{"as_of":"2026-08-07T05:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03ead8c67ffff3de3c188c7f846ee123591f5636d5d204222f7679524e9a7d6e","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-07T16:58:33.014401Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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":"2026-08-02T08:21:43.194828Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-09T03:05:55.283109Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"cited_work":{"arxiv_id":"2604.25427","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.25427","snapshot_observed_at":"2026-07-09T03:05:55.283109Z","title":"A Systematic Post-Train Framework for Video Generation","venue":"cs.CV","work_id":"447433fb-ea71-4357-8c19-53b528d34300","year":2026},"citing_paper":{"arxiv_id":"2605.07061","last_updated":"2026-05-29T22:08:19Z","snapshot_observed_at":"2026-08-03T12:53:30.755817Z","submitted_at":"2026-05-08T00:14:07Z","title":"Do Joint Audio-Video Generation Models Understand Physics?","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-11T02:12:04.230076Z"},"links":{"cited_paper":"/paper/2604.25427","citing_paper":"/paper/2605.07061"},"observation_digest":"sha256:f7db3fbf7ce464374582d1c39847181a45b9a7c0a3fcb56b6ddfec3c999b01b2","observation_id":"073d1283-3e1c-4cb7-a813-86b742ff8b35","resolution":{"observed_at":"2026-05-11T03:50:56.786431Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"cited_work":{"arxiv_id":"2604.25427","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.25427","snapshot_observed_at":"2026-07-09T03:05:55.283109Z","title":"A Systematic Post-Train Framework for Video Generation","venue":"cs.CV","work_id":"447433fb-ea71-4357-8c19-53b528d34300","year":2026},"citing_paper":{"arxiv_id":"2605.07061","last_updated":"2026-05-29T22:08:19Z","snapshot_observed_at":"2026-08-03T12:53:30.755817Z","submitted_at":"2026-05-08T00:14:07Z","title":"Do Joint Audio-Video Generation Models Understand Physics?","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-30T23:39:22.070629Z"},"links":{"cited_paper":"/paper/2604.25427","citing_paper":"/paper/2605.07061"},"observation_digest":"sha256:0abf88d6649b38fa559aceb993d5060f4a9e863840c3e886cccfe0cc1fcfbfa6","observation_id":"f7c47df5-db7c-48c4-af0f-dd15dd4d8eca","resolution":{"observed_at":"2026-06-30T23:45:08.213661Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"cited_work":{"arxiv_id":"2604.25427","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.25427","snapshot_observed_at":"2026-07-09T03:05:55.283109Z","title":"A Systematic Post-Train Framework for Video Generation","venue":"cs.CV","work_id":"447433fb-ea71-4357-8c19-53b528d34300","year":2026},"citing_paper":{"arxiv_id":"2605.15458","last_updated":"2026-05-14T22:40:56Z","snapshot_observed_at":"2026-08-01T23:49:14.751351Z","submitted_at":"2026-05-14T22:40:56Z","title":"Video Models Can Reason with Verifiable Rewards","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-19T15:03:14.894952Z"},"links":{"cited_paper":"/paper/2604.25427","citing_paper":"/paper/2605.15458"},"observation_digest":"sha256:8c372938a6d53a5fa978ebdcbac5b81a90db8afd7242dcbadcfdcc49d8c1100b","observation_id":"7604cd95-15c1-495a-89c8-62fec200cced","resolution":{"observed_at":"2026-05-19T15:07:37.126508Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"cited_work":{"arxiv_id":"2604.25427","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.25427","snapshot_observed_at":"2026-07-09T03:05:55.283109Z","title":"A Systematic Post-Train Framework for Video Generation","venue":"cs.CV","work_id":"447433fb-ea71-4357-8c19-53b528d34300","year":2026},"citing_paper":{"arxiv_id":"2607.06173","last_updated":"2026-07-26T20:43:47Z","snapshot_observed_at":"2026-08-03T04:45:08.357712Z","submitted_at":"2026-07-07T11:52:46Z","title":"MobileWan: Closing the Quality Gap for Mobile Video Diffusion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-08T14:37:46.957265Z"},"links":{"cited_paper":"/paper/2604.25427","citing_paper":"/paper/2607.06173"},"observation_digest":"sha256:ef5548536460f456d80bba5b14c7fcd1ff746335fd78c6adee4db57a0752e94d","observation_id":"7b48b3c1-cc18-44d4-8119-7bf3cdc9ff70","resolution":{"observed_at":"2026-07-08T14:44:59.787950Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.25427","snapshot_observed_at":"2026-08-02T08:21:43.194828Z","title":"A systematic post-train framework for video generation.arXiv preprint arXiv:2604.25427, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.06173","last_updated":"2026-07-26T20:43:47Z","snapshot_observed_at":"2026-08-03T04:45:08.357712Z","submitted_at":"2026-07-07T11:52:46Z","title":"MobileWan: Closing the Quality Gap for Mobile Video Diffusion","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T08:21:43.194828Z"},"links":{"cited_paper":"/paper/2604.25427","citing_paper":"/paper/2607.06173"},"observation_digest":"sha256:ddf2da8418b2c10f3c2dbb448670b67968fa60eb4259ee60556a25e79250fd4b","observation_id":"329de75c-2e64-409b-86ad-fe1e2eb22bbe","resolution":{"observed_at":"2026-08-02T08:21:43.194828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"cited_work":{"arxiv_id":"2604.25427","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.25427","snapshot_observed_at":"2026-07-09T03:05:55.283109Z","title":"A Systematic Post-Train Framework for Video Generation","venue":"cs.CV","work_id":"447433fb-ea71-4357-8c19-53b528d34300","year":2026},"citing_paper":{"arxiv_id":"2607.07675","last_updated":"2026-07-08T17:33:40Z","snapshot_observed_at":"2026-08-02T22:09:26.507433Z","submitted_at":"2026-07-08T17:33:40Z","title":"Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence","version":1},"reference_index":118,"source":"pdf_text","source_observed_at":"2026-07-09T02:55:58.018234Z"},"links":{"cited_paper":"/paper/2604.25427","citing_paper":"/paper/2607.07675"},"observation_digest":"sha256:7aeb518011ecc168ad6e783ec11ec49bf69ae1b923b731a83dee3678a819f103","observation_id":"66c0f92d-0bff-45c1-819e-af8c24f78fb8","resolution":{"observed_at":"2026-07-09T03:05:55.284648Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2604.25427/citation-record","integrity":"/paper/2604.25427/integrity","json":"/paper/2604.25427/citation-record.json","paper":"/paper/2604.25427"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T14:37:16.225324Z","title":"Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851","venue":null,"work_id":"82ba805b-3e59-43c6-b37f-3aa1940eea68","year":2020},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:297626e2e3ac0da2710c60f90a1d64f5f851a57fe6201766a4554bd24baee934","observation_id":"2d826b92-0f1c-4dee-9def-b09013512ee7","resolution":{"observed_at":"2026-05-27T01:33:22.467013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Scaling rectified flow trans- 9 formers for high-resolution image synthesis","venue":null,"work_id":"e64fb671-5ee7-416d-a3f2-853f819dd84c","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:3148aee380e3ed10643585b43eb2f1afa59042d83c99622ab52691de953f57c7","observation_id":"25d1649d-1c37-4c62-bbdb-37d026f65b62","resolution":{"observed_at":"2026-05-27T01:33:22.470170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T02:27:52.409989Z","title":"High- resolution image synthesis with latent diffusion models","venue":null,"work_id":"5427867b-47ba-4d43-a415-2912684a2d41","year":2022},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:7f15a026278b96afaa7fd20e94701156e3939a51f0d579132145c3d5efef226e","observation_id":"d2080a53-3adb-4fa0-a65d-41479dc42d50","resolution":{"observed_at":"2026-05-27T01:33:22.495145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-02T18:24:58.914589Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":"2210.02747","doi":"10.1038/s41467-024-47656-z","metadata_source":"pith","pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flow Matching for Generative Modeling","venue":"cs.LG","work_id":"6edb71c4-5d64-40af-a394-9757ea051a36","year":2022},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:56dcd8ab6492407d5786225addb829e59b3442e0be0758ed84aee652a4c9df8d","observation_id":"16edc2be-5500-44e1-87c2-181bf15a813e","resolution":{"observed_at":"2026-05-11T23:26:21.618412Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-06-01T22:57:59.860918+00:00","source":"crossref_status_cache"},{"observed_at":"2026-06-01T22:57:59.860918+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":"2209.03003","doi":"10.48550/arxiv.2209.03003","metadata_source":"pith","pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","venue":"cs.LG","work_id":"a1989e1b-d66d-4533-be3a-fb9c5fd62290","year":2022},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:d8f0cc3b10caad02118e2bb879343a72727e5a61f3bcce3351687360ae11dc2f","observation_id":"f2317269-d6e7-4f8b-8bba-ae4006dd853f","resolution":{"observed_at":"2026-05-11T23:26:21.716481Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T22:49:29.244251+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T22:49:29.244251+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07703","last_updated":"2025-03-10T17:58:33Z","snapshot_observed_at":"2026-07-06T20:50:09.622181Z","submitted_at":"2025-03-10T17:58:33Z","title":"Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model","version":1},"cited_work":{"arxiv_id":"2503.07703","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.07703","snapshot_observed_at":"2026-07-03T17:18:43.965269Z","title":"Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model","venue":"cs.CV","work_id":"e285b9d3-0bf4-4f98-ba3a-e545425ab960","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2503.07703","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:7eb309faf01ff048c28abe8a9256799da8ab125a7e43c9ba676aaa76ceeafe7a","observation_id":"afde2331-2014-4017-a127-8df4f8f83baf","resolution":{"observed_at":"2026-05-17T08:27:36.473449Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09113","last_updated":"2025-06-28T11:58:23Z","snapshot_observed_at":"2026-08-01T16:09:18.068564Z","submitted_at":"2025-06-10T17:56:11Z","title":"Seedance 1.0: Exploring the Boundaries of Video Generation Models","version":2},"cited_work":{"arxiv_id":"2506.09113","doi":"10.48550/arxiv.2506.09113","metadata_source":"pith","pith_arxiv_id":"2506.09113","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Seedance 1.0: Exploring the Boundaries of Video Generation Models","venue":"cs.CV","work_id":"b2e36b5d-99e4-45b4-9358-64f6d3501983","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2506.09113","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:1bcd2dbcf98199f1ac2b801f3f7ee0b93d176dfcab3b8c134eb6c665eeb04f29","observation_id":"d17cae76-e52f-45db-8da0-68039e15dce5","resolution":{"observed_at":"2026-05-11T23:26:21.527454Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03603","last_updated":"2025-03-11T08:14:25Z","snapshot_observed_at":"2026-08-03T00:44:01.942521Z","submitted_at":"2024-12-03T23:52:37Z","title":"HunyuanVideo: A Systematic Framework For Large Video Generative Models","version":6},"cited_work":{"arxiv_id":"2412.03603","doi":"10.48550/arxiv.2412.03603","metadata_source":"pith","pith_arxiv_id":"2412.03603","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"HunyuanVideo: A Systematic Framework For Large Video Generative Models","venue":"cs.CV","work_id":"881efa7e-7e73-4c66-9cc3-2803e551061c","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2412.03603","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:1a8f9aa59a9dad20602e0715be12ac856ca658b70de2922be6bb2b8f77702b32","observation_id":"ebc07572-2960-44ae-b1da-9a8f8efe7645","resolution":{"observed_at":"2026-05-11T23:26:21.522561Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-10T21:18:49.433177+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-10T21:18:49.433177+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2512.16776","last_updated":"2025-12-18T17:08:12Z","snapshot_observed_at":"2026-07-06T22:39:28.855621Z","submitted_at":"2025-12-18T17:08:12Z","title":"Kling-Omni Technical Report","version":1},"cited_work":{"arxiv_id":"2512.16776","doi":"10.48550/arxiv.2512.16776","metadata_source":"pith","pith_arxiv_id":"2512.16776","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kling-Omni Technical Report","venue":"cs.CV","work_id":"52d502bd-9d8e-4944-9bf2-cfd097cfdb4e","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2512.16776","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:f131b5070bfa76469314832a812b39b0347f923836f864126e6bdc2b4694292a","observation_id":"b5c8659c-fda4-4361-aaad-6383d305dacc","resolution":{"observed_at":"2026-05-15T21:00:58.771713Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-10T21:18:49.73319+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-10T21:18:49.73319+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20314","last_updated":"2025-04-19T02:22:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-26T08:25:43Z","title":"Wan: Open and Advanced Large-Scale Video Generative Models","version":2},"cited_work":{"arxiv_id":"2503.20314","doi":"10.1109/19.492748","metadata_source":"pith","pith_arxiv_id":"2503.20314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Wan: Open and Advanced Large-Scale Video Generative Models","venue":"cs.CV","work_id":"ad3ebc3b-4224-46c9-b61d-bcf135da0a7c","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2503.20314","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:a5ae5d48109ce6cfdeff534e28c2fefb71fea0cf33c45599d73e6fc792670e6c","observation_id":"1ba4edba-2d44-4f82-b818-43016f7b76d1","resolution":{"observed_at":"2026-05-11T23:26:21.762080Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T01:07:50.611559Z","title":"Vbench: Comprehensive benchmark suite for video generative models","venue":null,"work_id":"2383dbc4-0e5d-40ed-a102-5aac37332eae","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:b6ed2196f8a20e7c32b0c7051ebdf8cde668b492bb799660bebeb09efe5698ef","observation_id":"7e08599c-9a14-4278-9733-bb30f4a09a06","resolution":{"observed_at":"2026-05-27T01:33:22.505406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T09:04:46.752472Z","title":"Evalcrafter: Benchmarking and evaluating large video generation models","venue":null,"work_id":"6e757df9-4d94-4c48-b598-5f5b824a06e3","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:21781fed5102b0571540adc421ac88116166c2087c77983f25894a368740876b","observation_id":"a9c76876-90e4-4278-8a63-821a99feffaf","resolution":{"observed_at":"2026-05-27T01:33:22.509942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07818","last_updated":"2025-08-28T17:19:45Z","snapshot_observed_at":"2026-07-31T14:51:03.625964Z","submitted_at":"2025-05-12T17:59:34Z","title":"DanceGRPO: Unleashing GRPO on Visual Generation","version":4},"cited_work":{"arxiv_id":"2505.07818","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.07818","snapshot_observed_at":"2026-07-09T03:05:55.316091Z","title":"DanceGRPO: Unleashing GRPO on Visual Generation","venue":"cs.CV","work_id":"7404dd36-8f9c-478f-b089-ef9f8189c711","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2505.07818","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:b1615a3bccff39777b072919f34e2db8d3fd0118cfae9bf660f7274288d8770b","observation_id":"2f781947-4427-416b-9933-774d85a2c125","resolution":{"observed_at":"2026-05-11T23:26:21.455125Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11346","last_updated":"2025-06-28T11:46:35Z","snapshot_observed_at":"2026-07-06T21:09:50.780345Z","submitted_at":"2025-04-15T16:19:07Z","title":"Seedream 3.0 Technical Report","version":3},"cited_work":{"arxiv_id":"2504.11346","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.11346","snapshot_observed_at":"2026-07-04T07:29:38.429370Z","title":"Seedream 3.0 Technical Report","venue":"cs.CV","work_id":"013e56d0-7f47-4d0e-bbca-e9540fc0e0cc","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2504.11346","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:e9a68dc61f16dcd688c1f377c6001d77436e5dc734f893cfa6e9528895b0f570","observation_id":"40b62679-67c0-4a91-a375-1239ab9ddc42","resolution":{"observed_at":"2026-05-13T07:55:38.865721Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Promptist: Automated prompt optimization for text-to- image synthesis","venue":null,"work_id":"e13004ad-afa8-4fba-9588-2d03089949b6","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:622e40f0cd3b75d9a7c8e9ecc5738ac3297ec579ab9e5c651c0e6a38533a19c0","observation_id":"78231942-3c1a-464c-8526-57897bff24f6","resolution":{"observed_at":"2026-05-27T01:33:22.497633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neuroprompts: An adaptive framework to optimize prompts for text-to-image generation","venue":null,"work_id":"496fc67c-a9eb-4908-95fd-4f6cf8400da5","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:2efe89f85ecaa5d16ffcb2f726b37c525d976b5b6effe777497a174e83667730","observation_id":"443ccc62-0c9a-4b92-ab33-a33e8a77194f","resolution":{"observed_at":"2026-05-27T01:33:22.500021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17804","last_updated":"2024-03-26T15:42:01Z","snapshot_observed_at":"2026-08-05T08:40:05.671575Z","submitted_at":"2024-03-26T15:42:01Z","title":"Improving Text-to-Image Consistency via Automatic Prompt Optimization","version":1},"cited_work":{"arxiv_id":"2403.17804","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.17804","snapshot_observed_at":"2026-07-04T11:39:47.157006Z","title":"Improving text-to- image consistency via automatic prompt optimization","venue":null,"work_id":"19d0bf0e-d0ee-4b74-b0b3-754c11d56dfb","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2403.17804","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:f8d5552c1100030c2c97050cbc6f5cd0f16f8e8360d1fc18699b399130a6c4dd","observation_id":"4711f472-b4a9-4471-877b-5c57219ed890","resolution":{"observed_at":"2026-05-11T23:26:21.385348Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.17540","last_updated":"2025-05-23T06:44:26Z","snapshot_observed_at":"2026-07-06T21:29:06.781083Z","submitted_at":"2025-05-23T06:44:26Z","title":"RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2505.17540","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.17540","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reprompt: Reasoning-augmented reprompting for text-to-image generation via reinforcement learning","venue":null,"work_id":"945c2260-5f9c-4e65-85f7-290443e58495","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2505.17540","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:84df8e7fc0ad2357df6d9b0e442efc114891ab36e5ffa6ae379676fd4fad22a0","observation_id":"cb3317d0-c110-49a5-9976-de4a07b49920","resolution":{"observed_at":"2026-05-11T23:26:21.417103Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2602.01382","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T12:19:49.606655Z","title":"Promptrl: Prompt matters in rl for flow-based image generation","venue":null,"work_id":"4762b3e3-3543-475a-abb8-366e9d2e78bc","year":2026},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:b968605b0a3f77dab3332bb410433d37391fec2ed990f1795d42f64326bfd75b","observation_id":"8fb1e136-9143-48ff-ad7e-3f89f5fc3e97","resolution":{"observed_at":"2026-05-11T23:26:21.143258Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":"1707.06347","doi":"10.1016/j.artint.2010.12.005","metadata_source":"pith","pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proximal Policy Optimization Algorithms","venue":"cs.LG","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","year":2017},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:0da44d2bb6e379b42ad86b783703c0d430872cf49e9b186b28200557723aaf38","observation_id":"6ab2a961-003b-4ec1-94b7-39a668bdea4c","resolution":{"observed_at":"2026-05-11T23:26:21.204868Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T08:54:50.108470Z","title":"Direct preference optimization: Your language model is secretly a reward model","venue":null,"work_id":"5d2e1082-e73f-4c1b-a18c-bf0ba1772c23","year":2023},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:ebbc3bc76eaa28c43cb8badd71da6075e0d763fec6af6fb1ad4c85d37facd698","observation_id":"30730f75-ac23-46a3-875b-9840d623df96","resolution":{"observed_at":"2026-05-27T01:33:22.502785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.05470","last_updated":"2025-10-27T09:57:02Z","snapshot_observed_at":"2026-08-06T11:11:00.378627Z","submitted_at":"2025-05-08T17:58:45Z","title":"Flow-GRPO: Training Flow Matching Models via Online RL","version":5},"cited_work":{"arxiv_id":"2505.05470","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.05470","snapshot_observed_at":"2026-07-09T02:25:55.898592Z","title":"Flow-GRPO: Training Flow Matching Models via Online RL","venue":"cs.CV","work_id":"bf1e8e81-ff31-401a-a5dc-d9c49df168ab","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2505.05470","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:a8c4cd11b0d277a821ca2a3d9973fcd36b9b7e2eeb717bb9d3729b58a5a74f50","observation_id":"12719330-0b23-4ef2-9fe1-f172b83477e8","resolution":{"observed_at":"2026-05-11T23:26:21.055591Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.21802","last_updated":"2026-03-20T17:20:12Z","snapshot_observed_at":"2026-08-01T16:12:11.335962Z","submitted_at":"2025-07-29T13:40:09Z","title":"MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE","version":6},"cited_work":{"arxiv_id":"2507.21802","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.21802","snapshot_observed_at":"2026-07-09T03:05:55.323624Z","title":"MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE","venue":"cs.AI","work_id":"8b0ab84a-b7ea-46ea-a6ba-bf490a84d251","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2507.21802","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:276440403ede16bb141517e04834624dc2a06f574af89804b967572457528288","observation_id":"736447ae-cdb4-46ed-abd0-37fdbfa90f61","resolution":{"observed_at":"2026-05-13T13:27:50.191417Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.05952","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T03:05:55.300508Z","title":"Coefficients-preserving sampling for reinforcement learning with flow matching.arXiv preprint arXiv:2509.05952","venue":null,"work_id":"3bc58f2f-e4be-4ee5-804f-29ffd6dfdb30","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:618855449adcf1702aca8176a7316c95d74a8221c2d52af2600194f06dabd011","observation_id":"9c50c0ce-49f9-417e-96e3-42571116a446","resolution":{"observed_at":"2026-05-11T23:26:21.424137Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.04324","last_updated":"2025-10-15T06:35:29Z","snapshot_observed_at":"2026-07-06T22:08:40.965707Z","submitted_at":"2025-08-06T11:10:39Z","title":"TempFlow-GRPO: When Timing Matters for GRPO in Flow Models","version":4},"cited_work":{"arxiv_id":"2508.04324","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.04324","snapshot_observed_at":"2026-07-09T03:05:55.312048Z","title":"TempFlow-GRPO: When Timing Matters for GRPO in Flow Models","venue":"cs.CV","work_id":"fecc731c-f8a2-4f00-ab1b-51b87a133726","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2508.04324","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:5e1f02b3544f59b83a4b4de2a64f5786f86a6f24dea25b27a576cc39c59668f7","observation_id":"7478f0b2-183a-497e-b9ef-7244f735a9ff","resolution":{"observed_at":"2026-05-21T21:52:31.656662Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"G2rpo: Granular grpo for precise reward in flow models","venue":null,"work_id":"13d213e7-d0f2-4611-80bf-e0c11ec77a76","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:f5e968b3b2d6985277c38bb5bf402dcec44ec49a8b5a44fe74af3e708fa45b3a","observation_id":"4c2cfaa9-e281-48fa-bac4-cee970174b05","resolution":{"observed_at":"2026-05-27T01:33:22.507604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.00423","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T23:15:07.911874Z","title":"book above laptop","venue":null,"work_id":"4758d805-e3c2-43e3-aea4-509dfc8cbbe9","year":2026},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:112dc3a1fc596689ffd9f2bf7959982315fc458b840f9006bb263475c7f7e630","observation_id":"0593b9f3-4dfa-44a1-be66-82c2e6372a0a","resolution":{"observed_at":"2026-05-11T23:26:21.533894Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.06040","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T02:25:55.893505Z","title":"Branchgrpo: Stable and efficient grpo with structured branching in diffusion models","venue":null,"work_id":"4b5ca02b-b12f-4ca1-88b1-99007b8f9528","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:44317f4be8a8621eb9b8e0f7f039d7643697b781acb223c99b4d39610279c3c9","observation_id":"84589ea3-b4b7-40e0-8844-ebe7bee23478","resolution":{"observed_at":"2026-05-11T23:26:20.900099Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.16117","last_updated":"2026-02-16T17:14:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-19T16:09:33Z","title":"DiffusionNFT: Online Diffusion Reinforcement with Forward Process","version":2},"cited_work":{"arxiv_id":"2509.16117","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.16117","snapshot_observed_at":"2026-07-04T19:50:11.382174Z","title":"DiffusionNFT: Online Diffusion Reinforcement with Forward Process","venue":"cs.LG","work_id":"0ed3cf57-36ba-4962-847e-7a8f5f99901d","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2509.16117","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:5a1d14cdc6d86e1eb3185deecf822a854776e0272940a5d59d754af22ef0ecad","observation_id":"9b4f6aea-76e1-4d66-8d94-02fb5b0ee72f","resolution":{"observed_at":"2026-05-13T16:54:31.055857Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.25050","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T03:05:55.227536Z","title":"Advantage weighted matching: Aligning rl with pretraining in diffusion models","venue":null,"work_id":"b71cd776-fb9d-4e2f-b936-7105099de5b7","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:abe24487852658a5f083901184e8860ee0c65f527f6277e2b82b9c157a65cadf","observation_id":"1c465f67-fcb4-4630-83e5-484b1a80b928","resolution":{"observed_at":"2026-05-11T23:26:20.736561Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.17051","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T14:35:47.160182Z","title":"Astrolabe: Steering forward-process reinforcement learning for distilled autoregressive video models","venue":null,"work_id":"61b2b014-6aa6-4402-b4e2-fe5538c70b22","year":2026},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:1b20f5d70e537a05dcb73a7d6a2c86eda775fc597e198b0d15b72a7d57c1dff2","observation_id":"77c1f85c-e902-42a2-8f8c-401bb72db50b","resolution":{"observed_at":"2026-05-11T23:26:20.486856Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Professor forcing: A new algorithm for training recurrent networks.Advances in neural information processing systems, 29","venue":null,"work_id":"3f52e12e-62fe-4469-9991-07d3b1e7a8b2","year":2016},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:960b5bf359c812f7c4966baced2b3d8ac3c2838dad51ecc20aa65c7f6b7b5c6e","observation_id":"1e7299ca-fbee-4772-a795-b7b9486ea597","resolution":{"observed_at":"2026-05-27T01:33:22.518878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T03:24:28.911186Z","title":"Diffusion forcing: Next-token prediction meets full-sequence diffusion.Advances in Neural Information Processing Systems, 37:24081–24125","venue":null,"work_id":"5468fffd-cfd2-4046-8bd3-6a35d9948eb1","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:b5dea9e753041b43c172556019e736d1b03a4037529ec1732828e2c10f1f5604","observation_id":"d25a8b79-e7ff-4fcb-af11-b28201a94970","resolution":{"observed_at":"2026-05-27T01:33:22.485388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"From slow bidirectional to fast autoregressive video diffusion models","venue":null,"work_id":"8514bfb5-0790-4e77-bf94-d937cdab606b","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:82a6d92c7c29b5aabde6983ca22722dfd7c18dda56034b4902d07c5b465457ad","observation_id":"1b59a2e1-f4c5-4a1c-a25d-82260e2aefc6","resolution":{"observed_at":"2026-05-27T01:33:22.475582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"One-step diffusion with distribution matching distillation","venue":null,"work_id":"29c8104d-de08-4e9f-9fa0-0975f0d8a952","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:8745a3ad6bd14e84377a3c43ad52f4140048d62d62c94fb852e9e5ada82a08dc","observation_id":"a51d0f2d-5313-4a73-8848-46a8e0bf3aa3","resolution":{"observed_at":"2026-05-27T01:33:22.478838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08009","last_updated":"2025-11-10T04:36:27Z","snapshot_observed_at":"2026-08-02T00:07:53.851104Z","submitted_at":"2025-06-09T17:59:55Z","title":"Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion","version":2},"cited_work":{"arxiv_id":"2506.08009","doi":"10.48550/arxiv.2506.08009","metadata_source":"pith","pith_arxiv_id":"2506.08009","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion","venue":"cs.CV","work_id":"53e58ef9-7932-4b83-b757-34ac14db3e0f","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2506.08009","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:619c88cdba01124f45f4f9471c770236beaa59ebe62ce50eecc8c4a9a0864a33","observation_id":"89be9538-ebd0-4085-9823-8a620f7ee2e8","resolution":{"observed_at":"2026-05-11T23:26:19.855952Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.22622","last_updated":"2025-10-13T22:41:26Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T17:48:24Z","title":"LongLive: Real-time Interactive Long Video Generation","version":2},"cited_work":{"arxiv_id":"2509.22622","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.22622","snapshot_observed_at":"2026-07-09T07:56:04.670863Z","title":"LongLive: Real-time Interactive Long Video Generation","venue":"cs.CV","work_id":"29ec6550-6ac1-4cc6-8aae-b4206f1d5b38","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2509.22622","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:5ff9e659aeb26584b2145ad1fa7c4fb02fee9ce79d74b01fa1c7000786a4559c","observation_id":"117e9d13-7317-4ebb-a503-a49ba126a6e2","resolution":{"observed_at":"2026-05-15T03:52:59.837830Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.11647","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T16:49:57.649224Z","title":"Omniforcing: Unleashing real-time joint audio-visual generation","venue":null,"work_id":"10078c6b-bb46-407a-9aa3-312d149e6d74","year":2026},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:a2ab29214aa7d0bf17e649c547ba522503f9bea9b0b0e3956ef96dc37e571b66","observation_id":"5d9633ed-f760-4a70-8732-6cef6066e722","resolution":{"observed_at":"2026-05-11T23:26:20.346398Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.02214","last_updated":"2026-06-01T14:32:37Z","snapshot_observed_at":"2026-08-03T05:30:22.346810Z","submitted_at":"2026-02-02T15:19:22Z","title":"Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation","version":5},"cited_work":{"arxiv_id":"2602.02214","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.02214","snapshot_observed_at":"2026-07-10T13:47:05.847304Z","title":"Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation","venue":"cs.CV","work_id":"04f67f5b-e79a-4ad9-8e90-763e5e54bd3d","year":2026},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2602.02214","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:cff3bcacedcca54c092085afd74d2b354dc70d4837c4a1408fbaa8f07576d2be","observation_id":"874d90c0-11c5-484e-a556-e8195846eee4","resolution":{"observed_at":"2026-05-21T17:32:01.900965Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T13:13:50.609285Z","title":"Hpsv3: Towards wide-spectrum hu- man preference score","venue":null,"work_id":"7f1f4d17-3402-496d-a645-5ef5bc5cd4fa","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:19fd2b69394a4d3e8107813162c65ae45d5c95c038b547cd1de98315692bc74c","observation_id":"c1b62cc1-6d65-4e8d-9056-bf7dcef10555","resolution":{"observed_at":"2026-05-27T01:33:22.482493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T17:34:01.618368Z","title":"Imagereward: Learning and evaluating human preferences for text-to-image generation","venue":null,"work_id":"5c5c3eee-061e-4af7-b5f2-2e03489973ae","year":2023},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:8037f8f0f91a340a55d1a2a82e7378ac72fa408dc545ec5ab8efe0c2514520c3","observation_id":"b2760aad-d177-4ece-937c-47af0e5523e7","resolution":{"observed_at":"2026-05-27T01:33:22.472912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T03:05:55.757899Z","title":"Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in neural information processing systems, 36:36652–36663","venue":null,"work_id":"00e2bd13-9259-4c19-8ac6-cfe745a0da17","year":2023},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:79d9939ddd620a9243174ac2c75073a4faa940459a1a11ca0b039653fd03939c","observation_id":"44cd1b39-3e2d-44c7-bb0d-05399189b737","resolution":{"observed_at":"2026-05-27T01:33:22.492304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13918","last_updated":"2025-10-27T08:22:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-23T18:55:41Z","title":"Improving Video Generation with Human Feedback","version":2},"cited_work":{"arxiv_id":"2501.13918","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.13918","snapshot_observed_at":"2026-07-04T13:19:51.115512Z","title":"Improving Video Generation with Human Feedback","venue":"cs.CV","work_id":"cfe4c01d-1cf7-4a00-ba86-06d583ca2cff","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2501.13918","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:0f71a82afdb30943d33ddfdb11ca7a8f7bd6db7055ed6fb9fdab2f34e6966c9e","observation_id":"c990a72d-980c-45f9-9903-09b0356f6df1","resolution":{"observed_at":"2026-05-13T15:30:03.116566Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09341","last_updated":"2023-09-25T08:19:23Z","snapshot_observed_at":"2026-07-06T15:43:07.989730Z","submitted_at":"2023-06-15T17:59:31Z","title":"Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis","version":2},"cited_work":{"arxiv_id":"2306.09341","doi":"10.48550/arxiv.2306.09341","metadata_source":"pith","pith_arxiv_id":"2306.09341","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis","venue":"cs.CV","work_id":"40702548-f094-4c67-a5db-a62f426f852e","year":2023},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2306.09341","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:aee7f3ace81e55b3f86e1f6cc5939e0435ea5d5eb436a802b94e3f50a8b420d2","observation_id":"543285b1-ad8c-467f-b4f4-292c89867595","resolution":{"observed_at":"2026-05-11T23:26:20.296357Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T17:34:01.605157Z","title":"Human preference score: Better aligning text-to-image models with human preference","venue":null,"work_id":"6284bb58-fb78-42dc-93a6-02d2be538a76","year":2096},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:30ab6d16369d3e5a42f4ea5038af2ade10b4ccdd72744063353c11683a8528b8","observation_id":"d82339d2-4aee-41b8-bb2b-c072be8ee84f","resolution":{"observed_at":"2026-05-27T01:33:22.488556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T03:54:30.825321Z","title":"Videoscore: Building automatic metrics to simulate fine-grained human feedback for video generation","venue":null,"work_id":"a95a4387-dcbf-44b4-b3ed-2111b55ee80f","year":2024},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:c4e9d4878269725230c8405becc26d7103c152574942c0fde7f24719b9164ece","observation_id":"bc2904ad-65a6-40ac-8948-1f84010642c7","resolution":{"observed_at":"2026-05-27T01:33:22.515725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T03:54:30.812941Z","title":"Visionreward: Fine-grained multi-dimensional human preference learning for image and video generation","venue":null,"work_id":"00f4fdb0-f1bf-459c-877a-6ac01a6ea3ff","year":2026},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:3250377df58fd492e04c57bf784783eeca0c054ee156c9973b951edad22be80d","observation_id":"6bcafb58-1a48-4c40-9a65-900f8ee809cd","resolution":{"observed_at":"2026-05-27T01:33:22.512631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.05236","last_updated":"2026-02-25T13:17:30Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-07T08:36:05Z","title":"Unified Reward Model for Multimodal Understanding and Generation","version":2},"cited_work":{"arxiv_id":"2503.05236","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.05236","snapshot_observed_at":"2026-07-04T16:59:58.010213Z","title":"Unified Reward Model for Multimodal Understanding and Generation","venue":"cs.CV","work_id":"bf9fcf9a-1781-4008-960e-2bec1a717e4e","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2503.05236","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:d47571b4a1c97b93c329fcbd9e43f72a0603ea4f23526503fa6bb91d97b58845","observation_id":"e222173e-6f96-4381-afb8-87f70ca41023","resolution":{"observed_at":"2026-05-14T00:44:31.060111Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.08826","last_updated":"2025-09-10T17:59:31Z","snapshot_observed_at":"2026-08-04T20:08:51.890283Z","submitted_at":"2025-09-10T17:59:31Z","title":"RewardDance: Reward Scaling in Visual Generation","version":1},"cited_work":{"arxiv_id":"2509.08826","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.08826","snapshot_observed_at":"2026-07-04T10:39:45.924030Z","title":"Rewarddance: Reward scaling in visual generation","venue":null,"work_id":"eb1c8722-7fb8-4284-9c9d-37b91aa2cebf","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2509.08826","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:35458f079619c8415f6d8d2b01f560b6171d9bca8571d89a23a2bf48dfd77004","observation_id":"e7328192-0136-46d3-8da4-955bba962ac0","resolution":{"observed_at":"2026-05-11T23:26:20.184788Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.21631","last_updated":"2025-11-27T12:16:54Z","snapshot_observed_at":"2026-07-06T22:37:03.716474Z","submitted_at":"2025-11-26T17:59:08Z","title":"Qwen3-VL Technical Report","version":2},"cited_work":{"arxiv_id":"2511.21631","doi":"10.1016/j.neunet.2025.107777","metadata_source":"pith","pith_arxiv_id":"2511.21631","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen3-VL Technical Report","venue":"cs.CV","work_id":"1fe243aa-e3c0-4da6-b391-4cbcfc88d5c0","year":2025},"citing_paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-07T16:58:33.014401Z"},"links":{"cited_paper":"/paper/2511.21631","citing_paper":"/paper/2604.25427"},"observation_digest":"sha256:f11a42c98a2c31200a0efee9d5639745eec78ff9695698288f1148857cd873d0","observation_id":"141ec04f-2c7c-401d-b739-948027c14971","resolution":{"observed_at":"2026-05-11T23:26:20.176155Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.25427","last_updated":"2026-04-28T09:34:51Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T23:11:16.247497Z","submitted_at":"2026-04-28T09:34:51Z","title":"A Systematic Post-Train Framework for Video Generation"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":30,"verified_fuzzy":19},"total_outbound_references":50},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 6 inbound Pith citation observations for arXiv:2604.25427."}