{"as_of":"2026-08-10T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:77fb1d297f05997c13a6de62dcd93e3a8e6ea76ef87b546021cdfebccb60462e","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:30:14.998791Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2507.10855/citation-record","integrity":"/paper/2507.10855/integrity","json":"/paper/2507.10855/citation-record.json","paper":"/paper/2507.10855"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:30:15.910699Z","title":"Decomposing and interpreting image representations via text in vits beyond CLIP","venue":null,"work_id":"192174f3-e9f3-4d06-ae56-5038f8a88ffe","year":null},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.760717Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:d5334d309ca81e415b3bdefae6ffcd201863600a74c5a7ba0572bec653467690","observation_id":"9511c46e-1fea-4a3b-adb3-c6a4f55fc39f","resolution":{"observed_at":"2026-08-06T17:30:15.914536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.900009Z","title":"A fast iterative shrinkage- thresholding algorithm for linear inverse problems","venue":null,"work_id":"5582d102-0917-40fe-9a01-e88f30128497","year":2009},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.765244Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:098da6baedf2368f0ebd58a81031a09acdbf4ee55062161509e0230ee8089d65","observation_id":"e9828c74-1039-4500-be17-5d60b142e616","resolution":{"observed_at":"2026-08-06T17:30:15.903991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-06T17:30:14.769287Z","title":"Long- former: The long-document transformer","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.769287Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:a6aad68d94ed8f548d5a45daed09b5e3df4841bf0de5a62bbc13f93ceccd5fb8","observation_id":"8b962cc0-b80a-4cc0-940d-2ff011c390ad","resolution":{"observed_at":"2026-08-06T17:30:14.769287Z","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":"2026-08-06T17:30:15.888756Z","title":"Rep- resentation learning: A review and new perspectives","venue":null,"work_id":"bb686264-1084-43a9-b60f-61c629b7fe23","year":2013},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.773856Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:37c8f85964d61fcd81b5dfed89adf223d7d514c1588adbe4b8947b281d643712","observation_id":"e633657a-3ad1-42fe-8421-9782d655f17a","resolution":{"observed_at":"2026-08-06T17:30:15.893284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.877580Z","title":"Towards monosemanticity: De- composing language models with dictionary learning","venue":null,"work_id":"74bb2eb6-a844-420c-ac2b-83e666fbf7ab","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.777447Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:8829efc9f14688bea651348de2f359b626ab943a26508859fc9b7d948ad825c2","observation_id":"af27c774-498a-4188-a472-b857cabd314b","resolution":{"observed_at":"2026-08-06T17:30:15.881287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.866472Z","title":"Compressive sampling","venue":null,"work_id":"c194f184-9934-4a66-949f-c200486d1026","year":2006},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.781358Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:f8ab68ec9c8a9d30753db3dd6a6b5818ca723c6a7c4327cf0fbe8431d1c302ec","observation_id":"5c1f3671-1906-4e2f-9f36-d72a314c457e","resolution":{"observed_at":"2026-08-06T17:30:15.870181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.855073Z","title":"Pixart- σ: Weak-to-strong training of dif- fusion transformer for 4k text-to-image generation","venue":null,"work_id":"0eda1bb0-c180-4081-9e2f-09cdecac1793","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.785501Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:dcfe3d02dcc85db82938e5fd90ebf23063e50f20457af1500e93ee52d830466d","observation_id":"aa4eea79-b944-4551-90e3-cc858248af19","resolution":{"observed_at":"2026-08-06T17:30:15.859440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.843950Z","title":"Large convolutional model tuning via filter subspace","venue":null,"work_id":"51b30be2-bd61-4a8c-a82c-d0661d0e5d8e","year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.790524Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:b26b3b5e3a34d5d33f0fb412dadc7e5a300e45e691860f016cec8db7a55def34","observation_id":"096e96c4-dabc-4ba2-8170-74f55abbc1b2","resolution":{"observed_at":"2026-08-06T17:30:15.847854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.01818","last_updated":"2020-10-11T17:07:57Z","snapshot_observed_at":"2026-08-09T12:12:23.022580Z","submitted_at":"2020-08-04T20:34:59Z","title":"Graph Convolution with Low-rank Learnable Local Filters","version":2},"cited_work":{"arxiv_id":"2008.01818","doi":null,"metadata_source":"pith","pith_arxiv_id":"2008.01818","snapshot_observed_at":"2026-08-06T17:30:15.347792Z","title":"Graph Convolution with Low-rank Learnable Local Filters","venue":"stat.ML","work_id":"ffd265be-f3e2-42be-ae3c-b582c3866c63","year":2020},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.795434Z"},"links":{"cited_paper":"/paper/2008.01818","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:06872a97d1404caf06820e254ab42fa96984953ec60200a4e6ff12c6e29fd87e","observation_id":"d69b7fec-a966-4382-8b22-dfebd119467e","resolution":{"observed_at":"2026-08-06T17:30:15.352149Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.10509","last_updated":"2019-04-23T19:29:47Z","snapshot_observed_at":"2026-08-09T19:46:04.857927Z","submitted_at":"2019-04-23T19:29:47Z","title":"Generating Long Sequences with Sparse Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.10509","snapshot_observed_at":"2026-08-06T17:30:14.800598Z","title":"Generating long sequences with sparse transformers","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.800598Z"},"links":{"cited_paper":"/paper/1904.10509","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:aedf107fbdbfdcbeb0893b0a470a9b4dcfcac4359573376138b18073c4ef4218","observation_id":"3ce6576a-d060-42a4-9d2f-853414c06544","resolution":{"observed_at":"2026-08-06T17:30:14.800598Z","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":"2026-08-06T17:30:15.833502Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":"02b290ca-38bc-45d2-bd30-a3b3b4595c6f","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.805605Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:221736b388480ecd989849e25091052d609f105d096b79602f01f8cf15a673f8","observation_id":"e8b053f3-b16f-4d1f-85f3-65e9c598153d","resolution":{"observed_at":"2026-08-06T17:30:15.837289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.823030Z","title":"Transcoders enable fine-grained interpretable circuit analy- sis for language models","venue":null,"work_id":"3de98707-cda1-4b4c-a04b-7f0ea22b9fcd","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.810458Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:16a62b07cd5c9b96b84d3bfe0df258cc921e262dd4f964d3ca20e81938f72738","observation_id":"e733b948-10e0-42db-a9fa-084f9ae63278","resolution":{"observed_at":"2026-08-06T17:30:15.827010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02410","last_updated":"2023-07-02T22:58:51Z","snapshot_observed_at":"2026-08-07T23:25:34.011944Z","submitted_at":"2022-10-05T17:32:16Z","title":"The Vendi Score: A Diversity Evaluation Metric for Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02410","snapshot_observed_at":"2026-08-06T17:30:14.814750Z","title":"The vendi score: A diversity evaluation metric for machine learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.814750Z"},"links":{"cited_paper":"/paper/2210.02410","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:903837c825cad0395d34c97c932d28411e23d70329c400ce454f099288b27923","observation_id":"7f56e7ad-4ed9-49fd-879a-daa117a23228","resolution":{"observed_at":"2026-08-06T17:30:14.814750Z","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":"2026-08-06T17:30:15.812185Z","title":"Svdiff: Compact param- eter space for diffusion fine-tuning","venue":null,"work_id":"eeacf89c-a421-419c-854a-3db6a124dca9","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.819040Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:752f3be50f803ed4f59378835e505aea713e38263c873970a98dd371c0bcee7c","observation_id":"1a028349-fa93-4346-92c9-f99bbf7d92ae","resolution":{"observed_at":"2026-08-06T17:30:15.816138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.801566Z","title":"Conceptexpress: Harnessing diffusion models for single-image unsupervised concept extraction","venue":null,"work_id":"a5061517-9755-419e-9554-3407ab286fcd","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.823427Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:3ed9c076e7ea954f2095d8135775e28801cdf8235a60d45171d47f7fbbdf63db","observation_id":"7de6e6f4-2ea0-4c63-b085-3349a69f266c","resolution":{"observed_at":"2026-08-06T17:30:15.805367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.789356Z","title":"Lora: Low- rank adaptation of large language models","venue":null,"work_id":"2fe654b6-69bb-4e4e-b82c-3424f3f6fb7e","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.827441Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:02eaa572eb327266e6b786a0dcf8ab355ce3e5e9ead0c38b3e799b0c6544d3ee","observation_id":"3a2ce792-89af-4915-befe-4a32437de344","resolution":{"observed_at":"2026-08-06T17:30:15.793795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06633","last_updated":"2025-04-02T14:16:22Z","snapshot_observed_at":"2026-07-06T19:13:16.067395Z","submitted_at":"2024-09-10T16:44:47Z","title":"SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation","version":2},"cited_work":{"arxiv_id":"2409.06633","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.06633","snapshot_observed_at":"2026-08-06T17:30:15.306698Z","title":"SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation","venue":"cs.CV","work_id":"94384e52-0ad6-49a6-95d5-3699fb9ad7f0","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.830860Z"},"links":{"cited_paper":"/paper/2409.06633","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:8e9043a07c8a0a0d02b27af80c3f753d7c66665eb25891f7ad0fe6eedd85794c","observation_id":"6243c206-58f7-4441-873e-e681dc3c66d9","resolution":{"observed_at":"2026-08-06T17:30:15.310873Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.777839Z","title":"Text embed- ding is not all you need: Attention control for text-to-image semantic alignment with text self-attention maps","venue":null,"work_id":"7ff3a862-9830-43da-981c-73fd8dfeb098","year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.835088Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:ea1f9d606bdc6864d3f94c7e1fbc6fe4b23b0cabb4d6beceea77540ff5b7eebd","observation_id":"d56e6243-efed-47b1-91b9-b4f6a5e88808","resolution":{"observed_at":"2026-08-06T17:30:15.782104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10058","last_updated":"2024-10-14T00:53:59Z","snapshot_observed_at":"2026-07-06T19:32:46.742766Z","submitted_at":"2024-10-14T00:53:59Z","title":"Learning to Customize Text-to-Image Diffusion In Diverse Context","version":1},"cited_work":{"arxiv_id":"2410.10058","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.10058","snapshot_observed_at":"2026-08-06T17:30:15.288530Z","title":"Learning to Customize Text-to-Image Diffusion In Diverse Context","venue":"cs.CV","work_id":"e887592b-56b7-421c-a16e-e86e9bd66262","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.838687Z"},"links":{"cited_paper":"/paper/2410.10058","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:7c9de94a3d144f761c985354eb718b4a1ea5043aed4c05b20b2305efb062eb86","observation_id":"30ca9e2b-c1cb-4e00-8ac2-9606dd80a881","resolution":{"observed_at":"2026-08-06T17:30:15.293353Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.765368Z","title":"An introduction to variational autoencoders","venue":null,"work_id":"9f0ceee4-ff78-43d8-9954-d8b17d639a7f","year":2019},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.842850Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:e3b79f381ed916e9082d7933938f77b2d16e487f5c21dca3752c4a31811b436f","observation_id":"3835d217-b7bd-41d3-8148-244890f980a7","resolution":{"observed_at":"2026-08-06T17:30:15.769840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:14.846620Z","title":"Segment any- thing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.846620Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:9929b59aa03d9b39612295ead4f4f9da1f4f5677186d0b1ba91baf7d1747ad38","observation_id":"a66c29ff-09fc-40b2-ab90-2e6810ee15d6","resolution":{"observed_at":"2026-08-06T17:30:14.846620Z","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":"2026-08-06T17:30:15.745949Z","title":"Multi-concept customization of text-to-image diffusion","venue":null,"work_id":"8bdbc6e7-fceb-4890-a07a-8a2e71b66c9c","year":null},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.850785Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:b70155e7d7676ab89dae964710740dc6780834f795ee17e3c8e9e47cfde43935","observation_id":"ad84e6bb-dc24-44e0-a49a-789dcff9a340","resolution":{"observed_at":"2026-08-06T17:30:15.749955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.20766","last_updated":"2025-02-28T06:34:53Z","snapshot_observed_at":"2026-08-07T17:40:46.337382Z","submitted_at":"2025-02-28T06:34:53Z","title":"FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.20766","snapshot_observed_at":"2026-08-06T17:30:14.855194Z","title":"Flexprefill: A context-aware sparse attention mech- anism for efficient long-sequence inference","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.855194Z"},"links":{"cited_paper":"/paper/2502.20766","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:06c538bcbc0e8380a37981bb3456258ab34893ea1cd3b1bfd66d11c4cc579a35","observation_id":"da99d513-7753-4c4a-9aec-2bf4bf27ee7d","resolution":{"observed_at":"2026-08-06T17:30:14.855194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09353","last_updated":"2024-07-09T05:59:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-14T17:59:34Z","title":"DoRA: Weight-Decomposed Low-Rank Adaptation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09353","snapshot_observed_at":"2026-08-06T17:30:14.859810Z","title":"Dora: Weight-decomposed low-rank adaptation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.859810Z"},"links":{"cited_paper":"/paper/2402.09353","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:3f4fc992ccb3a354be66129fcf40910fdf03da0d50a811e3f6898b06ebc1702f","observation_id":"a0c2c29f-1e56-4d25-85f1-c33a7507450e","resolution":{"observed_at":"2026-08-06T17:30:14.859810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00723","last_updated":"2025-03-20T21:26:06Z","snapshot_observed_at":"2026-08-07T17:36:31.638572Z","submitted_at":"2025-03-02T04:11:03Z","title":"Re-Imagining Multimodal Instruction Tuning: A Representation View","version":3},"cited_work":{"arxiv_id":"2503.00723","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.00723","snapshot_observed_at":"2026-08-06T17:30:15.247415Z","title":"Re-Imagining Multimodal Instruction Tuning: A Representation View","venue":"cs.LG","work_id":"fa417d44-661b-4c26-8834-be4a7977c819","year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.864435Z"},"links":{"cited_paper":"/paper/2503.00723","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:862a56f57e7311b8fc5c7d79e10e5e5a92cb0709fb2424ab4306c825063581c7","observation_id":"4b1c59e3-0870-4802-8836-45fd52964225","resolution":{"observed_at":"2026-08-06T17:30:15.251852Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02047","last_updated":"2023-08-07T06:21:31Z","snapshot_observed_at":"2026-07-06T15:50:26.113234Z","submitted_at":"2023-07-05T06:05:36Z","title":"CAME: Confidence-guided Adaptive Memory Efficient Optimization","version":2},"cited_work":{"arxiv_id":"2307.02047","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.02047","snapshot_observed_at":"2026-08-06T17:30:15.223380Z","title":"CAME: Confidence-guided Adaptive Memory Efficient Optimization","venue":"cs.CL","work_id":"c58d72e4-e2d5-44d3-90d5-2666c94c3dc6","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.868394Z"},"links":{"cited_paper":"/paper/2307.02047","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:f63ff7e1758aae5dd61dbb2b3cad2f154f1f5ccb430cacaa50fbeb3e82578045","observation_id":"73856219-331c-496e-bcdb-fb41f05e0c1f","resolution":{"observed_at":"2026-08-06T17:30:15.228311Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.734487Z","title":"Supervised dictionary learning","venue":null,"work_id":"931d6fb1-2a6e-43e9-a777-65987302cbea","year":2008},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.872276Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:7e9873b1930b827be0221a2073c631dcfc694c6a34de11b2d6cb2e54d6f06dab","observation_id":"d70aca7b-cc9b-47dd-a216-6d314df84f84","resolution":{"observed_at":"2026-08-06T17:30:15.738523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19647","last_updated":"2025-03-27T05:44:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-28T17:56:07Z","title":"Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.19647","snapshot_observed_at":"2026-08-06T17:30:14.876081Z","title":"Sparse feature cir- cuits: Discovering and editing interpretable causal graphs in language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.876081Z"},"links":{"cited_paper":"/paper/2403.19647","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:250856300a204aa6962a1fc30a96574e142fa4343339b1a2c2e418d12636f7d7","observation_id":"bb28413e-c0a7-476d-a41b-ae3380648e15","resolution":{"observed_at":"2026-08-06T17:30:14.876081Z","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":"2026-08-06T17:30:15.725010Z","title":"Con- tinual learning with filter atom swapping","venue":null,"work_id":"8af9ab2d-8d7d-4a70-9bdc-47a38926bf90","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.880418Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:97fbd6f174bd72feed3a2219457fb11f41f260435fb41e93e240d29969072752","observation_id":"9e79c806-f05c-446c-90c1-3fbdd0f7001d","resolution":{"observed_at":"2026-08-06T17:30:15.728267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.714281Z","title":"Spatiotemporal joint filter decomposition in 3d convolutional neural networks","venue":null,"work_id":"68e68f43-4154-4554-9683-7eac08188f1c","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.884423Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:ac7bfb81d311e3e1c5b809044c382176e38a38678901b6bbe064ed745ddde04d","observation_id":"d9a52c6a-4468-409f-8888-94d639654a8a","resolution":{"observed_at":"2026-08-06T17:30:15.718494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.703259Z","title":"Training diffusion models towards diverse image generation with reinforcement learning","venue":null,"work_id":"bfc540fa-f7ed-4bda-92e1-9f69700c025a","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.888677Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:41148b76224e2f9d1705aadf1efc84ece6f317f42a3c83cd3c747f6f226fb79e","observation_id":"ad1d9996-b7bf-4c0c-9e6f-f041426bb985","resolution":{"observed_at":"2026-08-06T17:30:15.706928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03190","last_updated":"2025-03-03T04:11:46Z","snapshot_observed_at":"2026-08-09T03:35:16.715476Z","submitted_at":"2024-10-04T07:05:16Z","title":"Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03190","snapshot_observed_at":"2026-08-06T17:30:14.892909Z","title":"Tuning timestep- distilled diffusion model using pairwise sample optimiza- tion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.892909Z"},"links":{"cited_paper":"/paper/2410.03190","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:7bdeb7096a2c108799dd38cfca3d54ffe8c6fea87750fdc89571da4f0d0e3502","observation_id":"a2cac26c-8e79-4f7d-9e21-ecd4ccb78517","resolution":{"observed_at":"2026-08-06T17:30:14.892909Z","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":"2026-08-06T17:30:15.690716Z","title":"Coeff-tuning: A graph filter subspace view for tuning attention-based large models","venue":null,"work_id":"344eb199-93c9-4751-bf7f-14cdc2328a56","year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.897163Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:26b8bf7c431e89cb894c83f46727dda1a9ab518c8755e107968c49b1d5c50086","observation_id":"047baf86-a9dc-4380-b604-eefe4fe28c9d","resolution":{"observed_at":"2026-08-06T17:30:15.695264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.679195Z","title":"Emergence of simple- cell receptive field properties by learning a sparse code for natural images","venue":null,"work_id":"fca9c693-fe43-48ed-ae13-916291e02814","year":1996},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.900927Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:9bcc6268227f804164b5fbc02b1f58d8a97aa353da6d59d7beff1b9696839d96","observation_id":"4226990e-0c4e-420d-bfd4-4b963543249b","resolution":{"observed_at":"2026-08-06T17:30:15.683911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-09T18:02:17.307812Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-06T17:30:14.904940Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.904940Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:269dae3efe92f6ad0616cc29d3d1f91c76b78db1ca3f2ed925d5191f609d1dc7","observation_id":"00d3af64-ebc0-49a5-9126-3751ca30ec2f","resolution":{"observed_at":"2026-08-06T17:30:14.904940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02147","last_updated":"2025-02-01T16:45:14Z","snapshot_observed_at":"2026-08-09T21:37:48.059948Z","submitted_at":"2024-10-03T02:12:03Z","title":"Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement","version":2},"cited_work":{"arxiv_id":"2410.02147","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.02147","snapshot_observed_at":"2026-08-06T17:30:15.167910Z","title":"Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement","venue":"cs.LG","work_id":"13ccc186-89c7-49de-8f10-25422ddb7649","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.909776Z"},"links":{"cited_paper":"/paper/2410.02147","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:bc372ed52aa4e6a37f0da64a947e900bebe6b47ea15af9deabf6160e7b946ee4","observation_id":"129da327-983e-4cae-894e-57db7ff3ddf4","resolution":{"observed_at":"2026-08-06T17:30:15.173147Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.564769Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":"d6d33722-05ca-41b4-8fe1-611484771438","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.914154Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:f2df59e738e3004c994c4250e0fc0694fcfb9f74a84a0545e2584f4e8b9c14c9","observation_id":"82d6a9a0-8b77-4e86-b3d9-e90d4894b5a9","resolution":{"observed_at":"2026-08-06T17:30:15.569159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02078","last_updated":"2024-10-02T22:57:47Z","snapshot_observed_at":"2026-07-06T19:26:38.002071Z","submitted_at":"2024-10-02T22:57:47Z","title":"Posterior sampling via Langevin dynamics based on generative priors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02078","snapshot_observed_at":"2026-08-06T17:30:14.917980Z","title":"Posterior sampling via langevin dynamics based on generative priors.arXiv preprint arXiv:2410.02078, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.917980Z"},"links":{"cited_paper":"/paper/2410.02078","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:cadf7c34b1dd9d8b737356c84ceff4f65a29b38033e2a1519ad1e44123ae1f01","observation_id":"08d02a7f-6875-4edd-a70e-3a8a6c5bbcd4","resolution":{"observed_at":"2026-08-06T17:30:14.917980Z","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":"2026-08-06T17:30:15.553271Z","title":"Dcfnet: Deep neural network with decomposed convolutional filters","venue":null,"work_id":"c8b92b2a-d865-4b26-8589-ecac555fb81f","year":2018},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.921851Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:beab106858f2bfb185422ea488541d37d6f08d343265acf36278798fe1989f7f","observation_id":"9d05a825-ac72-4027-9e9a-4b07fed1450b","resolution":{"observed_at":"2026-08-06T17:30:15.557582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.542140Z","title":"Controlling text-to-image diffusion by orthogo- nal finetuning","venue":null,"work_id":"f7a6783b-82e7-47a0-b29f-12fd0f72db8a","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.926623Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:752d96e3cacf79a7ae8cb4c6c4c3d80a93442464e165232bbbbdc6f1f3651e63","observation_id":"0dbe1858-2f38-4fc6-adc9-797e0d1c4f37","resolution":{"observed_at":"2026-08-06T17:30:15.546721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.530575Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"0d6a5fcd-6911-4eb5-853a-b73ae487adc0","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.930569Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:810e57dcef8bb48afa7f41f1fbb6f7f49b4220ddaa829d45c0920e002c55f42e","observation_id":"ace7e34a-7272-4691-b644-997b24177ea2","resolution":{"observed_at":"2026-08-06T17:30:15.534676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.518965Z","title":"Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention","venue":null,"work_id":"fc87d693-4899-4646-9724-5b0e53799013","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.934714Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:eec264f7aa0d2560d37d27e3c59ebbae1ce5054f8352acea88618aec627386d1","observation_id":"876665a2-20a8-42e7-8e31-45eaa8af2c1d","resolution":{"observed_at":"2026-08-06T17:30:15.522994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:14.938952Z","title":"High-resolution image syn- thesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.938952Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:be47fe89082ddb1f73e2ee9d7e7c1f6ddde3244db0bb603ccc67b6a05d32d9ac","observation_id":"b318066c-1caa-4b6d-89c2-7b4135f8deb3","resolution":{"observed_at":"2026-08-06T17:30:14.938952Z","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":"2026-08-06T17:30:15.501776Z","title":"Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation","venue":null,"work_id":"06d3c424-525e-4848-b775-f86c6c4a08cb","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.943156Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:eb461803fb1ef6bdef349ccbdaa86a5cf743849ae9900142aebb81f125bcf41e","observation_id":"eae68459-335e-42c9-9f79-2dd68d48081f","resolution":{"observed_at":"2026-08-06T17:30:15.505290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:14.947269Z","title":"Unpacking sdxl turbo: Interpreting text-to-image models with sparse au- toencoders","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.947269Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:4ce4561f6281cbc5181188c49ca8dc69be469a534e447c37f2757c565130c497","observation_id":"f54b1b02-f063-4457-903f-236c4c0922c6","resolution":{"observed_at":"2026-08-06T17:30:14.947269Z","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":"2026-08-06T17:30:15.490706Z","title":"Sparse sinkhorn attention","venue":null,"work_id":"19afa7d3-c14c-48d1-a3a6-8384b42339b1","year":2020},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.951564Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:85734fde249cd612e079d784c68120fc28230381d60b1ed31e59a719f14b35bb","observation_id":"29c649b9-94a5-4f6a-a97d-e665b6fa74cc","resolution":{"observed_at":"2026-08-06T17:30:15.494150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01659","last_updated":"2025-02-07T13:44:24Z","snapshot_observed_at":"2026-08-10T17:26:01.178143Z","submitted_at":"2025-01-31T22:05:00Z","title":"Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques","version":2},"cited_work":{"arxiv_id":"2502.01659","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.01659","snapshot_observed_at":"2026-08-06T17:30:15.054198Z","title":"Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques","venue":"cs.LG","work_id":"c39bb327-b93d-444f-8f57-cf9bbd4c08d0","year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.955831Z"},"links":{"cited_paper":"/paper/2502.01659","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:9d8ff4a83569b17163a229148a65a7b10c354b25ef1bd7cb660de6a53640b315","observation_id":"68793c4a-d8bc-43c8-b927-4c223eb6b933","resolution":{"observed_at":"2026-08-06T17:30:15.058609Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.478380Z","title":"Attention is all you need","venue":null,"work_id":"d1aa877a-c319-4657-9d83-36af522532a8","year":2017},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.959840Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:c388b2e95303e6568da6119c3d57480cda032e01fcb21c0c62d0cc85d86f2e8d","observation_id":"dad8f7eb-841b-43dd-87e0-f4984279b962","resolution":{"observed_at":"2026-08-06T17:30:15.482518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11286","last_updated":"2020-02-24T19:35:47Z","snapshot_observed_at":"2026-08-09T17:04:09.186413Z","submitted_at":"2019-09-25T04:37:38Z","title":"Stochastic Conditional Generative Networks with Basis Decomposition","version":2},"cited_work":{"arxiv_id":"1909.11286","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.11286","snapshot_observed_at":"2026-08-06T17:30:15.033775Z","title":"Stochastic Conditional Generative Networks with Basis Decomposition","venue":"cs.CV","work_id":"a2878599-53cd-4d74-a066-943ef170f5aa","year":2019},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.963651Z"},"links":{"cited_paper":"/paper/1909.11286","citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:c8cc515a007fc4601dd89f2a5ae7f5f71db4c7a331e84db959c273ca22c1b20d","observation_id":"dbcc7baf-124c-4b95-9dd1-785ae1449fe4","resolution":{"observed_at":"2026-08-06T17:30:15.040899Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.465816Z","title":"Image generation using continuous filter atoms","venue":null,"work_id":"1bfe34e4-8845-442e-b017-7c37134734a1","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.967362Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:a7e4f0b8eee097229912dba2899118f762ef708ec3fe10b625575528d94185c0","observation_id":"8879c03b-fe14-48c9-8b58-fae899a69f9f","resolution":{"observed_at":"2026-08-06T17:30:15.470459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.454044Z","title":"Adaptive convolutions with per-pixel dynamic filter atom","venue":null,"work_id":"2081377f-929a-4189-9017-961156972e57","year":2021},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.970950Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:1fe0f576b8abc029a21643818a6836edb1a4e26a06f50b3fc9edac8d7d5ef040","observation_id":"1cde644b-e6fb-42af-8dd7-e72b3aa0e45b","resolution":{"observed_at":"2026-08-06T17:30:15.458829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.443057Z","title":"Advancing parameter efficiency in fine-tuning via representation editing","venue":null,"work_id":"b5f95254-e945-40eb-b7fb-4f7d69bc96d9","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.974721Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:0c823bb690c4697dc38b9c650a961483ba73428ff599842fe9cdb78bad86a7a0","observation_id":"5148cfb5-adff-4abc-963d-589070319bdd","resolution":{"observed_at":"2026-08-06T17:30:15.446692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.432258Z","title":"Reft: Representation finetuning for language models","venue":null,"work_id":"659163f3-a4f7-4350-b8f7-817702b44cc3","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.978232Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:dc6a3925e23ef7d0212295aec7ebf511cc02eb649912d188adf4e15d1e642792","observation_id":"8b95fe18-a91b-4d50-abd3-f9d3c7096558","resolution":{"observed_at":"2026-08-06T17:30:15.435755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.420679Z","title":"Imagere- ward: Learning and evaluating human preferences for text- to-image generation","venue":null,"work_id":"ef143d9e-0c48-4309-9b6c-3ebd940ef23f","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.981779Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:ffa6ae79090e5d7b85c44595716e4842ab85e3f92078cd18ad515baac1ba9a95","observation_id":"bba9694a-4656-4932-b54f-0b645d47c6db","resolution":{"observed_at":"2026-08-06T17:30:15.425238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:14.985479Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.985479Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:e66be49440462c0314ec7eb309167dc5c4fb02182e65494830fb810e0a0ffb30","observation_id":"316a5f64-7c05-4e57-bef7-91016736f517","resolution":{"observed_at":"2026-08-06T17:30:14.985479Z","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":"2026-08-06T17:30:15.400028Z","title":"Enhancing semantic fidelity in text-to-image synthesis: Attention regulation in diffusion models","venue":null,"work_id":"3f986f98-6b8c-40ff-9f4b-203434626f68","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.989372Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:bbb2153d0c259420011ba446d1301a6540c9a20bd16dcafac7b01dd21e77ccb9","observation_id":"8bafb27a-d84f-488f-86c2-879c47e3b7a2","resolution":{"observed_at":"2026-08-06T17:30:15.404694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.386628Z","title":"Object- conditioned energy-based attention map alignment in text-to- image diffusion models","venue":null,"work_id":"18a7e189-e24a-4c04-9a1c-9638d6b441a4","year":2024},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.993979Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:35b2742d44cfef8631a2fbaa12e1f1d1d2aa5b72b705e1b72cd737ae15ffef5f","observation_id":"bd515564-5b75-46c1-9d9f-cf33c271640c","resolution":{"observed_at":"2026-08-06T17:30:15.391151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T17:30:15.374510Z","title":"A grey ⟨V ⟩ wolf plushie","venue":null,"work_id":"e8e2c4f3-807a-4be8-8a67-3d0eab5755c9","year":2023},"citing_paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:30:14.998791Z"},"links":{"citing_paper":"/paper/2507.10855"},"observation_digest":"sha256:a0f8b1a7884af753c6d28fcf5efb7a3ff031c707f5508062179be45f2a3d3622","observation_id":"a9bd85a6-a7e8-4dd8-88cf-71992dbb7594","resolution":{"observed_at":"2026-08-06T17:30:15.378776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.10855","last_updated":"2025-07-14T23:03:24Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T06:59:39.901612Z","submitted_at":"2025-07-14T23:03:24Z","title":"Sparse Fine-Tuning of Transformers for Generative Tasks"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":8,"verified_fuzzy":37},"total_outbound_references":58},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2507.10855."}