{"as_of":"2026-08-18T14:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:50492b94f2f74163c3b1a9b589cb234893f23a86de886f59d260f209513ef321","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:38:39.468676Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T19:09:45.710658Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T05:43:58.843048Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"cited_work":{"arxiv_id":"2504.12259","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.12259","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a7c51b86-0a97-47f8-9f51-8762c205da69","year":2025},"citing_paper":{"arxiv_id":"2604.15911","last_updated":"2026-04-17T10:11:39Z","snapshot_observed_at":"2026-08-11T06:56:48.614928Z","submitted_at":"2026-04-17T10:11:39Z","title":"Efficient Video Diffusion Models: Advancements and Challenges","version":1},"reference_index":171,"source":"pdf_text","source_observed_at":"2026-05-10T08:28:29.706249Z"},"links":{"cited_paper":"/paper/2504.12259","citing_paper":"/paper/2604.15911"},"observation_digest":"sha256:268ff83a5d32baf40d1a3856ce9936e813e5cdc3ec879e1bb32722480ae03f77","observation_id":"a112b229-bdc1-4221-bdf2-9c76bfd30437","resolution":{"observed_at":"2026-05-10T09:03:25.957197Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"cited_work":{"arxiv_id":"2504.12259","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.12259","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a7c51b86-0a97-47f8-9f51-8762c205da69","year":2025},"citing_paper":{"arxiv_id":"2605.21042","last_updated":"2026-05-20T11:24:02Z","snapshot_observed_at":"2026-08-13T20:17:26.551686Z","submitted_at":"2026-05-20T11:24:02Z","title":"Dynamic Video Generation: Shaping Video Generation Across Time and Space","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-21T05:42:41.925474Z"},"links":{"cited_paper":"/paper/2504.12259","citing_paper":"/paper/2605.21042"},"observation_digest":"sha256:853dbc88a0f1ba74d5878c5a00622b46c19b91d327a17268cacd0b6107035f38","observation_id":"e0fa2656-9670-4c6a-9efe-3be96fa5e0fb","resolution":{"observed_at":"2026-05-21T05:43:58.844504Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.12259","snapshot_observed_at":"2026-07-31T19:09:45.710658Z","title":"Vgdfr: Diffusion-based video generation with dynamic latent frame rate","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28058","last_updated":"2026-07-30T11:36:48Z","snapshot_observed_at":"2026-08-15T16:46:42.684350Z","submitted_at":"2026-07-30T11:36:48Z","title":"Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-31T19:09:45.710658Z"},"links":{"cited_paper":"/paper/2504.12259","citing_paper":"/paper/2607.28058"},"observation_digest":"sha256:6ac19fb9a719247a44e74ef6bf5bc2cb01861f7214554f9b9ebfe9143d88fc8d","observation_id":"66ba069b-b811-46f8-b150-417ec937ff7f","resolution":{"observed_at":"2026-07-31T19:09:45.710658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.12259/citation-record","integrity":"/paper/2504.12259/integrity","json":"/paper/2504.12259/citation-record.json","paper":"/paper/2504.12259"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.15127","last_updated":"2023-11-25T22:28:38Z","snapshot_observed_at":"2026-08-17T12:45:30.627161Z","submitted_at":"2023-11-25T22:28:38Z","title":"Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15127","snapshot_observed_at":"2026-08-16T12:38:38.748487Z","title":"Stable video diffusion: Scaling latent video diffusion models to large datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.748487Z"},"links":{"cited_paper":"/paper/2311.15127","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:9132e8425dff1362c4bbb1fde7fef3a9b46ee7f39aa5b7eb5b2096a048f54d91","observation_id":"67cd8e16-c5c8-4a42-9e24-ac7a5f9bbb1c","resolution":{"observed_at":"2026-08-16T12:38:38.748487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:38.791819Z","title":"Token merging for fast sta- ble diffusion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.791819Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:05f54ffb7c32831a6d31a534f3c53b1f6bdfdb9f346e2c744d2a422b7074d71e","observation_id":"7a583eab-3f3b-47c9-a0b5-9e4ebec0e2d9","resolution":{"observed_at":"2026-08-16T12:38:38.791819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.09461","last_updated":"2023-03-01T19:45:11Z","snapshot_observed_at":"2026-08-13T04:00:22.647615Z","submitted_at":"2022-10-17T22:23:40Z","title":"Token Merging: Your ViT But Faster","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.09461","snapshot_observed_at":"2026-08-16T12:38:38.830872Z","title":"To- ken merging: Your vit but faster","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.830872Z"},"links":{"cited_paper":"/paper/2210.09461","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:88b793376f2b4bac0f83bcbd27750d45f574c25cb6755b26361b5133b14867d3","observation_id":"c97de3da-80a2-48ff-b874-60402cff15b2","resolution":{"observed_at":"2026-08-16T12:38:38.830872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.463362Z","title":"Video generation models as world simulators","venue":null,"work_id":"b1b0193e-2349-4259-b3b9-973a3c308235","year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.835678Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:b18d39f00d895facb84eb1d8eadb9707ec20a1acc758108aab99e4776ef84e13","observation_id":"9f5d3f8c-a654-436d-8470-613082278906","resolution":{"observed_at":"2026-08-16T12:38:40.467128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00426","last_updated":"2023-12-29T16:42:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-30T16:18:00Z","title":"PixArt-$\\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00426","snapshot_observed_at":"2026-08-16T12:38:38.840148Z","title":"Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.840148Z"},"links":{"cited_paper":"/paper/2310.00426","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:832d02c1d17067d3fa4841bb969035d842fe09e7d7572508c236b554e7f9e968","observation_id":"dbf2cee0-16fb-4b5c-9229-17942f5b814a","resolution":{"observed_at":"2026-08-16T12:38:38.840148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10733","last_updated":"2025-05-18T21:17:22Z","snapshot_observed_at":"2026-08-18T11:02:33.658704Z","submitted_at":"2024-10-14T17:15:07Z","title":"Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10733","snapshot_observed_at":"2026-08-16T12:38:38.844726Z","title":"Deep compression autoencoder for efficient high-resolution diffu- sion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.844726Z"},"links":{"cited_paper":"/paper/2410.10733","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:953896648c54201354d94f2cc6daa568b9acca319cbc901887b46d8790b48ece","observation_id":"75316492-cc38-4818-8092-cb50947e5397","resolution":{"observed_at":"2026-08-16T12:38:38.844726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.449963Z","title":"Structure and content-guided video synthesis with diffusion models","venue":null,"work_id":"4b8e6d80-f901-493f-a72f-c83d0af6640e","year":2023},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.849094Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:a42da2e12ea8fec8bb2dd73c533452e490fcfb6a980821ac70c1c24090132a16","observation_id":"a7c504ad-e4b8-49dc-9548-6bf0bbdf7e18","resolution":{"observed_at":"2026-08-16T12:38:40.455450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10709","last_updated":"2024-08-02T18:55:25Z","snapshot_observed_at":"2026-08-16T14:42:19.011451Z","submitted_at":"2023-11-17T18:59:04Z","title":"Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.10709","snapshot_observed_at":"2026-08-16T12:38:38.852882Z","title":"Emu video: Factorizing text-to-video generation by explicit image con- ditioning (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.852882Z"},"links":{"cited_paper":"/paper/2311.10709","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:34f87e2919177cd7fedeb55b50d3731a70b1ab4a3d9b359a757a2f815f665890","observation_id":"88cf8276-278b-41c1-96f3-d09461cb4c9c","resolution":{"observed_at":"2026-08-16T12:38:38.852882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.373495Z","title":"Generative adversarial nets","venue":null,"work_id":"8fc9c3cf-7add-41c2-a4d1-a48d9d0023c8","year":2014},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.857297Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:2674e2796b4900456aaf281fea2b31e66bd11f6dbde12eff7c77771fc5c74aea","observation_id":"9e5a09bc-9856-4832-85a0-660be567e736","resolution":{"observed_at":"2026-08-16T12:38:40.441136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:38.861486Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.861486Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:5d4abb8735a50e2c6ea57463b586f8cab4e87ae1c77ad5ec8eb788ccb07a2ccb","observation_id":"27af50fd-a0fd-4667-8052-5b3e31b2349d","resolution":{"observed_at":"2026-08-16T12:38:38.861486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.245584Z","title":"Real-time intermediate flow estimation for video frame interpolation","venue":null,"work_id":"ec93de48-673a-4b41-9dc0-cf067c24080d","year":2022},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.865295Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:28806057f08ecf7f54ec69ad0cb05605e1a28a4eab5cca2e95a623b04a4a37eb","observation_id":"7d47c23c-83b9-424f-bf20-f2e6cb12243c","resolution":{"observed_at":"2026-08-16T12:38:40.253317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:38.869788Z","title":"Vbench: Comprehensive bench- mark suite for video generative models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.869788Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:87f9e5fac1c435b78e0e5c9e00b5e7c8f0825846efbb5911474de2d49ae35134","observation_id":"6bf0d354-3709-4c34-b383-3d23d07a68dd","resolution":{"observed_at":"2026-08-16T12:38:38.869788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.226079Z","title":"Scale-adaptive feature aggregation for efficient space-time video super-resolution","venue":null,"work_id":"6ac2917b-9585-40ea-92ed-312dc231350a","year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.880698Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:ad95b6d2bfbaebd585c9f2d2ef55122c89ce65d58cf9dba562a942a6c7fb5ebf","observation_id":"6ccd0851-215a-461b-8741-12fb7dc9efb5","resolution":{"observed_at":"2026-08-16T12:38:40.230346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02397","last_updated":"2024-11-07T17:06:32Z","snapshot_observed_at":"2026-08-18T07:57:54.394259Z","submitted_at":"2024-11-04T18:59:44Z","title":"Adaptive Caching for Faster Video Generation with Diffusion Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02397","snapshot_observed_at":"2026-08-16T12:38:38.929792Z","title":"Adaptive caching for faster video generation with diffu- sion transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.929792Z"},"links":{"cited_paper":"/paper/2411.02397","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:ebdf293a16d4c8e745a0f1e42981aae2099e9be3be72d9369ba0086d4dabb0c0","observation_id":"84e3dfca-b5d0-48fd-a620-1d0c1b0c5e08","resolution":{"observed_at":"2026-08-16T12:38:38.929792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:38.974248Z","title":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.974248Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:2f75a2f06b1766f59defde38b2ed43432ea745ea1b5f133a1334878798b005f6","observation_id":"785a618b-2086-4314-bc37-98b826b1d4f2","resolution":{"observed_at":"2026-08-16T12:38:38.974248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:38.997313Z","title":"Auto-encoding vari- ational bayes, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:38.997313Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:087bee689332e5bd94a0df0ad73f419d4dc89161765cc61ee4e445cae353d5b4","observation_id":"3c8ba51d-0e8b-4a9b-9e50-85b66e57f6ce","resolution":{"observed_at":"2026-08-16T12:38:38.997313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03603","last_updated":"2025-03-11T08:14:25Z","snapshot_observed_at":"2026-08-17T07:44:10.213698Z","submitted_at":"2024-12-03T23:52:37Z","title":"HunyuanVideo: A Systematic Framework For Large Video Generative Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03603","snapshot_observed_at":"2026-08-16T12:38:39.001641Z","title":"Hunyuanvideo: A systematic framework for large video generative models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.001641Z"},"links":{"cited_paper":"/paper/2412.03603","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:356447b2cad8ba1d0dcb741c1c0f4636e780dc46bb89dbf7484ca3cd19f5ac9b","observation_id":"f37110b4-9d05-4312-8d6a-61ce26637afe","resolution":{"observed_at":"2026-08-16T12:38:39.001641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.006294Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.006294Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:f24cc28d311ba4fcab0782e4612fea93b9e5556210424404a8cda0a86167177f","observation_id":"79590cb1-e8a2-4854-b015-3cb4353b6a04","resolution":{"observed_at":"2026-08-16T12:38:39.006294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.187759Z","title":"Vidtome: Video token merging for zero-shot video editing","venue":null,"work_id":"ad7f8c4e-a66e-4d2c-95eb-5dce6cc1e707","year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.010561Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:f1697221d444c3f4c0fef868311d937cc824ddce8bb33fee4b9ef9b6894ca3ee","observation_id":"2716eb1c-0855-47ee-886a-ff772862207c","resolution":{"observed_at":"2026-08-16T12:38:40.192344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00131","last_updated":"2024-11-28T14:07:45Z","snapshot_observed_at":"2026-08-13T21:19:07.777045Z","submitted_at":"2024-11-28T14:07:45Z","title":"Open-Sora Plan: Open-Source Large Video Generation Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00131","snapshot_observed_at":"2026-08-16T12:38:39.014612Z","title":"Open-sora plan: Open-source large video generation model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.014612Z"},"links":{"cited_paper":"/paper/2412.00131","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:acb8a3204df02eb4715943816e40c2ffd821df732e6008d01f7bffc9f8e3cb2e","observation_id":"68635b21-b4e8-4a4e-9376-1d6ae49154c7","resolution":{"observed_at":"2026-08-16T12:38:39.014612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17177","last_updated":"2024-04-17T18:41:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-27T03:30:58Z","title":"Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17177","snapshot_observed_at":"2026-08-16T12:38:39.018662Z","title":"Sora: A review on background, technology, limitations, and opportunities of large vision models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.018662Z"},"links":{"cited_paper":"/paper/2402.17177","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:d7f66d56ee5a13e515b68ac41211f291294150ee5e7198b4b917244548b8912d","observation_id":"10665c07-2900-495a-881f-6ff016c30050","resolution":{"observed_at":"2026-08-16T12:38:39.018662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.082968Z","title":"A study of subjective video quality at various frame rates","venue":null,"work_id":"4bd66378-d50a-477a-b12a-aa269a71da8a","year":2015},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.023565Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:f1e0d3bf426ab0fcedcdf4a5a46d87c00c0d43f97ae1fd2a701ee36c4caa0847","observation_id":"9dc2b6ba-58e1-4460-8375-daadeb4d78f5","resolution":{"observed_at":"2026-08-16T12:38:40.142764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.028007Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.028007Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:ef041efaa787dd849e405ef3d74de71506061cf6b7cb5092b8950d6c6df92c00","observation_id":"82445d38-1761-4133-bde1-621ce609ecfa","resolution":{"observed_at":"2026-08-16T12:38:39.028007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-08-14T22:54:08.184266Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-16T12:38:39.032068Z","title":"Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.032068Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:ae3f914a083ad26cab07af9323c81af8d96e3272c891da619742200e779b7d16","observation_id":"e2a5609d-5ee1-441b-bb39-181a98999b2f","resolution":{"observed_at":"2026-08-16T12:38:39.032068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:40.060514Z","title":"Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petro- vic, and Yuming Du","venue":null,"work_id":"79bd9fbf-4689-446e-bae2-9a49cc2a6d19","year":2025},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.035562Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:9103cd631d0bf618fbf62f8ef07676373f855a3e0c249628b9856def051e9ba5","observation_id":"0dafea98-db47-46ad-84b8-e00dd28bc553","resolution":{"observed_at":"2026-08-16T12:38:40.065379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.038604Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.038604Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:4e50e9948df9ef1fc5d382de54c6c245b17e9d74761f9d9a097566db4e18fe0a","observation_id":"a09e068e-8a49-4998-bc65-122062ffb520","resolution":{"observed_at":"2026-08-16T12:38:39.038604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.042275Z","title":"Photorealistic text-to-image diffusion models with deep language understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.042275Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:f0ba4585fe9b8070f94a5ab67276599ec5d98b42bb2a2801d5efc5aa9c4210a9","observation_id":"06616b2f-840c-43f8-8011-c7cb69a0ccc7","resolution":{"observed_at":"2026-08-16T12:38:39.042275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15477","last_updated":"2025-05-20T23:20:36Z","snapshot_observed_at":"2026-08-16T13:16:07.432594Z","submitted_at":"2024-09-23T18:59:37Z","title":"MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15477","snapshot_observed_at":"2026-08-16T12:38:39.045973Z","title":"Mediconfusion: Can you trust your ai radiologist? probing the reliability of multimodal medical foundation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.045973Z"},"links":{"cited_paper":"/paper/2409.15477","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:11b3d832c00f19e42a5fc72c2f75ec34dfcc55e707661ffe0d44cbe2e95d773e","observation_id":"5f7cc21c-9ad1-4fff-b77a-3d4845c65bee","resolution":{"observed_at":"2026-08-16T12:38:39.045973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.087866Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.087866Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:0a6de6fbcbca1203d14abd474863aada004c8839ee574c4358bbfeaa105a2e8e","observation_id":"c03e854c-f3d5-4b08-9243-5dc3c098db7d","resolution":{"observed_at":"2026-08-16T12:38:39.087866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.911026Z","title":"Rate control for low-bit-rate video via variable-encoding frame rates","venue":null,"work_id":"873823c3-5ca9-488b-a3e9-1d56076e6058","year":2001},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.163124Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:b83f08340a3db517ffa9edc92aa49b0bc8a92526bf5973dd3691e046e05ffa6c","observation_id":"4d04a0cc-bd62-450f-b9fd-076a5a7a33ba","resolution":{"observed_at":"2026-08-16T12:38:39.978080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11706","last_updated":"2025-05-24T17:39:32Z","snapshot_observed_at":"2026-08-15T13:09:10.090692Z","submitted_at":"2024-12-16T12:28:22Z","title":"AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.11706","snapshot_observed_at":"2026-08-16T12:38:39.206024Z","title":"Asymrnr: Video diffusion transformers ac- celeration with asymmetric reduction and restoration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.206024Z"},"links":{"cited_paper":"/paper/2412.11706","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:32e216f19cc522aa197ecfc0ca75f471552be588f20c6fbdd44e050a5791231f","observation_id":"69b9cc37-7127-435d-b456-db656f0f3a31","resolution":{"observed_at":"2026-08-16T12:38:39.206024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.211071Z","title":"Mocogan: Decomposing motion and content for video generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.211071Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:23a098253689e2cfd8f4843db1e05c7ecffb25ced00cad9858b0790e44cfde9f","observation_id":"f992b9a2-9381-47fc-83e2-173ff9bd998b","resolution":{"observed_at":"2026-08-16T12:38:39.211071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.215567Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.215567Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:7fe3fc7523feef7a0cf50eae1c67205f29056bc1ef44ecd3dcefd399a841cb82","observation_id":"966842cc-25c9-4cec-8c6e-a6a5dd271bc0","resolution":{"observed_at":"2026-08-16T12:38:39.215567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.874326Z","title":"Omnitokenizer: A joint image- video tokenizer for visual generation","venue":null,"work_id":"06a33ea6-8fd7-474a-ab6f-58c33ec29343","year":2025},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.219635Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:b379b8e4be99f5232ef29a9cc2f8b3a909fab32ae0b17a12c3c183a598bf46de","observation_id":"6bad5f5d-b81c-4bbc-9428-816508494dc1","resolution":{"observed_at":"2026-08-16T12:38:39.878993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.223858Z","title":"Image quality assessment: from error visibility to structural similarity","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.223858Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:19532d7b7bde9cec9fe1600d1b7d60e0ba6fa88e68c7038ed30972187ba05c00","observation_id":"3a00d3d3-bfe1-4b9d-8f4e-f45448a2d84f","resolution":{"observed_at":"2026-08-16T12:38:39.223858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14806","last_updated":"2021-04-30T07:40:35Z","snapshot_observed_at":"2026-08-16T18:27:58.328538Z","submitted_at":"2021-04-30T07:40:35Z","title":"GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14806","snapshot_observed_at":"2026-08-16T12:38:39.227730Z","title":"Godiva: Gen- erating open-domain videos from natural descriptions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.227730Z"},"links":{"cited_paper":"/paper/2104.14806","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:e25f171f305437629b9eed66ecfa51beaef7fe26fd7ac43ce321a22ef53db5bd","observation_id":"18d6ff58-83ce-43d8-95bf-c94a328accb6","resolution":{"observed_at":"2026-08-16T12:38:39.227730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.851758Z","title":"Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling","venue":null,"work_id":"47ad6803-a400-4462-b2ad-634ec4143e46","year":2022},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.231929Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:81b070a48cc93fdafcb6a98c37e2229723333e8c39f885519815490e1fd11554","observation_id":"68b81096-9330-402e-b293-286acb0fad74","resolution":{"observed_at":"2026-08-16T12:38:39.856295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01776","last_updated":"2025-04-27T00:10:11Z","snapshot_observed_at":"2026-08-16T05:47:34.106216Z","submitted_at":"2025-02-03T19:29:16Z","title":"Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01776","snapshot_observed_at":"2026-08-16T12:38:39.235933Z","title":"Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.235933Z"},"links":{"cited_paper":"/paper/2502.01776","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:6e5a31259e2e4faa4de24d83a937b37a5d0bc5e445fc9657a0b10fb1439fdcd5","observation_id":"11aba5e0-8fb6-4d8a-83ff-7e7460eebaaf","resolution":{"observed_at":"2026-08-16T12:38:39.235933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11897","last_updated":"2025-04-02T13:25:35Z","snapshot_observed_at":"2026-08-16T12:57:40.879013Z","submitted_at":"2025-02-17T15:22:31Z","title":"DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation","version":2},"cited_work":{"arxiv_id":"2502.11897","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.11897","snapshot_observed_at":"2026-08-16T12:38:39.525175Z","title":"DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation","venue":"cs.CV","work_id":"bd89db9d-c66d-4eb4-ab00-c38ed9cf8c38","year":2025},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.273774Z"},"links":{"cited_paper":"/paper/2502.11897","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:b9adb0528f747c607d2ef37365eb31bcd2703fe38b4cc776e7a5d5f4a651ff7c","observation_id":"0aba210c-32b7-4284-a628-7169ef8c887f","resolution":{"observed_at":"2026-08-16T12:38:39.532010Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T12:38:39.802533Z","title":"Training-free and hardware-friendly acceleration for diffu- sion models via similarity-based token pruning","venue":null,"work_id":"b23f7611-a14f-48db-82af-3782b3105b19","year":null},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.356121Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:a9648c890d2636474d87743926822f7f1cf2828b3f3eee08f3fd8080e385196e","observation_id":"47fe8059-200c-4356-832f-d2cbf4e315de","resolution":{"observed_at":"2026-08-16T12:38:39.841175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.425758Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.425758Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:5f21e1da0f5f82d4e2cd1c24ca14da0613a383d4429be9df16c211d67935c7f6","observation_id":"cc845b07-edcf-4ae0-82c9-a0d547cb2e9e","resolution":{"observed_at":"2026-08-16T12:38:39.425758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:38:39.702536Z","title":"Cross- attention makes inference cumbersome in text-to-image dif- fusion models","venue":null,"work_id":"49cbe8cb-54e2-4e2f-be85-a3a2d758eccd","year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.458138Z"},"links":{"citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:2b883f63c0ec5a6cf34babe91eea779fc2772c3928b8d8ee417b34a453eb5509","observation_id":"73421633-e3aa-43eb-88d4-2929c029b1d1","resolution":{"observed_at":"2026-08-16T12:38:39.737343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12588","last_updated":"2025-02-27T07:00:30Z","snapshot_observed_at":"2026-08-16T13:24:31.259921Z","submitted_at":"2024-08-22T17:54:21Z","title":"Real-Time Video Generation with Pyramid Attention Broadcast","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12588","snapshot_observed_at":"2026-08-16T12:38:39.465001Z","title":"Real-time video generation with pyramid attention broad- cast","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.465001Z"},"links":{"cited_paper":"/paper/2408.12588","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:bb41079c3cc2e1c8a016c472a2083f8c8e3f16176012d4b202d30e97f02ce065","observation_id":"b35e6825-cabb-467f-9470-c2af8496806b","resolution":{"observed_at":"2026-08-16T12:38:39.465001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20404","last_updated":"2024-12-29T08:52:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-29T08:52:49Z","title":"Open-Sora: Democratizing Efficient Video Production for All","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.20404","snapshot_observed_at":"2026-08-16T12:38:39.468676Z","title":"Open-sora: Democratizing efficient video production for all","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T12:38:39.468676Z"},"links":{"cited_paper":"/paper/2412.20404","citing_paper":"/paper/2504.12259"},"observation_digest":"sha256:976e1a93fa35e9c98cda626c6051501b5f2235cf5cb80c26d068eaaadcecb62c","observation_id":"86f6fc95-ddf3-4dba-9692-427e16de919b","resolution":{"observed_at":"2026-08-16T12:38:39.468676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.12259","last_updated":"2025-04-16T17:09:13Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T12:31:46.037159Z","submitted_at":"2025-04-16T17:09:13Z","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":1,"verified_fuzzy":13},"total_outbound_references":44},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 3 inbound Pith citation observations for arXiv:2504.12259."}