{"as_of":"2026-08-10T11:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c388efb3396f5065833ff3d31063617b6c445d63c6defbd5352686e4786e633e","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T15:31:16.918337Z","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":"2026-05-16T15:21:00.904134Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought","version":3},"cited_work":{"arxiv_id":"2305.13903","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.13903","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Let’s think frame by frame: Evaluating video chain of thought with video infilling and prediction","venue":null,"work_id":"96f1e325-4d38-4a70-b4b5-0bd2a8ae9a15","year":2023},"citing_paper":{"arxiv_id":"2306.13549","last_updated":"2024-11-29T15:51:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-23T15:21:52Z","title":"A Survey on Multimodal Large Language Models","version":4},"reference_index":189,"source":"pdf_text","source_observed_at":"2026-05-16T02:56:41.658658Z"},"links":{"cited_paper":"/paper/2305.13903","citing_paper":"/paper/2306.13549"},"observation_digest":"sha256:496d8a031a1bc5015d95b613901e77b9928e27ec8c6ba97d88b44235c2607fca","observation_id":"4707c3c2-93dc-4bb6-a532-5c211b47e5bb","resolution":{"observed_at":"2026-05-16T02:56:42.169118Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought","version":3},"cited_work":{"arxiv_id":"2305.13903","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.13903","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Let’s think frame by frame: Evaluating video chain of thought with video infilling and prediction","venue":null,"work_id":"96f1e325-4d38-4a70-b4b5-0bd2a8ae9a15","year":2023},"citing_paper":{"arxiv_id":"2309.05922","last_updated":"2023-09-12T02:34:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-12T02:34:06Z","title":"A Survey of Hallucination in Large Foundation Models","version":1},"reference_index":122,"source":"arxiv_source","source_observed_at":"2026-05-16T15:21:00.778049Z"},"links":{"cited_paper":"/paper/2305.13903","citing_paper":"/paper/2309.05922"},"observation_digest":"sha256:1322b9d5c9ba60b448b472dc3c61d908e34be88ecdfb85dbf4e32d195deccb94","observation_id":"d0be8ea1-5ec6-4bec-ae78-8ae0d3253522","resolution":{"observed_at":"2026-05-16T15:21:00.906448Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13903","snapshot_observed_at":"2026-08-08T15:31:16.918337Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06428","last_updated":"2025-02-11T14:59:25Z","snapshot_observed_at":"2026-08-10T08:26:15.411427Z","submitted_at":"2025-02-10T13:03:05Z","title":"CoS: Chain-of-Shot Prompting for Long Video Understanding","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T15:31:16.918337Z"},"links":{"cited_paper":"/paper/2305.13903","citing_paper":"/paper/2502.06428"},"observation_digest":"sha256:1bc0c2f416439e49c7bfdb2d53e85bcf4dbc09f5f2aac83d4446dd60b015b040","observation_id":"219f3a82-976a-4de4-a9b6-24ae8631d8be","resolution":{"observed_at":"2026-08-08T15:31:16.918337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought","version":3},"cited_work":{"arxiv_id":"2305.13903","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.13903","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Let’s think frame by frame: Evaluating video chain of thought with video infilling and prediction","venue":null,"work_id":"96f1e325-4d38-4a70-b4b5-0bd2a8ae9a15","year":2023},"citing_paper":{"arxiv_id":"2503.12605","last_updated":"2025-03-23T13:47:43Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-16T18:39:13Z","title":"Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey","version":2},"reference_index":110,"source":"pdf_text","source_observed_at":"2026-05-15T17:18:52.996467Z"},"links":{"cited_paper":"/paper/2305.13903","citing_paper":"/paper/2503.12605"},"observation_digest":"sha256:a04d2b45c4f4949e069c522aaf9a1fc03f607bcf6c7cea5549323744c1074242","observation_id":"6feb924b-87b2-4017-bae1-b50e6550acd2","resolution":{"observed_at":"2026-05-15T17:18:53.132781Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13903","snapshot_observed_at":"2026-08-06T17:49:34.791982Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09876","last_updated":"2025-07-14T03:21:13Z","snapshot_observed_at":"2026-08-08T07:59:40.732848Z","submitted_at":"2025-07-14T03:21:13Z","title":"ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:49:34.791982Z"},"links":{"cited_paper":"/paper/2305.13903","citing_paper":"/paper/2507.09876"},"observation_digest":"sha256:61339132c951b15d2f575d56818992269a2909319d61e9a782f6e5bc4e74632a","observation_id":"34ee85ce-7b5b-4e31-ad35-2200692b9b20","resolution":{"observed_at":"2026-08-06T17:49:34.791982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13903","snapshot_observed_at":"2026-08-05T20:28:57.194938Z","title":"Himakunthala et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.10955","last_updated":"2025-08-14T07:25:45Z","snapshot_observed_at":"2026-08-09T12:54:37.629481Z","submitted_at":"2025-08-14T07:25:45Z","title":"Empowering Multimodal LLMs with External Tools: A Comprehensive Survey","version":1},"reference_index":154,"source":"arxiv_source","source_observed_at":"2026-08-05T20:28:57.194938Z"},"links":{"cited_paper":"/paper/2305.13903","citing_paper":"/paper/2508.10955"},"observation_digest":"sha256:816cfbdc1c60aadb966735cab8cdb7e33459958fd656e7cd89997e4935dfc7a9","observation_id":"b440622a-30ba-44b6-94be-850c0c53cbba","resolution":{"observed_at":"2026-08-05T20:28:57.194938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2305.13903/citation-record","integrity":"/paper/2305.13903/integrity","json":"/paper/2305.13903/citation-record.json","paper":"/paper/2305.13903"},"outbound":[],"paper":{"arxiv_id":"2305.13903","last_updated":"2023-11-09T06:50:26Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T10:26:42Z","title":"Let's Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.13903."}