{"as_of":"2026-08-09T17:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d03d6f70e4c61672eb8ba1a261984643f60334ecaa631819e3cb3b112a79983","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":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":17,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:02:28.305093Z","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":"2026-07-04T06:39:37.583391Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2307.06942","last_updated":"2024-01-04T05:00:34Z","snapshot_observed_at":"2026-07-06T15:53:46.393481Z","submitted_at":"2023-07-13T17:58:32Z","title":"InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-15T06:30:22.431538Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2307.06942"},"observation_digest":"sha256:68eadae95fe6d481a58858b527584b6335b83784e0cf9e08f1da48c3dd9f0376","observation_id":"875e6e67-3a47-4203-818f-5958c8b8cda0","resolution":{"observed_at":"2026-05-15T06:30:22.630119Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2310.01852","last_updated":"2024-01-22T03:11:15Z","snapshot_observed_at":"2026-08-07T05:10:33.059352Z","submitted_at":"2023-10-03T07:33:27Z","title":"LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment","version":7},"reference_index":200,"source":"arxiv_source","source_observed_at":"2026-05-17T03:27:58.952076Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2310.01852"},"observation_digest":"sha256:6c0a0f0b5bcb14ac648f2ed8b1d4eb35fc5a33f83884b55701f03e8b9cc49a26","observation_id":"c1994358-d629-48b1-a0c1-86163873df96","resolution":{"observed_at":"2026-05-17T03:27:59.093708Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-07T15:02:28.305093Z","title":"Available: https://arxiv.org/abs/2304.08345","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16594","last_updated":"2025-05-22T12:28:50Z","snapshot_observed_at":"2026-08-07T14:55:54.546955Z","submitted_at":"2025-05-22T12:28:50Z","title":"Temporal Object Captioning for Street Scene Videos from LiDAR Tracks","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:02:28.305093Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2505.16594"},"observation_digest":"sha256:9dffe7a44772ba222da1808f22220786dadbe04d56b234860039f185f10b33af","observation_id":"a349a548-3736-43a6-b28e-36458a90196b","resolution":{"observed_at":"2026-08-07T15:02:28.305093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-07T13:48:10.413560Z","title":"Valor: Vision-audio- language omni-perception pretraining model and dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20920","last_updated":"2025-05-27T09:10:59Z","snapshot_observed_at":"2026-08-09T07:43:10.959148Z","submitted_at":"2025-05-27T09:10:59Z","title":"HuMoCon: Concept Discovery for Human Motion Understanding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:48:10.413560Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2505.20920"},"observation_digest":"sha256:94a973b924cfcf14d4e5a226219a88654d49bd67c19543fc7014e8d2c30654dc","observation_id":"8ce6c9d6-75e6-4b1e-a17a-a4a5be0c0cc4","resolution":{"observed_at":"2026-08-07T13:48:10.413560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-06T22:24:42.615904Z","title":"arXiv preprint arXiv:2304.08345 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.21813","last_updated":"2025-06-26T23:25:23Z","snapshot_observed_at":"2026-08-08T09:29:41.133687Z","submitted_at":"2025-06-26T23:25:23Z","title":"CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:24:42.615904Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2506.21813"},"observation_digest":"sha256:ead2e8a0a2f264b5c6392fab97ea314e536a0fce6982928da6891daab10dfccb","observation_id":"fe5453a5-f02f-4f25-a966-97e60dd00d53","resolution":{"observed_at":"2026-08-06T22:24:42.615904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-06T19:43:43.140301Z","title":"arXiv preprint arXiv:2304.08345 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.04815","last_updated":"2025-07-07T09:33:19Z","snapshot_observed_at":"2026-08-09T08:45:09.436329Z","submitted_at":"2025-07-07T09:33:19Z","title":"From Vision To Language through Graph of Events in Space and Time: An Explainable Self-supervised Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:43:43.140301Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2507.04815"},"observation_digest":"sha256:e917a545409e6abf09e8dd61fb8e5e53f6bb0d47e7c0972be9b905b2a696ba97","observation_id":"df335657-6a11-42a5-8171-dcba49c06131","resolution":{"observed_at":"2026-08-06T19:43:43.140301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-06T15:48:26.258804Z","title":"Valor: Vision-audio- language omni-perception pretraining model and dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.14935","last_updated":"2025-07-20T12:09:19Z","snapshot_observed_at":"2026-08-09T02:21:57.024481Z","submitted_at":"2025-07-20T12:09:19Z","title":"Open-set Cross Modal Generalization via Multimodal Unified Representation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:48:26.258804Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2507.14935"},"observation_digest":"sha256:61b608c5716664658b91abe42fdf54f16cdaf909465926e22322ab2aee47cbd9","observation_id":"9eacc5aa-1288-437f-a5f3-2e665af6c317","resolution":{"observed_at":"2026-08-06T15:48:26.258804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2510.18346","last_updated":"2026-07-10T08:37:10Z","snapshot_observed_at":"2026-08-08T06:26:13.235254Z","submitted_at":"2025-10-21T06:58:34Z","title":"AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-18T05:26:07.722390Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2510.18346"},"observation_digest":"sha256:29c6167391de2ece1c21d4d8754b449c2c10e147c3844add5d456824ef60d72b","observation_id":"11881ef3-6e01-4b89-afc7-d619f895c333","resolution":{"observed_at":"2026-05-18T05:30:55.418387Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-04T08:55:25.662314Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.18346","last_updated":"2026-07-10T08:37:10Z","snapshot_observed_at":"2026-08-08T06:26:13.235254Z","submitted_at":"2025-10-21T06:58:34Z","title":"AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T08:55:25.662314Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2510.18346"},"observation_digest":"sha256:814875a65423ad70e2c8b5d2ad46f4de4b3b92691dba41ab67e00ad2244a4ac7","observation_id":"3306a6f6-bbbf-465c-af98-662b5806ab75","resolution":{"observed_at":"2026-08-04T08:55:25.662314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2511.12034","last_updated":"2026-05-12T07:28:48Z","snapshot_observed_at":"2026-08-02T17:34:14.031307Z","submitted_at":"2025-11-15T05:01:43Z","title":"Calibrated Multimodal Representation Learning with Missing Modalities","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-17T22:08:07.217659Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2511.12034"},"observation_digest":"sha256:559d879cd4171df60981413372b361a72d7b35b27632750bc170f402e6c18f7d","observation_id":"5dcaafb1-e84d-4b69-b176-9a7ecf78c673","resolution":{"observed_at":"2026-05-17T22:10:22.674286Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2511.21331","last_updated":"2026-04-03T11:14:20Z","snapshot_observed_at":"2026-07-06T22:37:03.716474Z","submitted_at":"2025-11-26T12:25:55Z","title":"The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-17T05:03:49.279871Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2511.21331"},"observation_digest":"sha256:43b5f0d7420824e6797068e83a63d5ca4ac07105cb6c5d470f192f875fb9c54b","observation_id":"781bc5ea-4cc7-4df0-a8a2-625cdf1dd5eb","resolution":{"observed_at":"2026-05-17T05:04:03.304830Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-03T17:16:36.862898Z","title":"Valor: Vision-audio- language omni-perception pretraining model and dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.10324","last_updated":"2026-06-22T06:41:41Z","snapshot_observed_at":"2026-08-03T17:16:35.340426Z","submitted_at":"2025-12-11T06:18:58Z","title":"EchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T17:16:36.862898Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2512.10324"},"observation_digest":"sha256:485e2609e5e54f68d50304e2f5f22ffd095142022f2a4df7dba8be18ed2390b6","observation_id":"faba092f-8f2e-4d0f-a247-3ac591ac731b","resolution":{"observed_at":"2026-08-03T17:16:36.862898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-08-03T15:26:12.988303Z","title":"Valor: Vision-audio- language omni-perception pretraining model and dataset","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.16978","last_updated":"2026-06-16T16:38:58Z","snapshot_observed_at":"2026-08-07T04:10:11.766593Z","submitted_at":"2025-12-18T18:59:27Z","title":"A Benchmark for Omni-Modal Reasoning in Long Videos","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T15:26:12.988303Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2512.16978"},"observation_digest":"sha256:59f6707f4a7ce00defa1a5855132f578f8248b4460505281048d5e481a90b558","observation_id":"daa03dbe-3f87-4a20-9165-9cdefb5e66bf","resolution":{"observed_at":"2026-08-03T15:26:12.988303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2604.14129","last_updated":"2026-04-15T17:51:28Z","snapshot_observed_at":"2026-07-06T23:02:00.082783Z","submitted_at":"2026-04-15T17:51:28Z","title":"Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-Visual Language Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T13:57:47.356373Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2604.14129"},"observation_digest":"sha256:288ba3c310ec574ac075ba63b785dc9f054d0bbba09d867cba930dbb5561aa68","observation_id":"9add115e-12de-41ea-b991-e34a409d30f9","resolution":{"observed_at":"2026-05-10T14:00:29.030971Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2606.12195","last_updated":"2026-06-10T15:17:08Z","snapshot_observed_at":"2026-08-01T02:09:41.655807Z","submitted_at":"2026-06-10T15:17:08Z","title":"InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning","version":1},"reference_index":216,"source":"arxiv_source","source_observed_at":"2026-06-27T09:48:27.652901Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2606.12195"},"observation_digest":"sha256:4ea056ce2e106c9096c510631fe195260f6d677fe140168ea226ee36b39eaaea","observation_id":"6519411a-63bd-4cfa-937e-b3dceaf101f2","resolution":{"observed_at":"2026-07-03T10:48:03.123950Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":"2304.08345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-04T06:39:37.583391Z","title":"Valor: Vision-audio-language omni-perception pretraining model and dataset","venue":null,"work_id":"aa26e74d-18c2-48ab-a2b5-ea304afa8dcc","year":2023},"citing_paper":{"arxiv_id":"2606.21734","last_updated":"2026-06-19T20:43:49Z","snapshot_observed_at":"2026-08-05T18:05:51.515234Z","submitted_at":"2026-06-19T20:43:49Z","title":"HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoning","version":1},"reference_index":299,"source":"arxiv_source","source_observed_at":"2026-06-26T14:19:53.450263Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2606.21734"},"observation_digest":"sha256:0b7f20df79f9cd99533a40658570f629fc5c4983555f281658bd2984e2612b32","observation_id":"cb6e2523-d51b-460a-b451-ff43e32fb131","resolution":{"observed_at":"2026-07-04T06:39:37.584884Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08345","snapshot_observed_at":"2026-07-14T12:48:58.688011Z","title":"arXiv preprint arXiv:2304.08345 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.10299","last_updated":"2026-07-11T13:04:24Z","snapshot_observed_at":"2026-08-08T22:46:15.287802Z","submitted_at":"2026-07-11T13:04:24Z","title":"Empowering Long-form Omni-modal Understanding with Robust Audio Perception","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T12:48:58.688011Z"},"links":{"cited_paper":"/paper/2304.08345","citing_paper":"/paper/2607.10299"},"observation_digest":"sha256:89a872d21bbc97009dc3b486e72f7f335defeca852048103815fbb4f00996f7d","observation_id":"018b58cc-c35a-49bc-acd4-6ad7c7afb02a","resolution":{"observed_at":"2026-07-14T12:48:58.688011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2304.08345/citation-record","integrity":"/paper/2304.08345/integrity","json":"/paper/2304.08345/citation-record.json","paper":"/paper/2304.08345"},"outbound":[],"paper":{"arxiv_id":"2304.08345","last_updated":"2025-01-06T09:10:55Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T02:26:34.656486Z","submitted_at":"2023-04-17T15:08:15Z","title":"VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset"},"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 17 inbound Pith citation observations for arXiv:2304.08345."}