{"as_of":"2026-08-14T06:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3d9ceae2eb7aecfe1fc109798773c39ee2680a739df71a2ef375b1cd37a022d0","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:49:02.568731Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2412.02220/citation-record","integrity":"/paper/2412.02220/integrity","json":"/paper/2412.02220/citation-record.json","paper":"/paper/2412.02220"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:49:04.126517Z","title":"Meta-adapters: Parameter ef- ficient few-shot fine-tuning through meta-learning","venue":null,"work_id":"641f83c2-98b5-4a1f-997f-59bf92de2e5e","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.127279Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:8f4d1772ae122ca20aab91b6c0319a10aa6cb628ab829ee0f4406f0f28746bff","observation_id":"0398907c-d6d2-47b2-a837-b297d1ec5d04","resolution":{"observed_at":"2026-08-11T23:49:04.131906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.08136","last_updated":"2019-07-24T14:43:31Z","snapshot_observed_at":"2026-08-10T12:54:21.108689Z","submitted_at":"2018-05-21T15:44:51Z","title":"Meta-learning with differentiable closed-form solvers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.08136","snapshot_observed_at":"2026-08-11T23:49:02.134452Z","title":"Meta-learning with differentiable closed-form solvers","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.134452Z"},"links":{"cited_paper":"/paper/1805.08136","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:49a9b3eb5be235125868ba93ca298053d7f4b73af3525fd77b2eea3b6513a7e6","observation_id":"6d44be06-e06a-4cf7-8e62-654c471a8646","resolution":{"observed_at":"2026-08-11T23:49:02.134452Z","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":"2026-08-11T23:49:02.142979Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.142979Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:74d09260ea6fd1d380793f01b5035ac2addcbe4b3869d0b3d360c2022cf4c036","observation_id":"2773f58c-774f-4cf9-913a-1b49a686fb92","resolution":{"observed_at":"2026-08-11T23:49:02.142979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.10544","last_updated":"2020-08-25T15:45:55Z","snapshot_observed_at":"2026-08-13T08:31:39.798197Z","submitted_at":"2020-05-21T09:55:26Z","title":"Cross-Domain Few-Shot Learning with Meta Fine-Tuning","version":4},"cited_work":{"arxiv_id":"2005.10544","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.10544","snapshot_observed_at":"2026-08-11T23:49:03.035153Z","title":"Cross-Domain Few-Shot Learning with Meta Fine-Tuning","venue":"cs.CV","work_id":"243937c9-7f96-409d-817a-a3affcdc71b6","year":2020},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.149775Z"},"links":{"cited_paper":"/paper/2005.10544","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:9294ceebe4810fbf52934f85629609174ac1e57fa03c71a780bdd51621df4315","observation_id":"759da982-b367-4826-b978-d2bec3875ee3","resolution":{"observed_at":"2026-08-11T23:49:03.042628Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16160","last_updated":"2024-01-30T15:44:58Z","snapshot_observed_at":"2026-08-13T04:32:42.021920Z","submitted_at":"2024-01-29T13:48:36Z","title":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16160","snapshot_observed_at":"2026-08-11T23:49:02.155342Z","title":"Llava-mole: Sparse mixture of lora experts for mitigating data con- flicts in instruction finetuning mllms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.155342Z"},"links":{"cited_paper":"/paper/2401.16160","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:a6fef8092260355de5bb9ee7a63428bd240739079e6a9bf4ae2b8c8adfec4882","observation_id":"6164bfe4-553e-4a0e-954a-1ee7e3d32d9a","resolution":{"observed_at":"2026-08-11T23:49:02.155342Z","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":"2026-08-11T23:49:04.090156Z","title":"Meta-learning via language model in-context tuning","venue":null,"work_id":"0d08f6c1-1153-4651-a3a4-6a281da630d0","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.161410Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:ea370fba7c28b9372bd1c4d6bd7a6d2ba47d824b6bf73257345449f31dbda0f2","observation_id":"98f0a992-d845-4112-bafd-bc6b50459adb","resolution":{"observed_at":"2026-08-11T23:49:04.096899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11416","last_updated":"2022-12-06T21:39:48Z","snapshot_observed_at":"2026-07-06T14:08:18.855958Z","submitted_at":"2022-10-20T16:58:32Z","title":"Scaling Instruction-Finetuned Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.11416","snapshot_observed_at":"2026-08-11T23:49:02.167253Z","title":"Scaling instruction-finetuned language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.167253Z"},"links":{"cited_paper":"/paper/2210.11416","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:5826c2f8edc91e4fee273801d179c50801f7519d6f7634e28cafdc597d4cde16","observation_id":"9c0105b8-6747-40e2-9375-8cb96af366c5","resolution":{"observed_at":"2026-08-11T23:49:02.167253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10559","last_updated":"2023-05-15T11:45:12Z","snapshot_observed_at":"2026-08-13T13:17:49.818952Z","submitted_at":"2022-12-20T18:58:48Z","title":"Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10559","snapshot_observed_at":"2026-08-11T23:49:02.172820Z","title":"Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.172820Z"},"links":{"cited_paper":"/paper/2212.10559","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:16d15a03ccd0a2c10c8bc4f62f459b9d6319024c85ee1f6535b89e4e5e182860","observation_id":"6302d483-60fa-4130-8c41-ea088a873d2a","resolution":{"observed_at":"2026-08-11T23:49:02.172820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00234","last_updated":"2024-10-05T11:47:02Z","snapshot_observed_at":"2026-07-06T14:36:25.690733Z","submitted_at":"2022-12-31T15:57:09Z","title":"A Survey on In-context Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00234","snapshot_observed_at":"2026-08-11T23:49:02.178226Z","title":"A survey on in-context learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.178226Z"},"links":{"cited_paper":"/paper/2301.00234","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:34e1d52a989f3aed416a9ccffeb0fd80c7f7e39f3c83f61d4461810dee31666a","observation_id":"55d85c64-64ef-4bd7-bef9-1f6995a75538","resolution":{"observed_at":"2026-08-11T23:49:02.178226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.08584","last_updated":"2021-05-18T15:13:00Z","snapshot_observed_at":"2026-08-08T13:22:47.133749Z","submitted_at":"2021-05-18T15:13:00Z","title":"Contrastive Model Inversion for Data-Free Knowledge Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.08584","snapshot_observed_at":"2026-08-11T23:49:02.184624Z","title":"Contrastive model inver- sion for data-free knowledge distillation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.184624Z"},"links":{"cited_paper":"/paper/2105.08584","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:faf52820453bbbdd8f42c3a88c3f8243772f6168c980e068acee55a7a8654610","observation_id":"4397f209-fc49-4de8-9e79-5a580d1374fa","resolution":{"observed_at":"2026-08-11T23:49:02.184624Z","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":"2026-08-11T23:49:04.068678Z","title":"Up to 100x faster data- free knowledge distillation","venue":null,"work_id":"343809a3-ed64-4b60-af26-992a2493804f","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.190260Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:89ae31d162489388e5b0cfbc4acbefe5d7c04bff94d0f58b9b88947f72979688","observation_id":"63bccb93-515f-471f-a58e-0cb33bd1cbfb","resolution":{"observed_at":"2026-08-11T23:49:04.075600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10971","last_updated":"2024-03-25T23:14:28Z","snapshot_observed_at":"2026-08-13T05:47:54.531511Z","submitted_at":"2023-10-17T03:35:27Z","title":"Context-Aware Meta-Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10971","snapshot_observed_at":"2026-08-11T23:49:02.195611Z","title":"Context-aware meta-learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.195611Z"},"links":{"cited_paper":"/paper/2310.10971","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:24a929425b844292bfb6c11df3c40cee74032cceb7b097d6fc1ead89c4e7d454","observation_id":"5708a13d-bfd0-4324-b240-d6de891addbe","resolution":{"observed_at":"2026-08-11T23:49:02.195611Z","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":"2026-08-11T23:49:04.042164Z","title":"Model- agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":"14c8f978-8d6b-4570-bb77-946499b27b5f","year":2017},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.203090Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:8b65bb0d6f77978d91ea62b6f437af098e3e4c68d5ab6e237fcb4d45fdc7731f","observation_id":"9576cb3c-48e7-493d-b7a8-65e8812bf5b1","resolution":{"observed_at":"2026-08-11T23:49:04.048319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:04.022075Z","title":"Styleadv: Meta style adversarial training for cross-domain few-shot learning","venue":null,"work_id":"ea5e4327-3124-4264-9e79-d83b49ec2186","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.208766Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:9a0c0bc0ee30061a8600a4fa3c3a39a853c8b951d8d9510c4ce589ccbe699f46","observation_id":"be3b4612-f74a-4243-ac22-231e943c781b","resolution":{"observed_at":"2026-08-11T23:49:04.028401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:04.003320Z","title":"On the effectiveness of parameter-efficient fine-tuning","venue":null,"work_id":"43da96b1-b670-4b53-93db-9138d22a1d7e","year":null},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.213937Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:b780bc15f3175011f038e2747e3e491a126383ccef71fa3a918c6b45193de6e5","observation_id":"e0814a38-e52c-4204-ba05-994e92b0de07","resolution":{"observed_at":"2026-08-11T23:49:04.008522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:02.219408Z","title":"Clip-adapter: Better vision-language models with feature adapters","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.219408Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:cbf23b1c9eb327463a7148a9e77f42907073c5a737d013abdd16462ebdf904f8","observation_id":"b20f672c-0898-4b0f-8207-528475496e7d","resolution":{"observed_at":"2026-08-11T23:49:02.219408Z","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":"2026-08-11T23:49:03.965368Z","title":"Know where you’re going: Meta-learning for parameter-efficient fine-tuning","venue":null,"work_id":"43698f74-28f6-4ab6-ac63-e479ed692811","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.226066Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:062de8ee4f317512b8e002badd3d0692183f84f9e116c092aa35f116191c1f81","observation_id":"b2a36af5-b307-45bf-aba0-ef00fd7bb434","resolution":{"observed_at":"2026-08-11T23:49:03.972440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12379","last_updated":"2024-07-04T02:55:57Z","snapshot_observed_at":"2026-08-14T01:02:48.299992Z","submitted_at":"2023-12-19T18:11:19Z","title":"Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.12379","snapshot_observed_at":"2026-08-11T23:49:02.231977Z","title":"Mixture of cluster-conditional lora experts for vision-language instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.231977Z"},"links":{"cited_paper":"/paper/2312.12379","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:e316980405be00366542d0da72c14b9250172c51694debd7f723f012c09b0389","observation_id":"3df9cc27-a4e3-4023-aa38-fa02beff9771","resolution":{"observed_at":"2026-08-11T23:49:02.231977Z","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":"2026-08-11T23:49:03.943160Z","title":"A broader study of cross-domain few-shot learning","venue":null,"work_id":"b8ac7810-d65c-40a6-9930-5bfd9a57978c","year":2020},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.239064Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:d000cf34447f69b0e742bae6edd426b1614a72314263a0c2ff55639c85c05a4e","observation_id":"7376e1aa-cfe5-42e4-9844-59777a4178c4","resolution":{"observed_at":"2026-08-11T23:49:03.950577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.920938Z","title":"Gradvit: 9 Gradient inversion of vision transformers","venue":null,"work_id":"11148183-aec7-4acc-b129-1fc8163334f0","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.244981Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:71bffe41bc971b6fd902ec8d7cac9b785edea1d2829ca6765105d5124afbfab8","observation_id":"09762856-406f-42fc-9ed0-c29637a2a080","resolution":{"observed_at":"2026-08-11T23:49:03.927687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04366","last_updated":"2022-02-02T16:39:23Z","snapshot_observed_at":"2026-08-13T17:56:57.877812Z","submitted_at":"2021-10-08T20:22:26Z","title":"Towards a Unified View of Parameter-Efficient Transfer Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04366","snapshot_observed_at":"2026-08-11T23:49:02.250584Z","title":"Towards a unified view of parameter-efficient transfer learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.250584Z"},"links":{"cited_paper":"/paper/2110.04366","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:39d9b9b745077c1d343cf4be191dfb27dca978665b8677349a722aa793426998","observation_id":"8bb711c1-9f0f-4ea8-8aa8-ee0f09296f97","resolution":{"observed_at":"2026-08-11T23:49:02.250584Z","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":"2026-08-11T23:49:03.898511Z","title":"Towards a unified view of parameter-efficient transfer learning","venue":null,"work_id":"707ea0fd-8d88-4262-bf47-36780f997dce","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.258909Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:053dae3b362314123636d5c2b8a61aa2bad60424e4baff451bdd467087bd3433","observation_id":"acd18b08-8764-46a5-8a69-7c68abb8d055","resolution":{"observed_at":"2026-08-11T23:49:03.905735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.875163Z","title":"Revisiting data-free knowledge distilla- tion with poisoned teachers","venue":null,"work_id":"641c3c42-2049-48bf-bfcf-3ab594f26f97","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.264946Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:49d861de20e32e60bfe31e4bb3712d2e6e107f2f8fe977e2ede41b2fa107aa5f","observation_id":"96f52803-948f-47de-8db4-c5c112cbd286","resolution":{"observed_at":"2026-08-11T23:49:03.881719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.855580Z","title":"Meta- learning the difference: preparing large language models for efficient adaptation","venue":null,"work_id":"658cf7b3-94a5-4744-8990-771e787a51d7","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.275105Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:e08f2ed9b5f1dbd82102900bfea780c11ca0d049828b04462ec8ebc5c53000b9","observation_id":"afa819e1-16ab-484d-97a8-9495e1172286","resolution":{"observed_at":"2026-08-11T23:49:03.862353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:02.280736Z","title":"Parameter-efficient transfer learning for nlp","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.280736Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:f8778d14727e29a4d2292c25cfcc8fd585c2cddc541ff460182975288f38fff7","observation_id":"43933b22-cd7b-44c2-ad0f-780c6454d2ae","resolution":{"observed_at":"2026-08-11T23:49:02.280736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-11T23:49:02.286143Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.286143Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:58a0e222f5446d755dab6af71027d6e8d6897eff8ee5cec2c8db2ec52b18b897","observation_id":"0fdfb6b8-2834-4cfa-89ad-e0a1858fcac6","resolution":{"observed_at":"2026-08-11T23:49:02.286143Z","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":"2026-08-11T23:49:03.821814Z","title":"Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference","venue":null,"work_id":"b00a2610-fcef-404c-9d3c-473bcd5e2d9b","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.291901Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:f81b294731524874c77551b0597c29628e62788d950f9f56fb3802a950af86f5","observation_id":"66314701-0626-40e1-a278-d60b7440024f","resolution":{"observed_at":"2026-08-11T23:49:03.828602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.802276Z","title":"Sparse model inversion: Ef- ficient inversion of vision transformers for data-free appli- cations","venue":null,"work_id":"6706e15c-55e9-4bbd-a166-822aa9e8f23c","year":null},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.297383Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:706e2035b7bc4722a0b86aa6f6712ce5d0ac9dd29239a303b2beae2f82a4b89e","observation_id":"6c525abc-d9e1-4087-a956-b32cb615b343","resolution":{"observed_at":"2026-08-11T23:49:03.808035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.784279Z","title":"Architecture, dataset and model- scale agnostic data-free meta-learning","venue":null,"work_id":"9279a9f3-d400-46e9-97ac-fef0e5004311","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.303216Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:22a53043dd27fdea943f901a765ed9b3a9bef7698b0cd2e62c6d8b7fe00c5754","observation_id":"f48bd6a3-6be4-4a1a-adcc-8369362582ab","resolution":{"observed_at":"2026-08-11T23:49:03.790863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18413","last_updated":"2025-02-15T05:56:37Z","snapshot_observed_at":"2026-08-13T11:30:45.144376Z","submitted_at":"2023-05-28T18:00:12Z","title":"Learning to Learn from APIs: Black-Box Data-Free Meta-Learning","version":3},"cited_work":{"arxiv_id":"2305.18413","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.18413","snapshot_observed_at":"2026-08-11T23:49:02.804617Z","title":"Learning to Learn from APIs: Black-Box Data-Free Meta-Learning","venue":"cs.LG","work_id":"0a6dfad9-8381-4952-82ae-e25ebe2ec2d1","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.308233Z"},"links":{"cited_paper":"/paper/2305.18413","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:b971892cff6ae3b64de24ef4ce5c901a21f0fa70df4358eb8db54f2390ee5968","observation_id":"e5538d31-8485-4076-8270-909a6a52e945","resolution":{"observed_at":"2026-08-11T23:49:02.811308Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.13269","last_updated":"2024-08-19T03:31:19Z","snapshot_observed_at":"2026-08-13T19:38:58.092030Z","submitted_at":"2023-07-25T05:39:21Z","title":"LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.13269","snapshot_observed_at":"2026-08-11T23:49:02.313137Z","title":"Lorahub: Efficient cross-task gen- eralization via dynamic lora composition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.313137Z"},"links":{"cited_paper":"/paper/2307.13269","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:9bd51d6ce18195484f1350852ba5ca40665f4f32e391f03b9888ecf82acc2758","observation_id":"24e5e0b6-0edf-47bb-a3c8-f0b900d38ef1","resolution":{"observed_at":"2026-08-11T23:49:02.313137Z","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":"2026-08-11T23:49:03.766828Z","title":"Diversity-aware meta visual prompting","venue":null,"work_id":"929538db-e2be-4943-b3df-2cf15d2c6260","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.317993Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:630d88546f7c81525fc52740a2ea2122e848810083e20d472f9bbe053ce15d10","observation_id":"67e4d478-98e5-41d8-aa77-99000b29bdc0","resolution":{"observed_at":"2026-08-11T23:49:03.772301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.12017","last_updated":"2023-01-30T05:25:59Z","snapshot_observed_at":"2026-08-02T23:44:31.455796Z","submitted_at":"2022-12-22T19:56:09Z","title":"OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.12017","snapshot_observed_at":"2026-08-11T23:49:02.323710Z","title":"Opt-iml: Scaling language model instruction meta learning through the lens of generalization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.323710Z"},"links":{"cited_paper":"/paper/2212.12017","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:c0139f1c0b1ac398b69189743a431cae83d02f19276aa2b9dae33ad4e2d189e5","observation_id":"1267bdaa-20b1-4db1-8fa0-010abb1b5c26","resolution":{"observed_at":"2026-08-11T23:49:02.323710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00690","last_updated":"2023-03-01T17:38:03Z","snapshot_observed_at":"2026-08-13T12:34:09.547076Z","submitted_at":"2023-03-01T17:38:03Z","title":"Rethinking Efficient Tuning Methods from a Unified Perspective","version":1},"cited_work":{"arxiv_id":"2303.00690","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.00690","snapshot_observed_at":"2026-08-11T23:49:02.728136Z","title":"Rethinking Efficient Tuning Methods from a Unified Perspective","venue":"cs.CV","work_id":"059c9662-bbae-4820-956f-1fc72e8f6316","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.329299Z"},"links":{"cited_paper":"/paper/2303.00690","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:5b94289d434e3e63f94458b75e137711508eed9648916b563e90b33e02c331c7","observation_id":"efad1bf9-512b-43ac-8b9e-fd5108628ea6","resolution":{"observed_at":"2026-08-11T23:49:02.733698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.747322Z","title":"All tokens matter: Token labeling for training better vision transform- ers","venue":null,"work_id":"e9291398-d511-48ba-a2dd-489ca77cfe34","year":2021},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.335305Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:00efd4b9dda4f5d7ca0760188c12a3585aef02913dbf05e5a4366c6a77c0548f","observation_id":"6b9c6ca0-ad0e-4ab6-9fb9-66b63f48d5a8","resolution":{"observed_at":"2026-08-11T23:49:03.753681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.729368Z","title":"Adaptive gradient-based meta-learning methods.Ad- vances in Neural Information Processing Systems, 32, 2019","venue":null,"work_id":"14205ee7-ea8e-42c5-843e-a5b59ca1bac9","year":2019},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.341780Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:5be4a0f5f7ffddb178f066bee4527aeaf87ad161f2ede8c0dd451fb29d71798b","observation_id":"911b7ce2-6f46-481b-a298-bacb50eb329e","resolution":{"observed_at":"2026-08-11T23:49:03.734959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.709992Z","title":"Token fusion: Bridging the gap between token pruning and token merging","venue":null,"work_id":"d5814613-a3db-45be-9582-d0f507851efb","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.347824Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:5edf93862940f42b34e440dcc825f3a6f5fe87defa69732f82de87886d2f4e7a","observation_id":"8e67df79-a96d-4d35-a080-8a84b58376eb","resolution":{"observed_at":"2026-08-11T23:49:03.715201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.10054","last_updated":"2022-02-21T09:03:34Z","snapshot_observed_at":"2026-08-13T16:37:33.949862Z","submitted_at":"2022-02-21T09:03:34Z","title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.10054","snapshot_observed_at":"2026-08-11T23:49:02.354758Z","title":"Fine-tuning can distort pretrained fea- tures and underperform out-of-distribution","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.354758Z"},"links":{"cited_paper":"/paper/2202.10054","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:db8bd8d8c29cf8bb75d3abc4e5a000afd8ec043758d95c70185af42fe8b78005","observation_id":"000d9fc0-8c14-4581-93b2-57792137d3a6","resolution":{"observed_at":"2026-08-11T23:49:02.354758Z","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":"2026-08-11T23:49:03.692081Z","title":"Surgical fine- tuning improves adaptation to distribution shifts","venue":null,"work_id":"67621588-298e-41d6-81c2-a287ec793089","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.362440Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:b08417c802c7ba81dcfb5c46bdc50c240b4cb1b754fd46bd26dd128368f10ef2","observation_id":"b4ff5f66-6d52-4bb4-9a2f-7cffd3c661f9","resolution":{"observed_at":"2026-08-11T23:49:03.697646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.675187Z","title":"Patch similarity aware data-free quantization for vision transformers","venue":null,"work_id":"378a0411-9782-4b63-ab90-03805eb98225","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.368615Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:529b8be2eb906f685ea4d20e5096587fb0ec95636bd84485e50478bc6a26917c","observation_id":"92fb4fdd-4403-439c-9dcf-7e3a1cdf0fff","resolution":{"observed_at":"2026-08-11T23:49:03.680222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.657657Z","title":"Psaq-vit v2: Toward accurate and general data-free quanti- zation for vision transformers","venue":null,"work_id":"55df09b0-0218-44a4-bd62-ba426440ad5f","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.373825Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:866cb816178f11dd02e72930ed4ea9473d40ce090a7ffce13330409125b6d2a3","observation_id":"cbe51bc8-866c-48a9-86a5-0bae69b3b300","resolution":{"observed_at":"2026-08-11T23:49:03.663624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05963","last_updated":"2024-10-08T12:15:08Z","snapshot_observed_at":"2026-08-12T22:28:14.766739Z","submitted_at":"2024-10-08T12:15:08Z","title":"Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05963","snapshot_observed_at":"2026-08-11T23:49:02.379022Z","title":"Training-free open-ended object detection and segmentation via attention as prompts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.379022Z"},"links":{"cited_paper":"/paper/2410.05963","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:ea0a888dcdff118720f5fbe417f2c8eeed34d3360a105421cf27051e80944bfa","observation_id":"6740e6a6-3d75-4691-86ac-3f1fdfbe0eca","resolution":{"observed_at":"2026-08-11T23:49:02.379022Z","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":"2026-08-11T23:49:03.637897Z","title":"Few- shot parameter-efficient fine-tuning is better and cheaper than in-context learning","venue":null,"work_id":"d9330fa9-2639-432b-94d6-f9536b8bc21f","year":1950},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.384494Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:2fea10562ff029c37c7a5b6ceb53375351fe80846b24360089feaa922a3faee0","observation_id":"1f8a181a-60c6-49a4-9f42-bea66e05abb9","resolution":{"observed_at":"2026-08-11T23:49:03.645142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.616643Z","title":"Small scale data-free knowledge distillation","venue":null,"work_id":"5891ffc0-b8a1-4e72-9aa5-4513f3dd0eee","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.389573Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:309c63bc4a1f9cd41abf10f3b40ce9594cab4fadf25aa9e10ec0083f2dd9d449","observation_id":"4782d2d7-16ee-4f89-a6d5-b0404f8473c0","resolution":{"observed_at":"2026-08-11T23:49:03.623743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.591740Z","title":"Matcher: Segment anything with one shot using all-purpose feature matching","venue":null,"work_id":"19fdb3ad-1053-48ee-b8ee-f0ce29ec43b8","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.394693Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:049b8cbc41d4871e0ea13648b6b53d65a4d928fa05455073116f68eff158c28c","observation_id":"848cac0e-3ddc-4fb2-9e94-703102b2f999","resolution":{"observed_at":"2026-08-11T23:49:03.598581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.571080Z","title":"DFRD: Data-free robustness distillation 10 for heterogeneous federated learning","venue":null,"work_id":"801e9da8-7eea-40db-a8eb-a9017a61c64b","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.400713Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:fb93976fcb7443c97ae4b4a500a6e2a12fa6ce72bb90c556f2fb5d9406b39638","observation_id":"f2a9e153-e96f-4920-a3a1-a7a2859a8ccb","resolution":{"observed_at":"2026-08-11T23:49:03.578020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.541955Z","title":"MetaICL: Learning to learn in context","venue":null,"work_id":"8a2d93af-f2dc-4e34-a613-04d634f94dd2","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.406113Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:52900abe31a2ab0ae7de37297fcb247751335646994e7338a74b0e24d0633f46","observation_id":"6a1ced4e-8f0b-4104-a2f6-673b79bb86a3","resolution":{"observed_at":"2026-08-11T23:49:03.548306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.520022Z","title":"Meta learning to bridge vision and language models for mul- timodal few-shot learning","venue":null,"work_id":"506def5b-859a-4536-b3f2-8f82e1129f26","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.411119Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:d06d0c9bf0e0deb4a33abc33453661a4900cd4c86e94dd5aa919b7a5b8b136ac","observation_id":"9644603e-d628-4d48-bc8d-df1f2fa2b869","resolution":{"observed_at":"2026-08-11T23:49:03.526604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.02999","last_updated":"2018-10-22T16:11:14Z","snapshot_observed_at":"2026-08-05T12:53:31.632675Z","submitted_at":"2018-03-08T08:29:38Z","title":"On First-Order Meta-Learning Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.02999","snapshot_observed_at":"2026-08-11T23:49:02.416026Z","title":"On first-order meta-learning algorithms","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.416026Z"},"links":{"cited_paper":"/paper/1803.02999","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:742838f589556aa60533de8e02d0965f721fe385b0fc711ef796478a72eb9749","observation_id":"fbdb9dc0-4022-4722-9d0d-45ae88edbebb","resolution":{"observed_at":"2026-08-11T23:49:02.416026Z","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":"2026-08-11T23:49:02.421377Z","title":"Automated flower classification over a large number of classes","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.421377Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:84ae44743b7cfbbfae35190bde386e0f0eee87c36532a900e443d212f02621e5","observation_id":"2fd652f5-1824-4e59-99e2-37a95c63cc4f","resolution":{"observed_at":"2026-08-11T23:49:02.421377Z","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":"2026-08-11T23:49:02.426404Z","title":"Dynamicvit: Efficient vision transformers with dynamic token sparsification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.426404Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:97094428b8746cc728cc33ee666ee20bc33a9f2ea96fd600137b5892db1c0f4e","observation_id":"ab31a570-5dcb-4296-bb01-b310e905a47d","resolution":{"observed_at":"2026-08-11T23:49:02.426404Z","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":"2026-08-11T23:49:03.478928Z","title":"Data-free knowledge distillation for fine-grained visual cat- egorization","venue":null,"work_id":"0a7eb193-5fc7-4b32-be7a-2be9f96731b7","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.431781Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:e023aa855d838e82bcf9f35c9830469dc88b64baeea79d9045f5553799f57121","observation_id":"4117e486-ab06-4127-984c-14d44cb9c82c","resolution":{"observed_at":"2026-08-11T23:49:03.484986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.461636Z","title":"Prototypical networks for few-shot learning","venue":null,"work_id":"107e7b21-2d47-4585-abb9-a22fde15f999","year":2017},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.436745Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:3f74a13fc7ed23371952dc6fe19bc9bc8f9fe2abb8ef6e0f4093bb4d67a28d85","observation_id":"812c7cdc-4a7d-41c4-91f0-8ebbdf5eb21e","resolution":{"observed_at":"2026-08-11T23:49:03.467187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.443574Z","title":"Meta-transfer learning for few-shot learning","venue":null,"work_id":"26aaab4f-d2ff-4711-9d1e-01e385229b9e","year":2019},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.442097Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:10c7a6c6725b898ce5d7d9732913199f80a151cc79752e2c0cfea91ef61d443d","observation_id":"e7595bca-dab0-4482-8153-dc3c2735b9fb","resolution":{"observed_at":"2026-08-11T23:49:03.449516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.422657Z","title":"Training data-efficient image transformers & distillation through at- tention","venue":null,"work_id":"f3c62cb7-f50c-4152-9114-85012c4cba31","year":2021},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.447985Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:a355d40ba0c3cc44c0ab75f82f8950b9f8f1ce4f8822bc6783604b91f18173e6","observation_id":"932ebb3d-8600-4da0-9145-6ca26b92106b","resolution":{"observed_at":"2026-08-11T23:49:03.428379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17046","last_updated":"2024-11-26T02:23:31Z","snapshot_observed_at":"2026-08-12T12:32:55.562854Z","submitted_at":"2024-11-26T02:23:31Z","title":"Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation","version":1},"cited_work":{"arxiv_id":"2411.17046","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.17046","snapshot_observed_at":"2026-08-11T23:49:02.617663Z","title":"Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation","venue":"cs.CV","work_id":"aea3ddb8-7d7c-48b5-abcb-a3e9276150e8","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.454513Z"},"links":{"cited_paper":"/paper/2411.17046","citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:f44b88c003e9be36024ab642bfe8ee67d18c5ae985b7436a9ac1cfe72bff0084","observation_id":"0ad02cc7-945c-473b-b5a7-c5bd2eae24d1","resolution":{"observed_at":"2026-08-11T23:49:02.625769Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.405382Z","title":"Nayer: Noisy layer data generation for efficient and effective data-free knowl- edge distillation","venue":null,"work_id":"d1c73109-122f-40a6-a645-3e98de4529bb","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.462256Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:f8fc100ab58abe86d7b7cedccacbe6f553e3782e0e05505a4c8ca5da96d08bff","observation_id":"8179444d-7cbd-4bc0-82c6-83c2d03e7f37","resolution":{"observed_at":"2026-08-11T23:49:03.410579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.386690Z","title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","venue":null,"work_id":"e9ff2876-df47-4eb0-bc63-7277879b0bdc","year":2020},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.468510Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:a61fe2d4d8067f3c323d26863b00bacbb0df71cbc0b75967694c725e86039c21","observation_id":"f8ecaca5-9874-4888-bd03-6e3cda50cc81","resolution":{"observed_at":"2026-08-11T23:49:03.393273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.368707Z","title":"Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016","venue":null,"work_id":"7b0ade3e-939f-413a-8a5f-e713f506bdfb","year":2016},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.476403Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:ebaf499cfdf72b7241896b6915dfa10e08392bc3501139a5e1f748831f2243b7","observation_id":"9001b451-1bdb-4926-9f7a-aa8a3eb9ab29","resolution":{"observed_at":"2026-08-11T23:49:03.373994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.349413Z","title":"Transformers learn in-context by gradient descent","venue":null,"work_id":"6d672aee-7a45-4ab2-b5d9-f8cdc37ff645","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.482501Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:eb5a2c2ae99a1c3fa9644e4d4fa4c0a1f918f05f1bb181aa1da6664e7fb46544","observation_id":"a388596d-3a7c-42ee-8022-cc48ba878758","resolution":{"observed_at":"2026-08-11T23:49:03.355102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.331678Z","title":"The Caltech-UCSD Birds-200- 2011 Dataset","venue":null,"work_id":"c5c5bf99-32c4-4f4c-9880-6bf74c8de059","year":2011},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.488873Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:9e3e7f02d49e80302f135bcd66726b8533eef15f66b79daf5b6f019092bb3091","observation_id":"9c432c83-e4e3-4eb6-b477-b82667b308b5","resolution":{"observed_at":"2026-08-11T23:49:03.337131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.314297Z","title":"Generalizing to unseen domains: A survey on do- main generalization","venue":null,"work_id":"7279f33e-1f42-448b-86ba-9dc33e34ba73","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.494305Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:69e426ed969be844957870435ec0b3cff514e00be967a6025128cc475084ac5e","observation_id":"449d0797-3a36-4462-a80f-d50725ba3ccf","resolution":{"observed_at":"2026-08-11T23:49:03.319922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.295708Z","title":"De-confounded data-free knowledge distillation for handling distribution shifts","venue":null,"work_id":"ceadd51f-d2ea-4068-b197-4a7ea5336b98","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.500548Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:5108210659968dcec890d776657c624047d0be62307a126e7f3555f77da0d01f","observation_id":"33ed02de-3979-495f-8d79-c48f10ee878a","resolution":{"observed_at":"2026-08-11T23:49:03.301668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:02.505639Z","title":"Meta learning on a sequence of imbalanced domains with difficulty awareness","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.505639Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:83bfbe9e9075bd35c114dec16ac787e670238b82a3ce51fc49d8d4a5e5ff11a2","observation_id":"ceaf2101-e809-4f01-a8fe-f897d279ebdf","resolution":{"observed_at":"2026-08-11T23:49:02.505639Z","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":"2026-08-11T23:49:03.264208Z","title":"Meta-learning without data via wasserstein distributionally- robust model fusion","venue":null,"work_id":"be8be389-54f5-4d86-91ad-43f59d156a03","year":2022},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.510457Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:29ddbd8a5940aa28b799a75c6076cd84576c36445f95e8f76b5202ca4826d42f","observation_id":"03601704-7229-410a-a2d2-a4adb1c18feb","resolution":{"observed_at":"2026-08-11T23:49:03.269704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.245678Z","title":"Task groupings regular- ization: Data-free meta-learning with heterogeneous pre- trained models","venue":null,"work_id":"c5c07a9f-31b3-4d24-a017-e81b3cb10dac","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.515709Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:7149f1124daf0c6839828c2210f649254d43876fe597e402459bedb7f84f1347","observation_id":"e69481af-ecd4-4a12-92e8-23b7ca3cbd2a","resolution":{"observed_at":"2026-08-11T23:49:03.251892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.226262Z","title":"Free: Faster and better data-free meta-learning","venue":null,"work_id":"0ad0c531-9edc-4b5f-a042-ee4c24fc7e07","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.521737Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:0d08225509cab7d07de893332fe5ddc391c223c4dace0e5e93b01aacd1ad61ee","observation_id":"fe82fd05-b513-4d59-8ec0-2b9685999930","resolution":{"observed_at":"2026-08-11T23:49:03.232310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.209312Z","title":"pi-tuning: Transferring multimodal foundation models with optimal multi-task inter- polation","venue":null,"work_id":"21314cf3-07d2-4ba0-af0e-79a0d1b98f4d","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.526950Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:4969ff2447f5c5842b3f0c213276831f938df945acedc718a3d49ff2a30abd17","observation_id":"1480a2e9-8234-4da5-8788-795281c2a89a","resolution":{"observed_at":"2026-08-11T23:49:03.214334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.190399Z","title":"Mole: Mixture of lora experts","venue":null,"work_id":"7abaf28f-defa-47bd-83ec-5c0f00be8ac1","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.532411Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:4429f9a8dfa60f0a27c426091d7e898f14f43af9e8473983d130bbe954acfb70","observation_id":"ff3a4ad4-5a20-4c68-be03-b081ff19249b","resolution":{"observed_at":"2026-08-11T23:49:03.197254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.170587Z","title":"Meta-personalizing vision- language models to find named instances in video","venue":null,"work_id":"69cf6cdb-6bdd-424b-8718-421e62d2f4a9","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.537671Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:fcae5e058f1612a835f2465e30892196ca0ffbf5a1bbeeea682d52d585e85524","observation_id":"8d6881d8-eebb-41ee-993b-035f8e2254bf","resolution":{"observed_at":"2026-08-11T23:49:03.176649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.152475Z","title":"11 Dreaming to distill: Data-free knowledge transfer via deep- inversion","venue":null,"work_id":"a33d9955-1231-4129-90df-ac93f25801a5","year":2020},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.542981Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:cc4ac89177444705aeacb7a24be73a6ef1658f896c33ef0bcf7927233824d5fa","observation_id":"8fee081b-75ed-4feb-9cc7-e8e1ccf8f167","resolution":{"observed_at":"2026-08-11T23:49:03.157971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.134473Z","title":"Bayesian model-agnostic meta-learning","venue":null,"work_id":"3b39facd-70f0-46ab-a083-34657a379a7d","year":2018},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.548707Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:422377dfb2960a186841b1d9e01745fa42b2ed42024691262e201e11ce323e8f","observation_id":"ba09bb1e-f0a7-450d-8ee7-f58cb2be3da3","resolution":{"observed_at":"2026-08-11T23:49:03.140780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.113390Z","title":"Data-free knowledge distillation via feature exchange and activation region constraint","venue":null,"work_id":"d37fd1ce-732a-48ea-9169-f51e1e2f2d82","year":2023},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.554557Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:f56dde000d4a71fba7852a383c75fbe4b8ba7025cbe2ebf8952ef998f789e34b","observation_id":"fb429769-24eb-4211-acad-35c833b74666","resolution":{"observed_at":"2026-08-11T23:49:03.119951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.094244Z","title":"recycle in-domain LoRAs","venue":null,"work_id":"bacc7b88-e432-4198-b04a-c0ef354c478d","year":2024},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.559610Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:79a8e88a99a5e6ad7d110ebbf3381cc6c2245028686a45af9c3029c33fde54a1","observation_id":"8e0c5053-03fe-4f80-be6e-5286445c7357","resolution":{"observed_at":"2026-08-11T23:49:03.099626Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T23:49:03.075442Z","title":null,"venue":null,"work_id":"02051b28-ceaf-4f55-a890-e8db7d91364c","year":null},"citing_paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T23:49:02.568731Z"},"links":{"citing_paper":"/paper/2412.02220"},"observation_digest":"sha256:4d05339632595e3a299fef79853674630feae710c99579052e907dbb2b002363","observation_id":"8148cc35-cdda-462a-8523-40df5a658ba6","resolution":{"observed_at":"2026-08-11T23:49:03.081209Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.02220","last_updated":"2024-12-03T07:25:30Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T14:23:55.655949Z","submitted_at":"2024-12-03T07:25:30Z","title":"Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":4,"verified_fuzzy":48},"total_outbound_references":75},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2412.02220."}