{"as_of":"2026-08-09T14:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3186d1e61ae8bc550adec9a29bda257d82ce79062d4e1ee2f2ae00af4a48a5c6","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:36:48.888398Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T11:18:44.433241Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.03103","last_updated":"2024-08-29T17:24:20Z","snapshot_observed_at":"2026-08-04T06:54:35.698693Z","submitted_at":"2023-10-04T18:47:34Z","title":"Learning to Prompt Your Domain for Vision-Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03103","snapshot_observed_at":"2026-08-06T22:36:48.888398Z","title":"Dual prompt tuning for domain-aware federated learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.21144","last_updated":"2025-06-26T10:59:14Z","snapshot_observed_at":"2026-08-07T22:02:20.239539Z","submitted_at":"2025-06-26T10:59:14Z","title":"Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:36:48.888398Z"},"links":{"cited_paper":"/paper/2310.03103","citing_paper":"/paper/2506.21144"},"observation_digest":"sha256:383e800b88c92cadcf772c9c5f3a0b64601c679fda9374f0c4a9893f178fc925","observation_id":"5bf8675a-b974-47d3-9de3-4399f4e4940b","resolution":{"observed_at":"2026-08-06T22:36:48.888398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03103","last_updated":"2024-08-29T17:24:20Z","snapshot_observed_at":"2026-08-04T06:54:35.698693Z","submitted_at":"2023-10-04T18:47:34Z","title":"Learning to Prompt Your Domain for Vision-Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03103","snapshot_observed_at":"2026-08-05T15:45:06.960861Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.19621","last_updated":"2025-08-27T06:59:10Z","snapshot_observed_at":"2026-08-06T17:39:48.062789Z","submitted_at":"2025-08-27T06:59:10Z","title":"Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T15:45:06.960861Z"},"links":{"cited_paper":"/paper/2310.03103","citing_paper":"/paper/2508.19621"},"observation_digest":"sha256:cf9ff78a508ed9d5ed4d317262e7a6d5a7840a2976f4851856df74939b829929","observation_id":"cb1e025e-3d7c-4fd4-ba7c-f631d62229b9","resolution":{"observed_at":"2026-08-05T15:45:06.960861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03103","last_updated":"2024-08-29T17:24:20Z","snapshot_observed_at":"2026-08-04T06:54:35.698693Z","submitted_at":"2023-10-04T18:47:34Z","title":"Learning to Prompt Your Domain for Vision-Language Models","version":5},"cited_work":{"arxiv_id":"2310.03103","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.03103","snapshot_observed_at":"2026-08-05T11:18:44.433241Z","title":"Learning to Prompt Your Domain for Vision-Language Models","venue":"cs.LG","work_id":"1880c1cf-7a8c-4be0-bc08-db9b98efbcb1","year":2023},"citing_paper":{"arxiv_id":"2509.06992","last_updated":"2025-09-03T03:46:35Z","snapshot_observed_at":"2026-08-08T12:25:37.091105Z","submitted_at":"2025-09-03T03:46:35Z","title":"FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T11:18:43.743402Z"},"links":{"cited_paper":"/paper/2310.03103","citing_paper":"/paper/2509.06992"},"observation_digest":"sha256:81e728eb95832acf0d732aa0b7bb781cbcdc22a7ecbbc5d90942c07549bf7eec","observation_id":"1896b4b4-1ccc-4be7-8f27-784984667722","resolution":{"observed_at":"2026-08-05T11:18:44.533863Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.03103/citation-record","integrity":"/paper/2310.03103/integrity","json":"/paper/2310.03103/citation-record.json","paper":"/paper/2310.03103"},"outbound":[],"paper":{"arxiv_id":"2310.03103","last_updated":"2024-08-29T17:24:20Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T06:54:35.698693Z","submitted_at":"2023-10-04T18:47:34Z","title":"Learning to Prompt Your Domain for Vision-Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.03103."}