{"as_of":"2026-08-20T06:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed20d8be2a81e94ecadd625d4c23d409a09ec16260a08f4fd7bd42cc9b2378b6","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":"2026-08-19T06:32:44.657259+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":"2026-08-01T17:56:04.618489Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T04:17:37.211006Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.11031","last_updated":"2024-12-07T06:57:05Z","snapshot_observed_at":"2026-08-19T16:05:26.961585Z","submitted_at":"2023-10-17T07:01:24Z","title":"Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters","version":2},"cited_work":{"arxiv_id":"2310.11031","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.11031","snapshot_observed_at":"2026-07-03T04:17:37.211006Z","title":"Domain generalization using large pretrained models with mixture-of-adapters,","venue":null,"work_id":"cb759046-587b-42ff-9061-0ac7c0bb8d11","year":2023},"citing_paper":{"arxiv_id":"2605.26230","last_updated":"2026-05-25T18:01:05Z","snapshot_observed_at":"2026-08-15T16:53:28.323305Z","submitted_at":"2026-05-25T18:01:05Z","title":"Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T23:08:52.333329Z"},"links":{"cited_paper":"/paper/2310.11031","citing_paper":"/paper/2605.26230"},"observation_digest":"sha256:7c9779cbf6d92ded0478c08ab2988fbdaec149683d41bd73a8c33bf43674f083","observation_id":"5727332b-22af-40d8-9756-262832d2ca7a","resolution":{"observed_at":"2026-06-29T23:14:01.794623Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11031","last_updated":"2024-12-07T06:57:05Z","snapshot_observed_at":"2026-08-19T16:05:26.961585Z","submitted_at":"2023-10-17T07:01:24Z","title":"Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters","version":2},"cited_work":{"arxiv_id":"2310.11031","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.11031","snapshot_observed_at":"2026-07-03T04:17:37.211006Z","title":"Domain generalization using large pretrained models with mixture-of-adapters,","venue":null,"work_id":"cb759046-587b-42ff-9061-0ac7c0bb8d11","year":2023},"citing_paper":{"arxiv_id":"2606.10488","last_updated":"2026-06-09T07:03:43Z","snapshot_observed_at":"2026-08-13T14:52:21.771955Z","submitted_at":"2026-06-09T07:03:43Z","title":"5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T14:03:24.516255Z"},"links":{"cited_paper":"/paper/2310.11031","citing_paper":"/paper/2606.10488"},"observation_digest":"sha256:04ca1a8c1a10d1ce46b4eec7b9f8fb6c90bc1bdb20a0d7d3cfc8fe0548a46aca","observation_id":"46184e6a-e913-42c1-bf72-2f8db9399d86","resolution":{"observed_at":"2026-07-03T04:17:37.212708Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11031","last_updated":"2024-12-07T06:57:05Z","snapshot_observed_at":"2026-08-19T16:05:26.961585Z","submitted_at":"2023-10-17T07:01:24Z","title":"Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.11031","snapshot_observed_at":"2026-08-01T17:56:04.618489Z","title":"arXiv preprint arXiv:2310.11031 (2024),https://arxiv.org/abs/2310.11031","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17467","last_updated":"2026-07-20T01:45:37Z","snapshot_observed_at":"2026-08-16T03:45:14.358223Z","submitted_at":"2026-07-20T01:45:37Z","title":"DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T17:56:04.618489Z"},"links":{"cited_paper":"/paper/2310.11031","citing_paper":"/paper/2607.17467"},"observation_digest":"sha256:f26bddf175a2121c6cd851356005b2c66a37135f35e1d2af45a9daf2548a4e01","observation_id":"a7209241-caf5-4928-822d-21ad14d75f73","resolution":{"observed_at":"2026-08-01T17:56:04.618489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2310.11031/citation-record","integrity":"/paper/2310.11031/integrity","json":"/paper/2310.11031/citation-record.json","paper":"/paper/2310.11031"},"outbound":[],"paper":{"arxiv_id":"2310.11031","last_updated":"2024-12-07T06:57:05Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T16:05:26.961585Z","submitted_at":"2023-10-17T07:01:24Z","title":"Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.11031."}