{"as_of":"2026-08-08T14:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:552307d25d9ba3a3b2dadca394c12916b09418ee04b9cdaa1dc69522046b745e","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:29:23.701411Z","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":"2026-05-25T04:55:23.983995Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.00575","last_updated":"2022-11-01T16:36:01Z","snapshot_observed_at":"2026-07-06T14:13:07.158001Z","submitted_at":"2022-11-01T16:36:01Z","title":"Text-Only Training for Image Captioning using Noise-Injected CLIP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.00575","snapshot_observed_at":"2026-08-07T04:29:23.701411Z","title":"Text-only train- ing for image captioning using noise-injected clip.arXiv preprint arXiv:2211.00575, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10575","last_updated":"2025-06-12T11:09:49Z","snapshot_observed_at":"2026-08-07T05:42:07.009165Z","submitted_at":"2025-06-12T11:09:49Z","title":"Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T04:29:23.701411Z"},"links":{"cited_paper":"/paper/2211.00575","citing_paper":"/paper/2506.10575"},"observation_digest":"sha256:99cfb877f3faaa569b45d31c1b62c90791cd71b46038b7c0adfeb95f60ad2828","observation_id":"09055feb-c934-45bb-b5b2-a5b9304eaafe","resolution":{"observed_at":"2026-08-07T04:29:23.701411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00575","last_updated":"2022-11-01T16:36:01Z","snapshot_observed_at":"2026-07-06T14:13:07.158001Z","submitted_at":"2022-11-01T16:36:01Z","title":"Text-Only Training for Image Captioning using Noise-Injected CLIP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.00575","snapshot_observed_at":"2026-08-06T21:51:55.746646Z","title":"Text-only training for image captioning using noise- injected clip","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23283","last_updated":"2025-06-29T15:14:55Z","snapshot_observed_at":"2026-08-08T07:28:03.599688Z","submitted_at":"2025-06-29T15:14:55Z","title":"MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T21:51:55.746646Z"},"links":{"cited_paper":"/paper/2211.00575","citing_paper":"/paper/2506.23283"},"observation_digest":"sha256:3882a47e90b07e4891aaab63a9fdae23d5af697b28a465fb180d183c4e8de113","observation_id":"00a9a893-7320-4bb5-884a-67d9c6c1167c","resolution":{"observed_at":"2026-08-06T21:51:55.746646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00575","last_updated":"2022-11-01T16:36:01Z","snapshot_observed_at":"2026-07-06T14:13:07.158001Z","submitted_at":"2022-11-01T16:36:01Z","title":"Text-Only Training for Image Captioning using Noise-Injected CLIP","version":1},"cited_work":{"arxiv_id":"2211.00575","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.00575","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Text-only training for image captioning using noise-injected clip.arXiv preprint arXiv:2211.00575","venue":null,"work_id":"713a8c1a-3ad4-46c4-800b-ab851d795283","year":2022},"citing_paper":{"arxiv_id":"2602.07026","last_updated":"2026-06-05T02:27:27Z","snapshot_observed_at":"2026-08-08T04:43:15.472097Z","submitted_at":"2026-02-02T13:59:39Z","title":"Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-16T08:17:29.924860Z"},"links":{"cited_paper":"/paper/2211.00575","citing_paper":"/paper/2602.07026"},"observation_digest":"sha256:390ba24a65881dfa740336164261c8c9dd92c69782baea00fba396f0657c3bac","observation_id":"c8e3154c-0770-4be3-9f31-d75c9ecab9e0","resolution":{"observed_at":"2026-05-16T08:17:36.066278Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2211.00575","last_updated":"2022-11-01T16:36:01Z","snapshot_observed_at":"2026-07-06T14:13:07.158001Z","submitted_at":"2022-11-01T16:36:01Z","title":"Text-Only Training for Image Captioning using Noise-Injected CLIP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.00575","snapshot_observed_at":"2026-08-03T05:34:28.659477Z","title":"Text-only training for image captioning using noise-injected clip","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.07026","last_updated":"2026-06-05T02:27:27Z","snapshot_observed_at":"2026-08-08T04:43:15.472097Z","submitted_at":"2026-02-02T13:59:39Z","title":"Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T05:34:28.659477Z"},"links":{"cited_paper":"/paper/2211.00575","citing_paper":"/paper/2602.07026"},"observation_digest":"sha256:2b0805f2a5602813b1e23c125de4705ccfba551a68daca3d831e9d199a0b802e","observation_id":"0e6a904c-6801-4af9-a36f-b62e22c7ec4a","resolution":{"observed_at":"2026-08-03T05:34:28.659477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00575","last_updated":"2022-11-01T16:36:01Z","snapshot_observed_at":"2026-07-06T14:13:07.158001Z","submitted_at":"2022-11-01T16:36:01Z","title":"Text-Only Training for Image Captioning using Noise-Injected CLIP","version":1},"cited_work":{"arxiv_id":"2211.00575","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.00575","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Text-only training for image captioning using noise-injected clip.arXiv preprint arXiv:2211.00575","venue":null,"work_id":"713a8c1a-3ad4-46c4-800b-ab851d795283","year":2022},"citing_paper":{"arxiv_id":"2605.23171","last_updated":"2026-05-22T02:43:19Z","snapshot_observed_at":"2026-08-01T19:48:27.330086Z","submitted_at":"2026-05-22T02:43:19Z","title":"Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T04:51:05.359636Z"},"links":{"cited_paper":"/paper/2211.00575","citing_paper":"/paper/2605.23171"},"observation_digest":"sha256:81753659e0a2b4cf58e80afc48677441c6f209b44bf7e0af0a5deb5da5146831","observation_id":"d641bb7a-ff3d-4379-8ba9-ffb19b54360d","resolution":{"observed_at":"2026-05-25T04:55:23.988089Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2211.00575/citation-record","integrity":"/paper/2211.00575/integrity","json":"/paper/2211.00575/citation-record.json","paper":"/paper/2211.00575"},"outbound":[],"paper":{"arxiv_id":"2211.00575","last_updated":"2022-11-01T16:36:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T14:13:07.158001Z","submitted_at":"2022-11-01T16:36:01Z","title":"Text-Only Training for Image Captioning using Noise-Injected CLIP"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2211.00575."}