_at":"2026-08-16T04:46:20.328993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:46:19.929937Z","title":"Llama: Open and efficient foundation language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.929937Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:1682991c8ce9736864157d75b16d1634336341250dd61fde69b21a7901b199df","observation_id":"767822e4-e063-4bd7-8ef8-600395cb7169","resolution":{"observed_at":"2026-08-16T04:46:19.929937Z","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":"2026-08-16T04:46:19.844654Z","title":"Parameter-efficient transfer learning for nlp","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.844654Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:76b7a5295c231ce5a1ad6ee6302009dd652d5f17ab6c13c46e0453c21c811704","observation_id":"d5379eee-9602-48f2-a080-dc5f3b645c52","resolution":{"observed_at":"2026-08-16T04:46:19.844654Z","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":"2026-08-16T04:46:19.781518Z","title":"Language models are few-shot learn- ers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:19.781518Z"},"links":{"citing_paper":"/paper/2505.00580"},"observation_digest":"sha256:25e94dac21e1ac215efe71b82d25f4ea5a2a72ed4ecf93da482f07e8955d045a","observation_id":"1ab831af-6189-4734-83ea-3c0695b7be1a","resolution":{"observed_at":"2026-08-16T04:46:19.781518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.00580","last_updated":"2025-07-15T12:51:28Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-22T02:44:55.948072Z","submitted_at":"2025-05-01T15:11:46Z","title":"Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":36},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.00580."}