{"as_of":"2026-08-09T04:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c59d292a1874afd72b1cd79a8adf3183d9a52d469e134f7b9b9382f82eb08ee8","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:29:37.864637Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.18770/citation-record","integrity":"/paper/2505.18770/integrity","json":"/paper/2505.18770/citation-record.json","paper":"/paper/2505.18770"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:33.867753Z","title":"Domain general- ization: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:33.867753Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:dc04f49f5d406da68b019298c989ca7b0637fe25816e349e290ddd3bd3c370cf","observation_id":"b3851154-9f71-41e6-83cb-0158e732df25","resolution":{"observed_at":"2026-08-07T14:29:33.867753Z","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":"2026-08-07T14:29:33.910869Z","title":"Generalizing to unseen domains: A survey on domain generalization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:33.910869Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:90fb4d8238943b93ce912a273cf27de66f1a7e97d270f31557579ff074175e5d","observation_id":"70e0585a-67a1-44ae-86c4-b06f27bbb695","resolution":{"observed_at":"2026-08-07T14:29:33.910869Z","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":"2026-08-07T14:29:45.378261Z","title":"Generalizing to unseen domains via adversarial data augmentation,","venue":null,"work_id":"f2173cd2-9b22-4bde-b4af-8b57731bc16d","year":2018},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:33.968230Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:16ee2f0ff789c739335d1fb0ed992c1f51a6b50aecc19efe867cceff7bd37579","observation_id":"d40e93fc-8a01-4279-b479-e76298f6326c","resolution":{"observed_at":"2026-08-07T14:29:45.474343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:45.216838Z","title":"A simple feature augmentation for domain generalization,","venue":null,"work_id":"f17b92ac-33ae-47d3-8177-00767a3f79f0","year":2021},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.052727Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:353e196a473c94e0b31787ec2ebf9eaea9575afd3426f8ac0a928e6466c6f4bb","observation_id":"8ebde99e-3e21-49e4-8b0c-fa3744d0da1f","resolution":{"observed_at":"2026-08-07T14:29:45.289633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-07T14:29:34.119648Z","title":"mixup: Beyond empirical risk minimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.119648Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:8a70bfd6be92feba636a5d0bd90519d27c4f2e8fc48294e1a4953f138420a068","observation_id":"0b4c6ed9-20d9-4c71-aa54-5eb6b7a00b82","resolution":{"observed_at":"2026-08-07T14:29:34.119648Z","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":"2026-08-07T14:29:34.176320Z","title":"Domain generalization via invariant feature representation,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.176320Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:ef142868388e58e4cf076cad04b2f4aea5e173ea87cfee5b0b2c55a1481e552e","observation_id":"45796ff8-77ea-4ff6-a43d-bbab9d69d534","resolution":{"observed_at":"2026-08-07T14:29:34.176320Z","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":"2026-08-07T14:29:45.106308Z","title":"Domain generalization with small data,","venue":null,"work_id":"9edd29aa-c27b-43fb-a41e-b591736c0817","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.236645Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:b7bb9b4e9035634f971d7f205b8112444ca79830ee15e25817a8994066c7fb28","observation_id":"f5527f58-f98c-4922-be51-4aea68bb22db","resolution":{"observed_at":"2026-08-07T14:29:45.145876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:44.908561Z","title":"Ensemble of averages: Improving model selection and boosting performance in domain gener- alization,","venue":null,"work_id":"d5775051-369e-4394-b959-913a18c9ff22","year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.321533Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:077a346ac05adc32ba441149bc082749475954997403757881256211d159ea65","observation_id":"62450aa6-a4d6-4962-8fd6-4cfa2a01b571","resolution":{"observed_at":"2026-08-07T14:29:45.000553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:34.387196Z","title":"Domain adaptation via prompt learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.387196Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:cca17187668770b983057fe453038ec0d0624bdf8dcfa26461a930fece184b12","observation_id":"014ed25d-6a98-4e45-b282-5186b8805875","resolution":{"observed_at":"2026-08-07T14:29:34.387196Z","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":"2026-08-07T14:29:44.710091Z","title":"Prompt-based distribution alignment for unsupervised domain adaptation,","venue":null,"work_id":"8901fdcb-e6a5-4363-84c8-ddef118a890d","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.449537Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:cfd583056f4d754c32f44d1200de7e42223b2e9935ff74ba2f4fd970c7cccfa6","observation_id":"589d9d6e-ccaf-4ae0-a8e1-0f7ce88d5416","resolution":{"observed_at":"2026-08-07T14:29:44.776215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:34.529806Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.529806Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:32a3c9ee61c5d8a08e12870e3bd7a51c08a3dee19d703587f63c514811d4d170","observation_id":"b6814aa9-faa1-42d2-9153-9b59880164ab","resolution":{"observed_at":"2026-08-07T14:29:34.529806Z","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":"2026-08-07T14:29:34.595412Z","title":"Scaling up visual and vision-language representation learning with noisy text supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.595412Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:6f119ae6c0cf1cc9177bd628ec07b3866d97b13c668ff0122f987b03a8e70f66","observation_id":"a6bfda7c-b6a6-43af-b431-39241ffe3198","resolution":{"observed_at":"2026-08-07T14:29:34.595412Z","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":"2026-08-07T14:29:34.654802Z","title":"Learning to prompt for vision- language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.654802Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:b567d7219f333afa2eb649ae7af47e26830e1cdc4f4b92bc5b5e02c2296fe9ea","observation_id":"c3d0ef34-3216-4b39-941c-0ba3fd46420a","resolution":{"observed_at":"2026-08-07T14:29:34.654802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19287","last_updated":"2024-11-12T10:48:21Z","snapshot_observed_at":"2026-07-06T18:07:27.432252Z","submitted_at":"2024-04-30T06:34:21Z","title":"Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19287","snapshot_observed_at":"2026-08-07T14:29:34.741589Z","title":"Revisiting the adversarial robustness of vision language models: a multimodal perspective,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.741589Z"},"links":{"cited_paper":"/paper/2404.19287","citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:c944c871f73d68894eff4644aa34343a344df0f99843b3864e9b0ce53bc1989d","observation_id":"ed9c31f9-b857-487a-bd06-383e5ae4aa40","resolution":{"observed_at":"2026-08-07T14:29:34.741589Z","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":"2026-08-07T14:29:34.818344Z","title":"Maple: Multi-modal prompt learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.818344Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:f49993917ad55b34630d701768c23d04852cc86dfb8e40e370d96a52bbf6ab6b","observation_id":"1162aaaf-69cf-4784-a5b2-c7d69785c281","resolution":{"observed_at":"2026-08-07T14:29:34.818344Z","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":"2026-08-07T14:29:44.423609Z","title":"Domain prompt learning for efficiently adapting clip to unseen domains,","venue":null,"work_id":"d7ff2082-ffec-4910-8b36-ea0944bfb700","year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.871550Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:e387488218cc3c0acdf4600417198a308bcd045813d9f5ac13577342f0125141","observation_id":"a6c1cd7c-cf63-4375-877c-15ce25dc43cc","resolution":{"observed_at":"2026-08-07T14:29:44.539308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:44.186231Z","title":"Soft prompt generation for domain generalization,","venue":null,"work_id":"718fc329-3805-4fbd-ad35-4ec1eb0e7c39","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.915462Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:aba9e3616d90df87cba3c43b13419a4fec24d71c6fb1f6843582438a4ee3c35a","observation_id":"67015d0c-2f63-4a0f-b509-7280fee28930","resolution":{"observed_at":"2026-08-07T14:29:44.290230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:43.894130Z","title":"Cbda: Contrastive-based data augmentation for domain generalization,","venue":null,"work_id":"952b1d5b-d750-447f-b388-c06d4634e035","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:34.960494Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:461834718ea9beb62dbb47d4d8b21509ed271629b1baa033e329439095b743c5","observation_id":"3cce10dc-01a0-4816-bac6-00f5a138d361","resolution":{"observed_at":"2026-08-07T14:29:44.023637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:43.705986Z","title":"Mixup-induced domain extrapolation for domain generalization,","venue":null,"work_id":"eb6cd9ba-33ed-4f90-b605-baf8a5f95417","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.010249Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:966fb8d7c5ff49b777753bb2bd6463a29f264a9925eb20e2398c29401957aac0","observation_id":"8963d02f-ecb5-4c39-a37d-c2c127c3cda8","resolution":{"observed_at":"2026-08-07T14:29:43.785164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:35.097705Z","title":"Domain generalization with adversarial feature learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.097705Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:607524e750ff16f3acd901ceaacdf8a7d4bfaf7138443520afeac99f5552436a","observation_id":"d2be7144-e616-40d5-ae9e-31fbf02a76c3","resolution":{"observed_at":"2026-08-07T14:29:35.097705Z","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":"2026-08-07T14:29:43.493839Z","title":"Domain generalization via inter- domain alignment and intra-domain expansion,","venue":null,"work_id":"b1102079-60df-4eaa-ba63-e2f9a425bf78","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.229501Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:f570605a41a609c77b193646f82066861e3a192a9695a970aac00f70b5e23465","observation_id":"2662f800-ed2b-4711-b841-ff506dc84607","resolution":{"observed_at":"2026-08-07T14:29:43.582537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:43.290990Z","title":"Domain-adversarial training of neural networks,","venue":null,"work_id":"47cc4210-581a-4712-80c1-02ae697663b0","year":2016},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.317081Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:cb40f508a7f2b784d18bc1ab8084b2f62808f51f0490ec5e7084ae434bfc78c1","observation_id":"3f28dec5-874d-426a-8ac0-f06b0b9a7a1a","resolution":{"observed_at":"2026-08-07T14:29:43.403055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:35.397636Z","title":"Deep domain generalization via conditional invariant adversarial networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.397636Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:a6687be6ebdde9d2e9cccd64250acc3dc9de90b8443954d7f9a60b27cf222df0","observation_id":"264ee814-5816-4452-a30b-ea8a4381656f","resolution":{"observed_at":"2026-08-07T14:29:35.397636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02893","last_updated":"2020-03-27T19:07:58Z","snapshot_observed_at":"2026-07-06T08:05:24.076802Z","submitted_at":"2019-07-05T15:26:26Z","title":"Invariant Risk Minimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02893","snapshot_observed_at":"2026-08-07T14:29:35.510562Z","title":"Invariant risk minimization,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.510562Z"},"links":{"cited_paper":"/paper/1907.02893","citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:4d171d2d9f26b71d95ff3025bd947e6208d907d00561890217b9cbfc899595f3","observation_id":"9e9ab3af-f83f-4ddf-99a0-9ae6e891852c","resolution":{"observed_at":"2026-08-07T14:29:35.510562Z","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":"2026-08-07T14:29:43.125931Z","title":"Invariant information bottleneck for domain generalization,","venue":null,"work_id":"d40f34d5-bb2e-4bf8-8d24-b026e29379d3","year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.541680Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:616864c25d221bc8da6b0ec66cc639120b7c06c0a6044f6ce6b99416878c9900","observation_id":"67ed05cb-2f49-4064-9ed7-5b64ff7cf9f4","resolution":{"observed_at":"2026-08-07T14:29:43.168446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.940999Z","title":"Exploiting domain- specific features to enhance domain generalization,","venue":null,"work_id":"e98763ca-cc9f-44af-a418-bf8be36c5bc5","year":2021},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.587561Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:f2e01852c8dac4c1017713eec6304c6113ae1db901b7bb6ea816db2fb4b8cbdd","observation_id":"b1002d5f-5e56-4c18-8af8-ebb685d44906","resolution":{"observed_at":"2026-08-07T14:29:43.028298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.820590Z","title":"Simple: Specialized model-sample matching for domain generalization,","venue":null,"work_id":"0f7703f7-105a-462b-b10e-5dbbea6513a0","year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.654499Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:3ff39ece5bdfdbee19c237aaf9ac8dfc317fe5fee158d89b777a8f84c75b1b20","observation_id":"d0e3e15b-c2d5-4e04-b5f4-c36e6b0e4f58","resolution":{"observed_at":"2026-08-07T14:29:42.880589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.661863Z","title":"Mixstyle neural networks for domain generalization and adaptation,","venue":null,"work_id":"2b78a34d-64ba-482a-b0fc-4add4286a808","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.734207Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:065236774d5e74455743469a158602954488deb80dd73afc9b6e08cb9e569d9c","observation_id":"b311f995-66a6-4b37-8264-eef2673fd261","resolution":{"observed_at":"2026-08-07T14:29:42.727176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.493582Z","title":"Knowledge distillation-based domain-invariant representation learning for domain generalization,","venue":null,"work_id":"6c58a6d0-0235-4993-8b2e-3425367cac58","year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.803843Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:67cd4520a302b8c3cd012f0b1ef97450fea9b17a2f164822a56b89816879e2a3","observation_id":"27fa6310-ffba-4e4a-adeb-12bccb55f247","resolution":{"observed_at":"2026-08-07T14:29:42.579352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.344039Z","title":"Boosting domain generalization by domain-aware knowledge distillation,","venue":null,"work_id":"16d86e1b-484f-4124-9e4c-e39317416b87","year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.876875Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:409867d842ec9321b133b53e4cf36a75d478cb9d779151f098fddd994fcb96c6","observation_id":"c60fa027-2372-49c9-a3af-67ea01007622","resolution":{"observed_at":"2026-08-07T14:29:42.415967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.219032Z","title":"Learning to generalize: Meta-learning for domain generalization,","venue":null,"work_id":"d8d1005a-ad24-4dd7-908e-cfe10100084d","year":2018},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.928730Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:3fa38dc4148c0060703d538a7ae00eba2ce12cbe85e37385db3d3d014f3ef7ff","observation_id":"11d0b151-1984-4748-bf91-92d0ca270ec8","resolution":{"observed_at":"2026-08-07T14:29:42.284486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:42.107716Z","title":"Discriminative adversarial do- main generalization with meta-learning based cross-domain validation,","venue":null,"work_id":"30b97a60-a2d9-4268-8fa9-d8025346fc83","year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:35.984747Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:76cc0a56d6ec4b80d1905b60523eb9abe0c47006f3ad5a9108ff01ea0211c6d7","observation_id":"b4b31a4f-916e-4c94-9440-969f57c8ab21","resolution":{"observed_at":"2026-08-07T14:29:42.133741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:41.984801Z","title":"Learning common and specific visual prompts for domain generalization,","venue":null,"work_id":"9cd98176-e4f3-43ae-9e6b-ff9837688220","year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.029752Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:d0efd1b949d3dde9d9e8ea582d354c9c33bcbe09552362fb950d06d1cf426131","observation_id":"b727a7d7-d7cd-44dc-9fbc-13d5778fa290","resolution":{"observed_at":"2026-08-07T14:29:42.033559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14163","last_updated":"2024-09-21T15:02:13Z","snapshot_observed_at":"2026-07-06T19:19:15.609969Z","submitted_at":"2024-09-21T15:02:13Z","title":"PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14163","snapshot_observed_at":"2026-08-07T14:29:36.084962Z","title":"Promptta: Prompt- driven text adapter for source-free domain generalization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.084962Z"},"links":{"cited_paper":"/paper/2409.14163","citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:bcf1c8ee8b2d292db47780270f5a5389d2191fc87484b1ebb45194fe084bba9d","observation_id":"92661be1-a9c5-4389-b3cf-2a391db1eca8","resolution":{"observed_at":"2026-08-07T14:29:36.084962Z","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":"2026-08-07T14:29:41.864257Z","title":"Consistent prompt learning for vision-language models,","venue":null,"work_id":"3c787562-ec8b-4a3b-95b9-b349b9e4257b","year":2025},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.150071Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:aaf60579c2da08f48cec5975e161b62ffb82b6b5ce53752f93feb1faa34c5a3d","observation_id":"402067f9-56c6-425e-8305-57256cf06bb0","resolution":{"observed_at":"2026-08-07T14:29:41.914169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:36.198006Z","title":"Tip-adapter: Training-free adaption of clip for few-shot classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.198006Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:69c215884ae0c64bf3a6cba861891703d0e48fca171dcdd075bb9aecd114373a","observation_id":"57fe4ef1-290b-48eb-b516-900a66ee8451","resolution":{"observed_at":"2026-08-07T14:29:36.198006Z","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":"2026-08-07T14:29:36.245217Z","title":"Clip-adapter: Better vision-language models with feature adapters,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.245217Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:83da53b36379ac402bd37cac2529d9d62d4e0f217282bca294fa785510009adb","observation_id":"bc71646b-af73-4c92-8255-808d6afff780","resolution":{"observed_at":"2026-08-07T14:29:36.245217Z","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":"2026-08-07T14:29:41.676972Z","title":"Clipceil: Domain generalization through clip via channel refinement and image-text alignment,","venue":null,"work_id":"f39946ad-c635-4d1a-9bdb-eca6bafea793","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.305833Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:7ebf466a9eb30739a5328f0f72fcd28d10cb09511f9b242189ef4626ae8d15b2","observation_id":"d06fd343-9f38-4a4c-8a62-2c5243f90f5d","resolution":{"observed_at":"2026-08-07T14:29:41.742392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:41.545992Z","title":"Stylip: Multi-scale style-conditioned prompt learning for clip-based domain generalization,","venue":null,"work_id":"cbabb11d-b69a-4b6a-856f-f18a494ddb99","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.362284Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:1952f66fe9f80d326c4b0d5cc9653b94af2e497b51097c980186a48b59998c89","observation_id":"8f04a2b0-f00c-4543-8082-584f6db37e48","resolution":{"observed_at":"2026-08-07T14:29:41.587481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:36.415810Z","title":"Disentangled prompt representation for domain generalization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.415810Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:e4c2d0606996436519c08a2529e343ea65686b477b75349904f8d7d272b64cb8","observation_id":"c2f67598-09f9-4d58-8157-a1c2aa8d7a7b","resolution":{"observed_at":"2026-08-07T14:29:36.415810Z","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":"2026-08-07T14:29:41.347281Z","title":"Ensembling disentangled domain-specific prompts for domain generalization,","venue":null,"work_id":"945d452a-2b11-4249-95e4-711c7636aebe","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.462367Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:b568c365aad34181748c674c49d18add56636c256b8cf2793cb5bb6acbc2593b","observation_id":"63602186-1200-47f8-8bb2-0d78a2a91b43","resolution":{"observed_at":"2026-08-07T14:29:41.418089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:41.169870Z","title":"Conditional prompt learning for vision-language models,","venue":null,"work_id":"2bb83c7b-0769-47b2-9742-d7cc751ff34a","year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.518194Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:ecf89b279a621226027cc639e7dba21af72b2becec10be1898cf9be3e46da444","observation_id":"b2b063b9-13d3-4317-aaac-ee27aece9558","resolution":{"observed_at":"2026-08-07T14:29:41.248625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:40.957198Z","title":"Nlnl: Negative learning for noisy labels,","venue":null,"work_id":"898b621e-b7df-45f5-b9dd-8f1af482ac65","year":2019},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.571271Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:0246502817e1958c4bc0b362ece75c48f04f65da90c45ca10ee0f56a5a91abf6","observation_id":"6e650738-86b3-4834-a8e7-4f4e6ca203bf","resolution":{"observed_at":"2026-08-07T14:29:41.048691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:36.642608Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.642608Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:62ffe69d535d3cd5188925419d486d8c1d87db76d0dd400396af2d254d5e8f28","observation_id":"e36f5134-c974-473c-9176-6e7a322150a4","resolution":{"observed_at":"2026-08-07T14:29:36.642608Z","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":"2026-08-07T14:29:36.697833Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.697833Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:2e795132a9ef177207cd4e0525fd74b073bb2ac2607ccc3da4e84419cfcc24af","observation_id":"84f9cfaa-c486-40be-a15f-a500cca54554","resolution":{"observed_at":"2026-08-07T14:29:36.697833Z","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":"2026-08-07T14:29:40.648663Z","title":"Learning open set network with discriminative reciprocal points,","venue":null,"work_id":"38343aa8-dcc5-42ef-b489-0a6cfc723059","year":2020},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.745893Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:39adbce22e91035eeb5a6a6e5a4693a65f02f82422344688ed7ffcf056776521","observation_id":"7c47ab84-aac7-40f6-9a1c-e773b6df4433","resolution":{"observed_at":"2026-08-07T14:29:40.785482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:40.327748Z","title":"Argue: Attribute-guided prompt tuning for vision-language models,","venue":null,"work_id":"1342d40a-b411-4b2f-b56d-32e262e517a7","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.791741Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:f965548bc9750c1308639d6d7b451d1eaf50b41f62144f5f8a033e3b52e63471","observation_id":"65ec4c2d-a69f-4aac-87d1-efda6e3739e9","resolution":{"observed_at":"2026-08-07T14:29:40.495025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:39.995114Z","title":"Clipn for zero-shot ood detection: Teaching clip to say no,","venue":null,"work_id":"11f01f74-b29f-4573-947f-8741c16a1a0c","year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.844673Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:7aea962dbc0d6d229720118d3e2519d3b843125a41b68a11b78e96c859f9e538","observation_id":"800772a8-3699-47da-9d44-1d8b28ed4823","resolution":{"observed_at":"2026-08-07T14:29:40.184328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:39.704604Z","title":"Learning transferable negative prompts for out-of-distribution detection,","venue":null,"work_id":"c6b2a251-0147-4fcc-bf62-85af32524817","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.890688Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:0ed897bff459f7f2590f5f22a3e7c66eba896b34cf054be209e91dbd36307eb1","observation_id":"bd94fe83-80d2-44da-92e5-d7475b8fbd27","resolution":{"observed_at":"2026-08-07T14:29:39.837650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:39.502280Z","title":"Semi- supervised learning with pseudo-negative labels for image classifica- tion,","venue":null,"work_id":"b7d5b207-8806-437d-958a-c7f3ba2f3835","year":2023},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.939319Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:ecc240cca93dd57defa4c0cde34c6fed29e070e1da1fa6c0418c0866f6262d04","observation_id":"6057958c-6f45-41e2-bc1c-c66339487998","resolution":{"observed_at":"2026-08-07T14:29:39.566086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:39.305590Z","title":"Vision-language models are strong noisy label detectors,","venue":null,"work_id":"e463234c-4b68-48be-af27-a539fde1d53d","year":2024},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:36.974547Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:359d668f8f28658baa706453f00a2b12c54b9e017a663302475f7eb06a60c0a8","observation_id":"ec55a492-0108-4e57-bd5c-8104d9ae7735","resolution":{"observed_at":"2026-08-07T14:29:39.380084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:37.042472Z","title":"Deeper, broader and artier domain generalization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.042472Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:7a69babe93f44a068fbe84a3aeb1d47c38240d60fedbc50161cbd87ded75f932","observation_id":"c9a7fd13-a13d-4981-b28a-259de1df9702","resolution":{"observed_at":"2026-08-07T14:29:37.042472Z","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":"2026-08-07T14:29:39.103804Z","title":"Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias,","venue":null,"work_id":"322d3b0e-b1bc-4bd4-aaab-eb6428a5b892","year":2013},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.105946Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:3c536c53a07a66f6ae5b0531538c460cd0902c3bf6dd021868a5789292e9302b","observation_id":"2d950008-1c65-4f08-8e85-3fc3a153f7bc","resolution":{"observed_at":"2026-08-07T14:29:39.175878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:37.182255Z","title":"Deep hashing network for unsupervised domain adaptation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.182255Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:a66f9fe954ad89fa17627fe893a1effa82c233277e7bdccb712af6bf59e22683","observation_id":"03a4fd0c-153c-4e9d-a7d4-feec6e32f702","resolution":{"observed_at":"2026-08-07T14:29:37.182255Z","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":"2026-08-07T14:29:38.849113Z","title":"Recognition in terra incognita,","venue":null,"work_id":"6edda0e7-2368-4f4c-9165-e818266d4141","year":2018},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.345940Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:ae219076a23cebc37c6f86bbdc3b096a7e77d1361f7e31e1f3d91e60d4713c35","observation_id":"05614b97-23fd-4ad6-9e07-01f1531ba793","resolution":{"observed_at":"2026-08-07T14:29:38.941197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:37.409295Z","title":"Moment matching for multi-source domain adaptation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.409295Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:a6479e939e2d920df053f689175956eee6bde1e0cbb44c61fe931fcd30521770","observation_id":"1257b819-bf59-4c84-8be9-4c5b1cbe1b93","resolution":{"observed_at":"2026-08-07T14:29:37.409295Z","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":"2026-08-07T14:29:38.475537Z","title":"In search of lost domain generalization,","venue":null,"work_id":"fc6551fd-1e43-4a08-bbe9-b33ab3a5a180","year":2020},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.483638Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:e8374bdd48020bac38d430a8baf7edf51c6c7d276a5f9ecc3d8c4986e9ba5166","observation_id":"fd6ac35b-7812-4cdd-8c20-dbe9ee594062","resolution":{"observed_at":"2026-08-07T14:29:38.677906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:29:38.217602Z","title":"Swad: Domain generalization by seeking flat minima,","venue":null,"work_id":"895af8a5-a181-4873-b2a5-7599afa0b7e7","year":2021},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.544679Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:18d0682cbbe28794cf4901d70cd8c4f681d985799b8a63bbe37b6493249635e7","observation_id":"5826920c-f232-4b52-a8bb-3bd4afbf0c31","resolution":{"observed_at":"2026-08-07T14:29:38.322825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17274","last_updated":"2022-06-03T17:52:04Z","snapshot_observed_at":"2026-07-06T12:55:27.843060Z","submitted_at":"2022-03-31T17:59:30Z","title":"Exploring Visual Prompts for Adapting Large-Scale Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17274","snapshot_observed_at":"2026-08-07T14:29:37.658386Z","title":"Exploring visual prompts for adapting large-scale models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.658386Z"},"links":{"cited_paper":"/paper/2203.17274","citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:b94c694f37bec9135acdbb674668e9ed6c172c69b7b7f2b25cf22b02a630f661","observation_id":"b2f9bdee-b5ef-488f-a24b-645e8e0715c4","resolution":{"observed_at":"2026-08-07T14:29:37.658386Z","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":"2026-08-07T14:29:37.864637Z","title":"Visual prompt tuning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T14:29:37.864637Z"},"links":{"citing_paper":"/paper/2505.18770"},"observation_digest":"sha256:9085d79c12416220373788b0b815e585538ef914bd7e667df11f358a88fa57b2","observation_id":"9d9006d5-5e40-44cd-bd74-25dd1dceaa55","resolution":{"observed_at":"2026-08-07T14:29:37.864637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.18770","last_updated":"2025-05-24T16:20:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T14:23:43.806027Z","submitted_at":"2025-05-24T16:20:06Z","title":"Dual-Path Stable Soft Prompt Generation for Domain Generalization"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":36},"total_outbound_references":60},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2505.18770."}