{"as_of":"2026-08-11T10:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d9b8136b08ae0036a8ef3356f6ae19b2a15a8a56985e9927d094018fa3916517","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":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":20,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:41:33.517914Z","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":"2026-07-03T17:18:43.867707Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2411.10442","last_updated":"2025-04-07T09:09:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-11-15T18:59:27Z","title":"Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-16T09:16:17.150383Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2411.10442"},"observation_digest":"sha256:852f13f28d239d92df35fa2b94ab81f0aa5df0467a12671707ae5a5452e8370a","observation_id":"824f55b6-504a-4d89-aa60-d90613f33225","resolution":{"observed_at":"2026-05-16T09:16:17.260929Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2412.05271","last_updated":"2025-09-26T12:52:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-06T18:57:08Z","title":"Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling","version":5},"reference_index":133,"source":"pdf_text","source_observed_at":"2026-05-10T13:23:57.588851Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2412.05271"},"observation_digest":"sha256:319e1ef4914863dffe0a6db13ff2c3ebcbe099880a8e09bb2d0cf25b02c745a2","observation_id":"6f2357ce-2b2b-4ee5-b88d-a80f4916dbc8","resolution":{"observed_at":"2026-05-10T13:23:58.160664Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-08-10T22:41:33.517914Z","title":"Omnicorpus: An unified mul- timodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00958","last_updated":"2025-05-13T16:29:08Z","snapshot_observed_at":"2026-08-11T03:06:20.615484Z","submitted_at":"2025-01-01T21:29:37Z","title":"2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:41:33.517914Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2501.00958"},"observation_digest":"sha256:0311646f610bc395db35d516be3c85c88c530d504a486008184a84f5d943fdbf","observation_id":"ac0ba78b-5f72-4496-89f4-a62cafe8728f","resolution":{"observed_at":"2026-08-10T22:41:33.517914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2501.12386","last_updated":"2025-07-13T18:57:17Z","snapshot_observed_at":"2026-08-06T07:17:05.291678Z","submitted_at":"2025-01-21T18:59:00Z","title":"InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-17T02:52:20.643070Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2501.12386"},"observation_digest":"sha256:68402f47c040ca386e57541f4cd1a45504483b5900ed4f2a5947e133bb65839a","observation_id":"f1b88b23-ddb9-408e-bb7e-bfcb08664a1d","resolution":{"observed_at":"2026-05-17T02:52:20.735829Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-08-10T18:04:34.989102Z","title":"OmniCorpus: A unified multimodal corpus of 10 billion-level images in- terleaved with text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.14818","last_updated":"2025-01-20T18:40:47Z","snapshot_observed_at":"2026-08-11T01:59:38.596539Z","submitted_at":"2025-01-20T18:40:47Z","title":"Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models","version":1},"reference_index":221,"source":"pdf_text","source_observed_at":"2026-08-10T18:04:34.989102Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2501.14818"},"observation_digest":"sha256:8e4a389eccf869bd2c5756f754eb05d606af0f48d43a1548db6e88357fdb3266","observation_id":"85ae94bf-1568-48f6-8ec1-a7df8729faeb","resolution":{"observed_at":"2026-08-10T18:04:34.989102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2505.14683","last_updated":"2025-07-27T11:45:16Z","snapshot_observed_at":"2026-08-11T02:56:05.992492Z","submitted_at":"2025-05-20T17:59:30Z","title":"Emerging Properties in Unified Multimodal Pretraining","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T16:23:41.854132Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2505.14683"},"observation_digest":"sha256:d6b5d205b19360cbeeecb63f445e92fd042b0577fcd038762dc0eaeb6da0de4f","observation_id":"e957f681-90ad-44fd-b6e4-8a60d50f561c","resolution":{"observed_at":"2026-05-10T16:23:42.093546Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2507.01006","last_updated":"2026-01-01T13:07:25Z","snapshot_observed_at":"2026-08-03T18:50:30.558321Z","submitted_at":"2025-07-01T17:55:04Z","title":"GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning","version":6},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-11T04:48:26.355351Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2507.01006"},"observation_digest":"sha256:831b2dd1c87e16adbbdde0b555272f83e9784486b1ddc0f8cdb0d7c7c68682af","observation_id":"43d12450-2c33-4b38-841a-93432052401f","resolution":{"observed_at":"2026-05-11T04:48:26.861808Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-08-06T15:57:02.050118Z","title":"Omnicorpus: An unified mul- timodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14675","last_updated":"2025-07-19T16:03:34Z","snapshot_observed_at":"2026-08-09T00:51:52.133273Z","submitted_at":"2025-07-19T16:03:34Z","title":"Docopilot: Improving Multimodal Models for Document-Level Understanding","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T15:57:02.050118Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2507.14675"},"observation_digest":"sha256:5d9b064817d7d4720efedab0325e22514de194fb0f15d3644a4d2128091eca89","observation_id":"c852b742-35d6-41e3-8e07-17ec85210c5f","resolution":{"observed_at":"2026-08-06T15:57:02.050118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-08-06T00:59:52.075206Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04088","last_updated":"2025-08-07T03:52:48Z","snapshot_observed_at":"2026-08-08T12:10:28.774282Z","submitted_at":"2025-08-06T05:10:29Z","title":"GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T00:59:52.075206Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2508.04088"},"observation_digest":"sha256:6baa57ce59536b7bfde82e298eafb8a17bc84fc3168ac6835eaa4dc9ff4ff852","observation_id":"34bf0cf9-76bf-47c6-a541-abe3413a2711","resolution":{"observed_at":"2026-08-06T00:59:52.075206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2509.18154","last_updated":"2025-09-16T19:41:48Z","snapshot_observed_at":"2026-08-09T11:10:24.301223Z","submitted_at":"2025-09-16T19:41:48Z","title":"MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T17:07:27.277040Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2509.18154"},"observation_digest":"sha256:473b23b90a46fd9e896b6021c7cba9d92125e26aa88c1dcb463101e504967788","observation_id":"ab0f426d-e632-49d3-9d1c-f60c320fcc9e","resolution":{"observed_at":"2026-05-15T17:07:27.436302Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-08-04T09:54:25.305850Z","title":"Qingyun Li, Zhe Chen, Weiyun Wang, Wenhai Wang, Shenglong Ye, Zhenjiang Jin, Guanzhou Chen, Yinan He, Zhangwei Gao, Erfei Cui, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.12784","last_updated":"2026-06-28T11:09:54Z","snapshot_observed_at":"2026-08-04T09:54:23.338683Z","submitted_at":"2025-10-14T17:56:11Z","title":"SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T09:54:25.305850Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2510.12784"},"observation_digest":"sha256:3e02e8a9c36ed6ad073f09228714b46b7286cf06f9a6b2c82fdc1838b2ccdde2","observation_id":"17a19361-0ea5-4e8b-9ff8-16d408649dd5","resolution":{"observed_at":"2026-08-04T09:54:25.305850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2604.08644","last_updated":"2026-04-09T17:51:11Z","snapshot_observed_at":"2026-08-11T06:34:32.751399Z","submitted_at":"2026-04-09T17:51:11Z","title":"EXAONE 4.5 Technical Report","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T17:47:34.692414Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2604.08644"},"observation_digest":"sha256:e8aca6272fe3bc324e2bc1cb2ed91c4f627b9964a150e618b66ddb4ea9d20819","observation_id":"fe5a3143-1304-4a6d-8fd8-71012da7726d","resolution":{"observed_at":"2026-05-11T06:10:57.656341Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2605.12305","last_updated":"2026-05-12T15:54:49Z","snapshot_observed_at":"2026-07-06T23:24:03.984179Z","submitted_at":"2026-05-12T15:54:49Z","title":"Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-13T05:48:04.997796Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2605.12305"},"observation_digest":"sha256:52384fe44ab57291fae4b33f08baa992848cf4f26f7f23f6e71ebf8ab0e80bfd","observation_id":"ed4986ff-549e-4aee-8c4a-8a21cccce593","resolution":{"observed_at":"2026-05-13T05:52:22.782843Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2605.22344","last_updated":"2026-05-21T11:30:29Z","snapshot_observed_at":"2026-07-06T23:32:39.761431Z","submitted_at":"2026-05-21T11:30:29Z","title":"Bernini: Latent Semantic Planning for Video Diffusion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-22T06:39:47.124605Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2605.22344"},"observation_digest":"sha256:92bc496ac8e936fbfc1970aae5406a4b0187511fb71e22d2cef9845e53060641","observation_id":"83bdc400-7f10-406d-b2a6-a5c5268b0e7f","resolution":{"observed_at":"2026-05-22T06:41:10.547052Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2606.11188","last_updated":"2026-06-09T17:59:28Z","snapshot_observed_at":"2026-08-11T07:11:46.352755Z","submitted_at":"2026-06-09T17:59:28Z","title":"ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-27T13:29:11.526106Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2606.11188"},"observation_digest":"sha256:cc49aaede6327041016f6fead58ef28cfbccd4f7b2ff14e84aa4e5dcbd6579fb","observation_id":"7e73a6c3-6dba-435c-8b6c-48fc1e285a9e","resolution":{"observed_at":"2026-07-03T05:07:38.515381Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2606.17030","last_updated":"2026-06-17T13:54:57Z","snapshot_observed_at":"2026-08-01T21:48:31.832288Z","submitted_at":"2026-06-15T17:52:31Z","title":"Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-06-27T04:19:26.332718Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2606.17030"},"observation_digest":"sha256:fef104905bf47de514a24678738f8b1143c4e8076b169388077500b7a7b3ee11","observation_id":"5f6ca5a9-977d-4d38-a4c9-93d2453d2ecf","resolution":{"observed_at":"2026-07-03T17:18:43.868974Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2606.28551","last_updated":"2026-08-10T01:17:41Z","snapshot_observed_at":"2026-08-11T09:17:21.653485Z","submitted_at":"2026-06-26T19:11:29Z","title":"DataComp-VLM: Improved Open Datasets for Vision-Language Models","version":1},"reference_index":161,"source":"pdf_text","source_observed_at":"2026-06-30T01:16:16.834861Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2606.28551"},"observation_digest":"sha256:0f894791434106125f89592218af9c469edafcda1150ef44655a564e39d9e1d7","observation_id":"9204d090-fde8-4020-8c14-a098b4082c56","resolution":{"observed_at":"2026-07-01T15:45:47.667104Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":"2406.08418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-03T17:18:43.867707Z","title":"Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text","venue":null,"work_id":"7d9f48da-319d-4a13-8604-4f435eb9c224","year":2024},"citing_paper":{"arxiv_id":"2606.28551","last_updated":"2026-08-10T01:17:41Z","snapshot_observed_at":"2026-08-11T09:17:21.653485Z","submitted_at":"2026-06-26T19:11:29Z","title":"DataComp-VLM: Improved Open Datasets for Vision-Language Models","version":2},"reference_index":161,"source":"pdf_text","source_observed_at":"2026-07-02T21:10:10.548489Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2606.28551"},"observation_digest":"sha256:0cfccab3c67aaf4cd385cacc9d1157a5228fdd3cb8e6927aa4f994eb9fe88da8","observation_id":"be8464c3-3919-4ec1-a51e-1d77cc30a5a5","resolution":{"observed_at":"2026-07-02T21:17:23.935699Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-12T04:17:40.198357Z","title":"arXiv preprint arXiv:2406.08418 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03184","last_updated":"2026-07-03T10:42:34Z","snapshot_observed_at":"2026-08-05T15:07:18.952856Z","submitted_at":"2026-07-03T10:42:34Z","title":"BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception","version":1},"reference_index":300,"source":"arxiv_source","source_observed_at":"2026-07-12T04:17:40.198357Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2607.03184"},"observation_digest":"sha256:a69a264b7876b0091962c3b0ccb2c291f323378d0643c3109bacb8e39f300fcb","observation_id":"c0cf02e3-bc76-4a1f-8f75-b1509b5be0f5","resolution":{"observed_at":"2026-07-12T04:17:40.198357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08418","snapshot_observed_at":"2026-07-12T01:50:59.184754Z","title":"arXiv preprint arXiv:2406.08418 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03530","last_updated":"2026-07-03T17:59:58Z","snapshot_observed_at":"2026-08-04T18:44:44.436706Z","submitted_at":"2026-07-03T17:59:58Z","title":"MentalThink: Shaping Thoughts in Mental SVG World","version":1},"reference_index":233,"source":"arxiv_source","source_observed_at":"2026-07-12T01:50:59.184754Z"},"links":{"cited_paper":"/paper/2406.08418","citing_paper":"/paper/2607.03530"},"observation_digest":"sha256:363cbe008277369ff0eecfd2074e3c8723d382366f8a4f135bb9e2fbff276f5e","observation_id":"650ce7e9-078c-454c-9954-ca660d138e27","resolution":{"observed_at":"2026-07-12T01:50:59.184754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.08418/citation-record","integrity":"/paper/2406.08418/integrity","json":"/paper/2406.08418/citation-record.json","paper":"/paper/2406.08418"},"outbound":[],"paper":{"arxiv_id":"2406.08418","last_updated":"2024-07-12T08:54:51Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T09:18:59.444343Z","submitted_at":"2024-06-12T17:01:04Z","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2406.08418."}