{"as_of":"2026-08-11T12:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b008812a55345bb869b97b5a79a0ffdaad02c6b7fbec7544f3129bfa16a25fa","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:30:11.718568Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.14948/citation-record","integrity":"/paper/2505.14948/integrity","json":"/paper/2505.14948/citation-record.json","paper":"/paper/2505.14948"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T15:30:10.190854Z","title":"Accessed: 2025-02-10","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.190854Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:f91a8fb4d5454a3eff5355171e028a0ccf4022724ad397d2877448d9d23db8cd","observation_id":"df9c7ff3-8b35-4535-95c0-4c1abba71632","resolution":{"observed_at":"2026-08-07T15:30:10.190854Z","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":"2026-08-07T15:30:12.045017Z","title":"pole” is detected as a “black cart","venue":null,"work_id":"e354037a-3523-4153-9092-9f48205efd4a","year":2024},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.718568Z"},"links":{"citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:9e24d70cea49c20f55b3403a4ae8bfcc685a6a22be6e4ee38c8ebd6c6748970f","observation_id":"22ed4e6a-8782-48f2-a013-c00d7edbbb8f","resolution":{"observed_at":"2026-08-07T15:30:12.147621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07696","last_updated":"2022-11-14T08:38:19Z","snapshot_observed_at":"2026-08-09T13:27:02.122300Z","submitted_at":"2022-06-15T17:44:47Z","title":"Diffusion Models for Video Prediction and Infilling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07696","snapshot_observed_at":"2026-08-07T15:30:10.812764Z","title":"Diffusion models for video prediction and infilling.arXiv preprint arXiv:2206.07696,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.812764Z"},"links":{"cited_paper":"/paper/2206.07696","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:234bf16a623438a1a79086f3d551f8737b1cf2972344d72a68e34b8679937a78","observation_id":"4f6330c6-4ae4-4707-8d25-e240bc47d163","resolution":{"observed_at":"2026-08-07T15:30:10.812764Z","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":"2026-08-07T15:30:12.649395Z","title":"Car that knows before you do: Anticipating maneuvers via learning temporal driving models","venue":null,"work_id":"2f737e29-09ee-4dbe-8b81-8d6c28d7dc0b","year":2025},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.883570Z"},"links":{"citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:b3a20b62a703bc2555f909c2fe4fbefe6cc4e102885311bfbc6f37d76fe1c8ed","observation_id":"41d371fa-0b0d-4c1a-bf19-b3adc726e8e9","resolution":{"observed_at":"2026-08-07T15:30:12.747071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13311","last_updated":"2023-10-11T06:28:41Z","snapshot_observed_at":"2026-08-09T06:54:52.641338Z","submitted_at":"2023-05-22T17:59:45Z","title":"VDT: General-purpose Video Diffusion Transformers via Mask Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13311","snapshot_observed_at":"2026-08-07T15:30:11.076093Z","title":"Vdt: An empirical study on video diffusion with transformers.arXiv preprint arXiv:2305.13311, 3(5):9,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.076093Z"},"links":{"cited_paper":"/paper/2305.13311","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:5316d0234dca946681a4523957537fd272c1fbc2c3e1a3d6577b209e6bb56599","observation_id":"3f2a45de-1123-4c3e-b497-16d9f3cc2d8e","resolution":{"observed_at":"2026-08-07T15:30:11.076093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.11743","last_updated":"2023-06-19T08:30:56Z","snapshot_observed_at":"2026-08-01T15:44:15.604385Z","submitted_at":"2022-11-21T18:59:33Z","title":"SinFusion: Training Diffusion Models on a Single Image or Video","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.11743","snapshot_observed_at":"2026-08-07T15:30:11.156461Z","title":"Sinfusion: Training diffusion models on a single image or video.arXiv preprint arXiv:2211.11743,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.156461Z"},"links":{"cited_paper":"/paper/2211.11743","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:fac32ef433c31ce6da6f4101529f7e08363e63b9bda671b9605991206111447e","observation_id":"837119ae-ef25-4c63-be04-abe6e3618ffa","resolution":{"observed_at":"2026-08-07T15:30:11.156461Z","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":"2026-08-07T15:30:12.434661Z","title":"A fast algorithm for nonlinearly constrained optimization calculations","venue":null,"work_id":"b5445db4-314e-4f14-a586-3a1bc65c99f3","year":1977},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.246627Z"},"links":{"citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:581300426a28e185da96c448b2dcdf736b4d628a3731aab2fff450ed9c59fab5","observation_id":"8cde052d-4e14-4b71-a69e-fd28ea808929","resolution":{"observed_at":"2026-08-07T15:30:12.533415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18400","last_updated":"2025-03-20T16:37:17Z","snapshot_observed_at":"2026-08-09T12:02:00.353147Z","submitted_at":"2024-04-29T03:30:06Z","title":"LLM-SR: Scientific Equation Discovery via Programming with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18400","snapshot_observed_at":"2026-08-07T15:30:11.470078Z","title":"Llm-sr: Scientific equation discovery via programming with large language models.arXiv preprint arXiv:2404.18400,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.470078Z"},"links":{"cited_paper":"/paper/2404.18400","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:137367dd8f45115d0ae95bdb2174c911f36fbbdb5ac532581bca823351d3ecd7","observation_id":"816c6a57-fd57-4468-bd45-8dba583a1448","resolution":{"observed_at":"2026-08-07T15:30:11.470078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12275","last_updated":"2024-09-20T18:56:41Z","snapshot_observed_at":"2026-08-08T15:50:41.166007Z","submitted_at":"2024-02-19T16:39:18Z","title":"WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12275","snapshot_observed_at":"2026-08-07T15:30:11.560232Z","title":"Worldcoder, a model-based llm agent: Building world models by writing code and interacting with the environment.arXiv preprint arXiv:2402.12275,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.560232Z"},"links":{"cited_paper":"/paper/2402.12275","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:6778b46a84e7292da7b2b49a84c019d7ada1e853266ca4118d7c2382ecbe5d38","observation_id":"3edb1114-cb0d-4895-8506-bc5228bbc993","resolution":{"observed_at":"2026-08-07T15:30:11.560232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14159","last_updated":"2024-01-25T13:12:09Z","snapshot_observed_at":"2026-07-06T17:20:25.138890Z","submitted_at":"2024-01-25T13:12:09Z","title":"Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14159","snapshot_observed_at":"2026-08-07T15:30:11.338776Z","title":"Grounded sam: Assembling open-world models for diverse visual tasks.arXiv preprint arXiv:2401.14159,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.338776Z"},"links":{"cited_paper":"/paper/2401.14159","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:f11f0c496a5d06ac70ac178946b9498f718a8fa309f87e9029224336c7971094","observation_id":"141c27fc-1b57-4ace-8d88-c83c98ddbab0","resolution":{"observed_at":"2026-08-07T15:30:11.338776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.13221","last_updated":"2023-03-20T17:29:45Z","snapshot_observed_at":"2026-08-10T00:51:42.114461Z","submitted_at":"2022-11-23T18:58:39Z","title":"Latent Video Diffusion Models for High-Fidelity Long Video Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.13221","snapshot_observed_at":"2026-08-07T15:30:10.713998Z","title":"Latent video diffusion models for high-fidelity video generation with arbitrary lengths.arXiv preprint arXiv:2211.13221, 2(3):4,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.713998Z"},"links":{"cited_paper":"/paper/2211.13221","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:c5356b95476f2cffd6218f45a2b4342016f222986672e7838ccc40b37cebbbcb","observation_id":"a71249b7-c2d8-400b-b009-8cdece99d48a","resolution":{"observed_at":"2026-08-07T15:30:10.713998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02385","last_updated":"2025-06-22T03:30:55Z","snapshot_observed_at":"2026-08-02T22:45:54.982294Z","submitted_at":"2024-11-04T18:53:05Z","title":"How Far is Video Generation from World Model: A Physical Law Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02385","snapshot_observed_at":"2026-08-07T15:30:10.968194Z","title":"How far is video generation from world model: A physical law perspective.arXiv preprint arXiv:2411.02385,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.968194Z"},"links":{"cited_paper":"/paper/2411.02385","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:a9a0d88a71ab069968ddd5d30ab0b2d4c4897a08064f694928b4425b64c33b07","observation_id":"b2fa6464-2d89-486f-b47a-e8d3a8cb11bc","resolution":{"observed_at":"2026-08-07T15:30:10.968194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15127","last_updated":"2023-11-25T22:28:38Z","snapshot_observed_at":"2026-08-07T21:47:08.589400Z","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":"2026-08-07T15:30:10.371648Z","title":"Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.371648Z"},"links":{"cited_paper":"/paper/2311.15127","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:ede03bed0083f438f2421bd651cd68705953536889aeeb1a489a944896a009f7","observation_id":"3733353e-b3c8-406b-b5fd-c08f5c811b4d","resolution":{"observed_at":"2026-08-07T15:30:10.371648Z","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":"2026-08-07T15:30:12.245843Z","title":"(2015) 30K 0.0176 0.0187 0.0502 0.0544Galileo Wu et al","venue":null,"work_id":"44a97a4e-9196-49fa-84ca-88b22b457c2b","year":2015},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:11.638243Z"},"links":{"citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:b83cda788fa4b3db7926bfdccaf4f7e88bffafc5ca910739075f2901b03ca773","observation_id":"68508ad9-c66a-49cc-8027-b3cb000bb14c","resolution":{"observed_at":"2026-08-07T15:30:12.334342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.08027","last_updated":"2026-04-23T21:02:30Z","snapshot_observed_at":"2026-07-30T08:49:18.908938Z","submitted_at":"2024-11-12T18:56:58Z","title":"LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.08027","snapshot_observed_at":"2026-08-07T15:30:10.507691Z","title":"Llmphy: Complex physical reasoning using large language models and world models.arXiv preprint arXiv:2411.08027,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.507691Z"},"links":{"cited_paper":"/paper/2411.08027","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:b2a9f278de7a2197759729831a3dd879dd4e363417d823efb3034ad24dc09eb3","observation_id":"01a15451-bd44-4d6e-bc9b-3ff41946bcae","resolution":{"observed_at":"2026-08-07T15:30:10.507691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.11252","last_updated":"2018-03-06T16:35:06Z","snapshot_observed_at":"2026-08-03T04:41:23.218059Z","submitted_at":"2017-10-30T21:48:54Z","title":"Stochastic Variational Video Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.11252","snapshot_observed_at":"2026-08-07T15:30:10.258176Z","title":"Stochastic variational video prediction.arXiv preprint arXiv:1710.11252,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.258176Z"},"links":{"cited_paper":"/paper/1710.11252","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:d30706c910bc89a7ea3fc4ceb2e210a382fb992423baf6c5c3b0fa91ab91a980","observation_id":"483b5d7e-db23-43e9-9c2c-29fb8cd5ca6f","resolution":{"observed_at":"2026-08-07T15:30:10.258176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.01582","last_updated":"2023-05-05T17:44:07Z","snapshot_observed_at":"2026-08-11T11:36:50.605147Z","submitted_at":"2023-05-02T16:31:35Z","title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.01582","snapshot_observed_at":"2026-08-07T15:30:10.635908Z","title":"Interpretable machine learning for science with pysr and symbolicregression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:10.635908Z"},"links":{"cited_paper":"/paper/2305.01582","citing_paper":"/paper/2505.14948"},"observation_digest":"sha256:1ceda27fb250d483ad7d14ee62540bfb9551aff03cc2eb1df317faa3eafadbf0","observation_id":"18fd8010-cef0-4c43-a0bd-1b5a7c624d55","resolution":{"observed_at":"2026-08-07T15:30:10.635908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14948","last_updated":"2025-05-20T22:17:47Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T06:55:07.608375Z","submitted_at":"2025-05-20T22:17:47Z","title":"Programmatic Video Prediction Using Large Language Models"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":17},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2505.14948."}