{"as_of":"2026-08-08T22:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:69e027b1af7d92014fec2dc30ef1d0b623b8dc7120e8a08adda0e9b3adb366b6","coverage":[{"denominator":81,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":81,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T06:00:57.046150Z","state":"measured"},{"denominator":82,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":82,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T04:37:46.090777Z","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":"2026-05-12T06:06:24.603613Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"cited_work":{"arxiv_id":"2506.06275","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.06275","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Movie Facts and Fibs (MF \\^","venue":null,"work_id":"57157856-bde3-4cb5-8f4a-230bfacc0f78","year":null},"citing_paper":{"arxiv_id":"2605.09874","last_updated":"2026-05-11T01:59:59Z","snapshot_observed_at":"2026-07-30T14:13:44.494421Z","submitted_at":"2026-05-11T01:59:59Z","title":"EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-12T04:37:46.090777Z"},"links":{"cited_paper":"/paper/2506.06275","citing_paper":"/paper/2605.09874"},"observation_digest":"sha256:4222d442147378f7fbb6488d2ae1aa1fdc99cc715cf4022073341aafeebf283d","observation_id":"0247282f-46b0-4ec8-a7af-5cfc4b9fa650","resolution":{"observed_at":"2026-05-12T06:06:24.607724Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.06275/citation-record","integrity":"/paper/2506.06275/integrity","json":"/paper/2506.06275/citation-record.json","paper":"/paper/2506.06275"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.14219","last_updated":"2024-08-30T21:17:17Z","snapshot_observed_at":"2026-07-06T18:03:47.096406Z","submitted_at":"2024-04-22T14:32:33Z","title":"Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14219","snapshot_observed_at":"2026-08-07T06:00:56.837684Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.837684Z"},"links":{"cited_paper":"/paper/2404.14219","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:f2ab7673cb3892ea0b136d079779ff24508c153c2eb23456432c145315c6323c","observation_id":"3dbbe518-562b-43e6-b03c-8de5cd7c6d21","resolution":{"observed_at":"2026-08-07T06:00:56.837684Z","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":"2026-08-07T06:00:56.841252Z","title":"Infinibench: A comprehensive benchmark for large multimodal models in very long video understanding, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.841252Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:22de3ed232ff1bc66666343d78550c8a4b53dd2018de739947a3898b55addaeb","observation_id":"95538f57-de00-4044-aec6-42e5ecb61d05","resolution":{"observed_at":"2026-08-07T06:00:56.841252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-08-07T06:00:56.843888Z","title":"Qwen2.5-vl technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.843888Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:757e11a651873ad0654b5050c4744de573ff0d443af09ab3f060bdf90b0a1b90","observation_id":"7fb0bc7b-3ca1-46ea-92d6-e076dd9f3974","resolution":{"observed_at":"2026-08-07T06:00:56.843888Z","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":"2026-08-07T06:00:56.847080Z","title":"Memory consolidation enables long-context video understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.847080Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:bb182badfb605ea8b0149c89f3d3fa721548b437dd4739f2a7563d6a5845e6c8","observation_id":"a3a76b66-8636-4821-b3f8-77c24e7b0085","resolution":{"observed_at":"2026-08-07T06:00:56.847080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18403","last_updated":"2025-06-02T11:31:19Z","snapshot_observed_at":"2026-07-06T18:37:21.973331Z","submitted_at":"2024-06-26T14:56:13Z","title":"LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18403","snapshot_observed_at":"2026-08-07T06:00:56.849677Z","title":"Llms instead of human judges? a large scale empirical study across 20 nlp evaluation tasks.arXiv preprint arXiv:2406.18403, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.849677Z"},"links":{"cited_paper":"/paper/2406.18403","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:1923a172cc998bb40fc42fd7590a5df540ae0bd2b68cd78cd898d4708be017f6","observation_id":"179b2d20-9325-4ff1-b447-3b8fc0cc03b0","resolution":{"observed_at":"2026-08-07T06:00:56.849677Z","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":"2026-08-07T06:00:57.590453Z","title":"Hadzic, Taran Kota, Jimming He, Cristobal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, and Fei-Fei Li","venue":null,"work_id":"23b63b27-c5fa-41e7-ba74-17df4712f285","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.852611Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:3a57974bef3812234e19fa2503af2c1ef65afa732ff1e4b353ef366a7e12cead","observation_id":"b2c254c6-ec9c-4f92-9412-132a96c5f277","resolution":{"observed_at":"2026-08-07T06:00:57.592958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12075","last_updated":"2024-12-16T18:46:45Z","snapshot_observed_at":"2026-07-06T20:07:58.873037Z","submitted_at":"2024-12-16T18:46:45Z","title":"CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12075","snapshot_observed_at":"2026-08-07T06:00:56.855381Z","title":"Cg-bench: Clue-grounded question answering benchmark for long video understanding.arXiv preprint arXiv:2412.12075, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.855381Z"},"links":{"cited_paper":"/paper/2412.12075","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:ddf2316cffbfe16315005f144f6f41030f4e3db86d5c0e782e2bc1e609d1eef7","observation_id":"d426b64f-a31c-4848-bfb2-48061f7ec977","resolution":{"observed_at":"2026-08-07T06:00:56.855381Z","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":"2026-08-07T06:00:56.858110Z","title":"Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.858110Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:d08cd636abf94724108113dc642db84996aac25d83ae4b4dc6a6042d6ab3a64b","observation_id":"b4aafe70-e76b-4ea2-978e-0adce15a8fe8","resolution":{"observed_at":"2026-08-07T06:00:56.858110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17146","last_updated":"2024-12-05T14:28:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-25T17:59:51Z","title":"Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.17146","snapshot_observed_at":"2026-08-07T06:00:56.860531Z","title":"Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.860531Z"},"links":{"cited_paper":"/paper/2409.17146","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:d67316785fc600a992e108a931f86e989461c5fd02dfc33aabe3654257028c8e","observation_id":"b8268873-e78d-4ac4-b87e-286d9f98e358","resolution":{"observed_at":"2026-08-07T06:00:56.860531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14515","last_updated":"2024-10-30T13:38:10Z","snapshot_observed_at":"2026-07-06T18:34:24.078145Z","submitted_at":"2024-06-20T17:26:01Z","title":"MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14515","snapshot_observed_at":"2026-08-07T06:00:56.863411Z","title":"Mmbench-video: A long-form multi-shot benchmark for holistic video understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.863411Z"},"links":{"cited_paper":"/paper/2406.14515","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:75fa6d1189fa998e7c204dbb230fd9449d643bc5a72c092102dd3b4923a6b2a0","observation_id":"e8a93478-79eb-43dc-9b5d-4b9eed8e11e0","resolution":{"observed_at":"2026-08-07T06:00:56.863411Z","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":"2026-08-07T06:00:57.578850Z","title":"Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis, 2024","venue":null,"work_id":"5f4fef0a-f55b-4a72-bc4a-b97ebfb02919","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.866203Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:aa3625cb07592fc7150d5f63702e821641a8a8ef9746582e8e61da8ffef08c37","observation_id":"616ae0b2-6f27-4c8e-ad54-e29f750b57f8","resolution":{"observed_at":"2026-08-07T06:00:57.581669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.571285Z","title":"Video-mmmu: Evaluating knowledge acquisition from multi-discipline professional videos","venue":null,"work_id":"6fb4b838-9553-485e-8c62-77e2cc12c0b4","year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.871356Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:f9e3ab2018133928e8364dbbc9bb2a1dbcfc8cfda392316e71152e1059dd3b6b","observation_id":"88b87ae7-8177-4abd-ba3e-c8ff682564b9","resolution":{"observed_at":"2026-08-07T06:00:57.574381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.563202Z","title":"Movienet: A holistic dataset for movie understanding","venue":null,"work_id":"0c76dc6e-b131-4fa7-bdcd-edec1108c2c2","year":2020},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.876297Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:5413758b9127a2be914631d2a88d9e9fd351dccd6debb70ba541d69d847e0644","observation_id":"5ce38d0b-2dba-4664-bd79-57751542e4b5","resolution":{"observed_at":"2026-08-07T06:00:57.566263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.554436Z","title":"Stop uploading test data in plain text: Practical strategies for mitigating data contamination by evaluation benchmarks","venue":null,"work_id":"63cbf8fa-287f-429c-a6c5-11d6e82f5341","year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.878674Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:36d99c15052fa21a3e73b1ef7d42d61a4531824b3e439874992b7a05aba9dce2","observation_id":"a35c169a-c774-4c9d-98fa-faf3a21aa74c","resolution":{"observed_at":"2026-08-07T06:00:57.557448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.545522Z","title":"Needle in a haystack - pressure testing LLMs, 2024","venue":null,"work_id":"bd29cdbf-3896-47f9-8319-91b7d1d57126","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.883819Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:30b813bcf5a5c81940ef60291867fb324cf517499cb0d2ee14958df3c3121311","observation_id":"23e8bf43-a19a-48ba-be62-8e24581ad37f","resolution":{"observed_at":"2026-08-07T06:00:57.548694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:56.886154Z","title":"One thousand and one pairs: A “novel” challenge for long-context language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.886154Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:6704eceddb28eaf9958979552ab9b5d516bc1275696b0a504507d383f68f44fa","observation_id":"43e4fb47-36f0-4e12-9846-706375296cd4","resolution":{"observed_at":"2026-08-07T06:00:56.886154Z","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":"2026-08-07T06:00:56.888635Z","title":"TVQA: Localized, compositional video question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.888635Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:bef9b0a3dfa84e737f6dd236240b9c2898f2d8b5e7ea9bd46b92464cbc86ab92","observation_id":"7f4ee9c5-c60f-4997-8830-9cd8754bbeba","resolution":{"observed_at":"2026-08-07T06:00:56.888635Z","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":"2026-08-07T06:00:56.891037Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.891037Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:31f3226b70c4d937482b65a9ebc5a869b97f44b2d959d7cc14c7a171dedf213e","observation_id":"51d57200-a121-49dd-aee8-b6e92f6f5435","resolution":{"observed_at":"2026-08-07T06:00:56.891037Z","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":"2026-08-07T06:00:57.533278Z","title":"Merlot reserve: Neural script knowledge through vision and language and sound","venue":null,"work_id":"781e8399-f2da-4be9-a4be-e9a27b51d185","year":2022},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.893414Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:93df8c8d098f7e117be0f1906318a8f14959aab51ca62b1a9a0ea6525b1e48e7","observation_id":"75aff597-cc40-43a7-83aa-f1702c5a4ade","resolution":{"observed_at":"2026-08-07T06:00:57.536206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05993","last_updated":"2025-01-10T08:35:13Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T12:44:57Z","title":"Aria: An Open Multimodal Native Mixture-of-Experts Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05993","snapshot_observed_at":"2026-08-07T06:00:56.895831Z","title":"Aria: An open multimodal native mixture-of-experts model, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.895831Z"},"links":{"cited_paper":"/paper/2410.05993","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:3579bfc57e32a6297d4143a8130302860c1ce20e7ec7d323b99598b84f16b733","observation_id":"61a95390-c468-4b95-992e-c6f6636ede24","resolution":{"observed_at":"2026-08-07T06:00:56.895831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06355","last_updated":"2024-01-04T02:06:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-10T17:59:04Z","title":"VideoChat: Chat-Centric Video Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06355","snapshot_observed_at":"2026-08-07T06:00:56.898389Z","title":"Videochat: Chat-centric video understanding.arXiv preprint arXiv:2305.06355, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.898389Z"},"links":{"cited_paper":"/paper/2305.06355","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:fbd6a02b552229497fd1ae2209a7a61734569c84659f068d0b3b880095871723","observation_id":"d0161f92-a0c5-4a8c-a35f-243b89c77a80","resolution":{"observed_at":"2026-08-07T06:00:56.898389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03205","last_updated":"2025-05-30T20:24:51Z","snapshot_observed_at":"2026-08-08T08:18:12.659088Z","submitted_at":"2024-05-06T07:10:09Z","title":"Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.03205","snapshot_observed_at":"2026-08-07T06:00:56.901023Z","title":"Anchored answers: Unravelling positional bias in gpt-2’s multiple- choice questions.arXiv preprint arXiv:2405.03205, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.901023Z"},"links":{"cited_paper":"/paper/2405.03205","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:5f616bfd7a3c9412ab447b11cf73b44b45925105657c8e3357351ed9cf3bb43d","observation_id":"4eb44370-18c3-4b9d-9de5-3e902697d56d","resolution":{"observed_at":"2026-08-07T06:00:56.901023Z","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":"2026-08-07T06:00:56.903725Z","title":"Contrastive decoding: Open-ended text generation as optimization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.903725Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:893a00b9125bb1b976cfc6c7717a3ea92300ec1a322ed4943470d29816772d9f","observation_id":"23218241-3d3b-4161-b4b2-224ebe739eb4","resolution":{"observed_at":"2026-08-07T06:00:56.903725Z","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":"2026-08-07T06:00:56.906287Z","title":"Llama-vid: An image is worth 2 tokens in large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.906287Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:057f3d670adb195ff889bf89fa0eab164f306cd8822c781f573c0ea3a2aa8f4f","observation_id":"4985de5e-2abd-4ad8-9b8b-15a208ca95b9","resolution":{"observed_at":"2026-08-07T06:00:56.906287Z","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":"2026-08-07T06:00:57.521197Z","title":"World model on million-length video and language with blockwise ringattention","venue":null,"work_id":"1328768e-a12e-485b-894c-2be07b55227c","year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.908532Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:793f4303687063fb7c152d3f5f9308a3099478c61f044afa9877985a116f1d71","observation_id":"10def0f5-5245-457a-b658-355be0530ddf","resolution":{"observed_at":"2026-08-07T06:00:57.524542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:56.910861Z","title":"Visual instruction tuning, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.910861Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:665eb689a1b5581d0abc61c7e936e8390f57b100001a8e9a765b27738046199e","observation_id":"967fb098-2b18-4567-a4d8-4143df8e4060","resolution":{"observed_at":"2026-08-07T06:00:56.910861Z","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":"2026-08-07T06:00:57.510364Z","title":"Is your video language model a reliable judge? InThe Thirteenth International Conference on Learning Representations, 2025","venue":null,"work_id":"b0009361-9c86-4ca3-8691-9812a2c8c283","year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.913190Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:369c15c78ce2f9039c035b4321d5a51722507c72ce11ac9f3ab12ef59f2a4c1d","observation_id":"3d98c38e-8bda-4777-b07f-04ba65877ee6","resolution":{"observed_at":"2026-08-07T06:00:57.513146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.501858Z","title":"Nvila: Efficient frontier visual language models, 2024","venue":null,"work_id":"6dac277b-998c-4539-a5f3-44f6f79091e2","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.915429Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:70fc107fba49580332c49bbd0eca6e30c89197cc72b665fd804436dbe30fbfa6","observation_id":"76eae7de-0c9e-4fe0-901d-087e9b3766f9","resolution":{"observed_at":"2026-08-07T06:00:57.505461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14248","last_updated":"2025-05-30T17:01:57Z","snapshot_observed_at":"2026-07-06T19:35:48.603411Z","submitted_at":"2024-10-18T07:52:22Z","title":"Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models","version":2},"cited_work":{"arxiv_id":"2410.14248","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.14248","snapshot_observed_at":"2026-08-07T06:00:57.238555Z","title":"Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models","venue":"cs.CL","work_id":"0ae34638-9c40-4d63-a35a-6f210be846d8","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.917881Z"},"links":{"cited_paper":"/paper/2410.14248","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:5e707b21737f89960711495a4cd1c729f10cb771ea35665a936645ad5f13d915","observation_id":"dd4ac394-dfec-4cd4-8db4-7047bd0bcaf3","resolution":{"observed_at":"2026-08-07T06:00:57.241715Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20797","last_updated":"2024-06-17T17:51:50Z","snapshot_observed_at":"2026-08-07T21:47:16.436942Z","submitted_at":"2024-05-31T13:59:18Z","title":"Ovis: Structural Embedding Alignment for Multimodal Large Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20797","snapshot_observed_at":"2026-08-07T06:00:56.920387Z","title":"Ovis: Structural Embedding Alignment for Multimodal Large Language Model.arXiv e-prints, art","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.920387Z"},"links":{"cited_paper":"/paper/2405.20797","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:cbf9d0494dca634092dadc4c7d4b47eae82334d91a1c4300640dfd451c515476","observation_id":"e7ea8119-c001-4313-b8b4-2dd6293465b7","resolution":{"observed_at":"2026-08-07T06:00:56.920387Z","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":"2026-08-07T06:00:57.494665Z","title":"Valley: Video assistant with large language model enhanced ability, 2023","venue":null,"work_id":"13eb4957-5225-4af4-879c-823124124c16","year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.923163Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:1ee5f5ffad52f933840280991cd248113eb15ec1ec04e20c0570a07a00dc7196","observation_id":"30c21deb-f754-42cd-8196-a1b5eeebe7c5","resolution":{"observed_at":"2026-08-07T06:00:57.497326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.13281","last_updated":"2025-03-23T11:01:22Z","snapshot_observed_at":"2026-08-07T18:28:45.079559Z","submitted_at":"2024-11-20T12:48:34Z","title":"VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.13281","snapshot_observed_at":"2026-08-07T06:00:56.925414Z","title":"Videoau- toarena: An automated arena for evaluating large multimodal models in video analysis through user simulation.arXiv preprint arXiv:2411.13281, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.925414Z"},"links":{"cited_paper":"/paper/2411.13281","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:8f3ad83e9c78a492ec84d9aa5e0242e86a31e1e5738a15abf09914feb5e4da89","observation_id":"58b872c7-8679-4cdd-bba8-3cc549cdbb46","resolution":{"observed_at":"2026-08-07T06:00:56.925414Z","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":"2026-08-07T06:00:56.928000Z","title":"Video-chatgpt: Towards detailed video understanding via large vision and language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.928000Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:dde60e92098e94b5ee5876945cd28ac445ab91f5f039788f858341c8388e7d5b","observation_id":"bc071e67-7a13-4624-bd0d-15159caaee83","resolution":{"observed_at":"2026-08-07T06:00:56.928000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09126","last_updated":"2023-08-17T17:59:59Z","snapshot_observed_at":"2026-07-06T16:07:21.951225Z","submitted_at":"2023-08-17T17:59:59Z","title":"EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.09126","snapshot_observed_at":"2026-08-07T06:00:56.930356Z","title":"Egoschema: A diagnostic benchmark for very long-form video language understanding.arXiv preprint arXiv:2308.09126, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.930356Z"},"links":{"cited_paper":"/paper/2308.09126","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:686ecd2f271aa368ec0dc8eafda92421c9f996da0b6f75dea2ce7ba55f4e9e87","observation_id":"d3ebb7c6-b8f5-4554-81cc-2595dd0a4c2b","resolution":{"observed_at":"2026-08-07T06:00:56.930356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14996","last_updated":"2025-06-08T14:56:13Z","snapshot_observed_at":"2026-08-07T16:52:48.017259Z","submitted_at":"2025-03-19T08:45:03Z","title":"Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14996","snapshot_observed_at":"2026-08-07T06:00:56.933234Z","title":"Right answer, wrong score: Uncovering the inconsistencies of llm evaluation in multiple-choice question answering.arXiv preprint arXiv:2503.14996, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.933234Z"},"links":{"cited_paper":"/paper/2503.14996","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:580b5d263bb32eb5e97abb20d8d32d0af6ddaf275e68de4cac768a296e12beed","observation_id":"98d7f9db-20c5-445b-8e17-87402094ef20","resolution":{"observed_at":"2026-08-07T06:00:56.933234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09582","last_updated":"2025-01-18T00:52:42Z","snapshot_observed_at":"2026-07-06T20:06:09.333397Z","submitted_at":"2024-12-12T18:54:48Z","title":"Neptune: The Long Orbit to Benchmarking Long Video Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09582","snapshot_observed_at":"2026-08-07T06:00:56.935935Z","title":"Neptune: The long orbit to benchmarking long video understanding, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.935935Z"},"links":{"cited_paper":"/paper/2412.09582","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:4d3fccf27784f92eb94c51e30b4f6b21bfd7e6ea92929a49556ac9839d121f87","observation_id":"f37af5f2-89b8-4bcd-af6f-c0557e44a17c","resolution":{"observed_at":"2026-08-07T06:00:56.935935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-07T06:00:56.938513Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.938513Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:2e1d82d46e6e794d41f2644161c93e679ceb03ba795b1e700aa4597f6dc6693a","observation_id":"65d1812c-b78e-4778-84de-5d824fb31caf","resolution":{"observed_at":"2026-08-07T06:00:56.938513Z","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":"2026-08-07T06:00:56.941552Z","title":"Movie plot analysis via turning point identification","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.941552Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:fe0966862d374c334f6f882ad5853e5f2d9de44d670adaa0987fe680d1fef4c6","observation_id":"88b23c33-f7a5-4752-8eda-c3b0699c32fc","resolution":{"observed_at":"2026-08-07T06:00:56.941552Z","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":"10.18653/v1/2020.acl-main.174","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T06:00:57.080163Z","title":"Screenplay summariza- tion using latent narrative structure","venue":null,"work_id":"db3fa9a5-f319-4bfa-84ff-a283b20361ab","year":1920},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.944168Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:2b20089e6e6ae15e0bd0b02bbdcab45cb31c655e666c88fef42ffbdeaaff3320","observation_id":"b5976af9-30bf-423d-aa0e-455f78386ac3","resolution":{"observed_at":"2026-08-07T06:00:57.084836Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04447","last_updated":"2025-04-11T07:10:02Z","snapshot_observed_at":"2026-08-06T02:13:58.479161Z","submitted_at":"2024-12-05T18:57:23Z","title":"EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04447","snapshot_observed_at":"2026-08-07T06:00:56.946680Z","title":"Egoplan-bench2: A benchmark for multimodal large language model planning in real-world scenarios.arXiv preprint arXiv:2412.04447, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.946680Z"},"links":{"cited_paper":"/paper/2412.04447","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:ce01a2c14dc0de88f21af2c7959ab95732ad7733c2c97093e9244b2dc515b151","observation_id":"5900ee82-62fd-4757-bae9-cfd7a231a0fc","resolution":{"observed_at":"2026-08-07T06:00:56.946680Z","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":"2026-08-07T06:00:56.949537Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.949537Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:5fc321f028b2723ed76d3e2414dbc9d4e4568033df9b872520c0aae23984e38e","observation_id":"886a64cd-0adb-4b7c-a86c-86f03b35f197","resolution":{"observed_at":"2026-08-07T06:00:56.949537Z","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":"2026-08-07T06:00:56.951890Z","title":"Robust speech recognition via large-scale weak supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.951890Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:752d15842bd030d31ab7a6bc9d2701c6697f27549ddb9443260ab68c1caddde0","observation_id":"28df8268-ac92-432a-820f-81a3a296aedd","resolution":{"observed_at":"2026-08-07T06:00:56.951890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.08813","last_updated":"2024-10-21T03:08:08Z","snapshot_observed_at":"2026-08-07T17:42:30.374808Z","submitted_at":"2024-05-14T17:59:02Z","title":"CinePile: A Long Video Question Answering Dataset and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.08813","snapshot_observed_at":"2026-08-07T06:00:56.954513Z","title":"Cinepile: A long video question answering dataset and benchmark.arXiv preprint arXiv:2405.08813, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.954513Z"},"links":{"cited_paper":"/paper/2405.08813","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:1fad19cb35315ea2ceddedc6c687203534f8bb85cfb758c3f060bb00fb2d4e25","observation_id":"13e2c0fb-30ac-409e-9f52-7726c30ad6ae","resolution":{"observed_at":"2026-08-07T06:00:56.954513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19098","last_updated":"2025-05-19T10:18:07Z","snapshot_observed_at":"2026-07-06T20:28:57.453439Z","submitted_at":"2025-01-31T12:45:46Z","title":"$\\infty$-Video: A Training-Free Approach to Long Video Understanding via Continuous-Time Memory Consolidation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19098","snapshot_observed_at":"2026-08-07T06:00:56.957099Z","title":"McNamee, and André F","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.957099Z"},"links":{"cited_paper":"/paper/2501.19098","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:6a99e3cd178b4e0afc7c224e88549233bcfbbd2d935c91d3c2a63771b5723a9e","observation_id":"6f61abf0-2a63-4875-8b84-cfeeb0d97834","resolution":{"observed_at":"2026-08-07T06:00:56.957099Z","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":"2026-08-07T06:00:57.476119Z","title":"Trusting your evidence: Hallucinate less with context-aware decoding","venue":null,"work_id":"c06bfbfa-28d2-4f4e-930e-f170e4343204","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.959860Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:5d4ecc46eabdf51245f243943e88c9813b1ae56f35a5ee22509d2fce81b17996","observation_id":"ae2d1c1f-8cc0-49d6-834a-5a54be98c14a","resolution":{"observed_at":"2026-08-07T06:00:57.479032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13508","last_updated":"2025-03-13T19:42:04Z","snapshot_observed_at":"2026-08-07T17:05:48.634748Z","submitted_at":"2025-03-13T19:42:04Z","title":"It is Too Many Options: Pitfalls of Multiple-Choice Questions in Generative AI and Medical Education","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.13508","snapshot_observed_at":"2026-08-07T06:00:56.964951Z","title":"It is too many options: Pitfalls of multiple-choice questions in generative ai and medical education","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.964951Z"},"links":{"cited_paper":"/paper/2503.13508","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:3fb0b0c89537aa58abf6bf9551c599c962ecf92270391283233ee9d80392f0ac","observation_id":"835245b2-76a1-49c6-86be-dea589a25624","resolution":{"observed_at":"2026-08-07T06:00:56.964951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.16449","last_updated":"2024-03-09T06:43:37Z","snapshot_observed_at":"2026-08-05T04:26:02.499259Z","submitted_at":"2023-07-31T07:15:45Z","title":"MovieChat: From Dense Token to Sparse Memory for Long Video Understanding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.16449","snapshot_observed_at":"2026-08-07T06:00:56.967687Z","title":"Moviechat: From dense token to sparse memory for long video understanding.arXiv preprint arXiv:2307.16449, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.967687Z"},"links":{"cited_paper":"/paper/2307.16449","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:323c4b855951cc2962e7dfd3384b7bf2cbc46689ca409b7d93790c380da4b432","observation_id":"7e5fa39a-d49d-44f5-9ae8-4c8effec6e48","resolution":{"observed_at":"2026-08-07T06:00:56.967687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17176","last_updated":"2024-04-26T06:17:04Z","snapshot_observed_at":"2026-07-06T18:05:59.538624Z","submitted_at":"2024-04-26T06:17:04Z","title":"MovieChat+: Question-aware Sparse Memory for Long Video Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17176","snapshot_observed_at":"2026-08-07T06:00:56.970276Z","title":"Moviechat+: Question-aware sparse memory for long video question answering.arXiv preprint arXiv:2404.17176, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.970276Z"},"links":{"cited_paper":"/paper/2404.17176","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:3cf4547aeea490ac312ea93a2221fac6789b0d62e6424989b21a6b31ec3cae48","observation_id":"a72b4ca9-7698-4895-9ef0-3b6547706f43","resolution":{"observed_at":"2026-08-07T06:00:56.970276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-07T06:00:56.973042Z","title":"Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.973042Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:d51b5ce84a0b62c2ab121dc229bfb47958ef6681b1bdf4711dfe496d1ef3efc2","observation_id":"075f24ad-0cd7-47cc-b7f8-d969bae98396","resolution":{"observed_at":"2026-08-07T06:00:56.973042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14786","last_updated":"2025-02-20T18:08:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-20T18:08:29Z","title":"SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14786","snapshot_observed_at":"2026-08-07T06:00:56.975636Z","title":"Siglip 2: Multilingual vision- language encoders with improved semantic understanding, localization, and dense features","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.975636Z"},"links":{"cited_paper":"/paper/2502.14786","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:4e19bf27eb872116cb7f934c43783924291257e34f172740e947f6077b13b38d","observation_id":"a695d49b-f432-4f8e-bcdb-a69c8560da44","resolution":{"observed_at":"2026-08-07T06:00:56.975636Z","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":"2026-08-07T06:00:57.467883Z","title":"AdaCAD: Adaptively decoding to balance conflicts between contextual and parametric knowledge","venue":null,"work_id":"da826aa9-7b5c-4b22-a32f-d3f976648568","year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.978154Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:c1f8aba45b991715505f0ea6faa5ea8e7834fbc4af1696ba7707845fa0f315ff","observation_id":"08be5ddc-2573-4a46-9640-3a4072847048","resolution":{"observed_at":"2026-08-07T06:00:57.471151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11230","last_updated":"2025-02-11T02:17:24Z","snapshot_observed_at":"2026-08-06T02:18:35.725376Z","submitted_at":"2024-06-17T05:54:06Z","title":"Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11230","snapshot_observed_at":"2026-08-07T06:00:56.980537Z","title":"Multimodal needle in a haystack: Benchmarking long- context capability of multimodal large language models.arXiv preprint arXiv:2406.11230, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.980537Z"},"links":{"cited_paper":"/paper/2406.11230","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:6d8ab55bd4936beef665328c77e78c122fc763de224aa30a399eec548fee24e3","observation_id":"7f3d84b0-1d89-45bf-afde-9d63081f84c0","resolution":{"observed_at":"2026-08-07T06:00:56.980537Z","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":"2026-08-07T06:00:56.983169Z","title":"Lvbench: An extreme long video understanding benchmark, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.983169Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:86c81806b95f5a34947ed7989cdc2048ba470b9b51e61985264a8eb7af44f2d7","observation_id":"0bfbd83a-e957-481a-a63d-885e263d8b0d","resolution":{"observed_at":"2026-08-07T06:00:56.983169Z","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":"2026-08-07T06:00:56.985963Z","title":"Videoagent: Long-form video understanding with large language model as agent","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.985963Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:b57b6049fc1747e3dcc7308e75707db1b7d04356e664a5ef4ce475aa9ad32f9f","observation_id":"9fb1d83c-2578-4f51-8ecc-0ca55ff4e8ab","resolution":{"observed_at":"2026-08-07T06:00:56.985963Z","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":"2026-08-07T06:00:57.453271Z","title":"Videollamb: Long-context video understanding with recurrent memory bridges, 2024","venue":null,"work_id":"aa919cc3-08ee-4930-83e1-3083a612d1b8","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.988510Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:93fa85ab9cf26e4d1540b30bf2289e60cc56528227359c95b5d9ea85d63c53f0","observation_id":"462885d5-2bf7-4164-9e8f-e35caa3bab70","resolution":{"observed_at":"2026-08-07T06:00:57.455993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19209","last_updated":"2025-03-14T13:57:16Z","snapshot_observed_at":"2026-08-05T17:33:53.862042Z","submitted_at":"2024-05-29T15:49:09Z","title":"VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19209","snapshot_observed_at":"2026-08-07T06:00:56.990818Z","title":"Videotree: Adaptive tree-based video representation for llm reasoning on long videos.arXiv preprint arXiv:2405.19209, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.990818Z"},"links":{"cited_paper":"/paper/2405.19209","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:f8b4f751a9c35b8ef94e52f040a2fbd2081036f981bbeb04766619e402f60432","observation_id":"bafa5580-7d0f-462f-a507-2c1989dff989","resolution":{"observed_at":"2026-08-07T06:00:56.990818Z","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":"2026-08-07T06:00:57.445727Z","title":"Tenenbaum, and Chuang Gan","venue":null,"work_id":"19520402-0858-4f7e-ab91-6091a8bc9c02","year":2021},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.993378Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:8b88f564d26ef1c07a3b2744ebdaf4d0e5236f19ad744cac2db0e3686ee8cb8a","observation_id":"ccb18b42-d2ee-4817-86cf-18b7b39e0c50","resolution":{"observed_at":"2026-08-07T06:00:57.448759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:56.995632Z","title":"Longvideobench: A benchmark for long-context interleaved video-language understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.995632Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:3de002657d1965e0579336ba56677fdd1cc8290198856d39f4997e92aee3c0fd","observation_id":"6fc27423-4722-45b9-9f75-2e30d3e446ed","resolution":{"observed_at":"2026-08-07T06:00:56.995632Z","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":"2026-08-07T06:00:56.997977Z","title":"Next-qa: Next phase of question- answering to explaining temporal actions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.997977Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:708531620695e0540b8926f0924b1d7fce98fda2e0d988796a5a0c136d23447e","observation_id":"6760223f-58d2-4ec1-bbd7-148a5b92981f","resolution":{"observed_at":"2026-08-07T06:00:56.997977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20215","last_updated":"2025-03-26T04:17:55Z","snapshot_observed_at":"2026-08-06T08:46:20.194739Z","submitted_at":"2025-03-26T04:17:55Z","title":"Qwen2.5-Omni Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.20215","snapshot_observed_at":"2026-08-07T06:00:57.000343Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.000343Z"},"links":{"cited_paper":"/paper/2503.20215","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:db25ea0c0ee6f50d949af7269ae666362b18a5350030ff1e0dd3d07be6c67b2b","observation_id":"0dbb8263-fde6-49b7-8c90-ba9d9797e36a","resolution":{"observed_at":"2026-08-07T06:00:57.000343Z","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":"2026-08-07T06:00:57.002924Z","title":"Pllava : Parameter-free llava extension from images to videos for video dense captioning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.002924Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:4cc93b323297d1585cf27a0585e51686bc5375ec9054dfae2c383fd097a352e9","observation_id":"64d2832e-edb9-4cf8-be2f-ef3b14498fc6","resolution":{"observed_at":"2026-08-07T06:00:57.002924Z","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":"2026-08-07T06:00:57.427756Z","title":"Just ask: Learning to answer questions from millions of narrated videos","venue":null,"work_id":"63194d34-e140-4992-be0b-7ba074985def","year":2021},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.005333Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:12bd4d325df0a9805cbdd9fc185ad0e06dc161197329d339a735c9d16d024eb4","observation_id":"a62d46c6-1036-497c-96f4-38cdbb35b660","resolution":{"observed_at":"2026-08-07T06:00:57.430391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.420436Z","title":"Justice or prejudice? quantifying biases in LLM-as-a-judge","venue":null,"work_id":"e1a8d0f8-e355-402f-870e-42e517255203","year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.007608Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:0baab15281e449b1ad88eb9e214f617d859e9595ff413380abfc0cf5ff9fc1f2","observation_id":"9d7c74ba-3881-466f-8a23-3ed41b43d464","resolution":{"observed_at":"2026-08-07T06:00:57.423281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.412893Z","title":"Activitynet-qa: A dataset for understanding complex web videos via question answering","venue":null,"work_id":"a52b0e3c-2788-4adb-a344-77fac04a2518","year":2019},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.009993Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:77e7ea0b5367036cf8e55160ac816a8ffe564a2be4dd7d3b21ddf58da658a74d","observation_id":"c3633974-9125-46c9-90b8-b5523148329a","resolution":{"observed_at":"2026-08-07T06:00:57.415781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.405768Z","title":"Merlot: Multimodal neural script knowledge models","venue":null,"work_id":"93823014-ffe8-4914-8fac-3dd73c626b4b","year":2021},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.012364Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:72825b22e0410b8294e0f17f5a36f743d62694b158661348aca2f851acc28971","observation_id":"50f3b593-8d14-4364-9b52-56505b331cd0","resolution":{"observed_at":"2026-08-07T06:00:57.408407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13106","last_updated":"2025-06-03T03:33:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T18:59:46Z","title":"VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13106","snapshot_observed_at":"2026-08-07T06:00:57.014587Z","title":"Videollama 3: Frontier multimodal foundation models for image and video understanding.arXiv preprint arXiv:2501.13106, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.014587Z"},"links":{"cited_paper":"/paper/2501.13106","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:2b57a6195543e69cbbac687880f34f1790748a93eb5d84c00928b3932e40f9ff","observation_id":"1944a34a-1968-42dc-abc7-646a0bf944a0","resolution":{"observed_at":"2026-08-07T06:00:57.014587Z","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":"2026-08-07T06:00:57.398445Z","title":"A simple llm framework for long-range video question-answering","venue":null,"work_id":"e4ec5785-0a17-49de-8585-51508aae6828","year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.017356Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:10240588a5e3922de7de845078e6a317cbd1a5c1a559b598fa41b36ac42c7a76","observation_id":"10bd0a87-1047-4102-b7ed-356e09ae2a06","resolution":{"observed_at":"2026-08-07T06:00:57.401439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02858","last_updated":"2023-10-25T06:23:31Z","snapshot_observed_at":"2026-07-06T15:38:39.712379Z","submitted_at":"2023-06-05T13:17:27Z","title":"Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02858","snapshot_observed_at":"2026-08-07T06:00:57.019680Z","title":"Video-llama: An instruction-tuned audio-visual language model for video understanding.arXiv preprint arXiv:2306.02858, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.019680Z"},"links":{"cited_paper":"/paper/2306.02858","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:653c7aa9c4aa7e226db2ffd67f468796c4243a885d9ebcb43167eac7e4ab804a","observation_id":"1e4a762f-7acb-4b53-9d18-3bfc005b705b","resolution":{"observed_at":"2026-08-07T06:00:57.019680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04817","last_updated":"2025-09-01T05:09:27Z","snapshot_observed_at":"2026-08-08T07:28:54.378232Z","submitted_at":"2023-12-08T03:33:38Z","title":"LvBench: A Benchmark for Long-form Video Understanding with Versatile Multi-modal Question Answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04817","snapshot_observed_at":"2026-08-07T06:00:57.022279Z","title":"Movqa: A benchmark of versatile question-answering for long-form movie understanding, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.022279Z"},"links":{"cited_paper":"/paper/2312.04817","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:6caa0869c63b652d19cbfc4e4b51f3e418749fe17a3cbf5547da03bc04309a6d","observation_id":"21a9f31e-4cd3-4917-a883-0863f50c32fb","resolution":{"observed_at":"2026-08-07T06:00:57.022279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02713","last_updated":"2025-08-01T16:40:14Z","snapshot_observed_at":"2026-08-02T12:24:31.329178Z","submitted_at":"2024-10-03T17:36:49Z","title":"LLaVA-Video: Video Instruction Tuning With Synthetic Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02713","snapshot_observed_at":"2026-08-07T06:00:57.024929Z","title":"Video Instruction Tuning With Synthetic Data.arXiv e-prints, art","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.024929Z"},"links":{"cited_paper":"/paper/2410.02713","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:5668221d5ed735a80f6ca954f18244b82a74439b311313c946c586e8f966b466","observation_id":"3b014608-1e4e-4c3d-bae1-f2fe67a62cb5","resolution":{"observed_at":"2026-08-07T06:00:57.024929Z","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":"2026-08-07T06:00:57.391089Z","title":"Needle in a video haystack: A scalable synthetic evaluator for video MLLMs","venue":null,"work_id":"abbb5920-f477-4cbf-9a7c-f14b033f534c","year":2025},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.027321Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:0fad1e7591648a6ec3d332b984a416e4d2d070dfed3e173acdc41d6d6dde508f","observation_id":"a42027df-1fc4-417e-b038-935d905e835b","resolution":{"observed_at":"2026-08-07T06:00:57.393887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04264","last_updated":"2025-01-01T15:53:58Z","snapshot_observed_at":"2026-08-03T20:38:36.602554Z","submitted_at":"2024-06-06T17:09:32Z","title":"MLVU: Benchmarking Multi-task Long Video Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04264","snapshot_observed_at":"2026-08-07T06:00:57.029729Z","title":"Mlvu: A comprehensive benchmark for multi-task long video understanding.arXiv preprint arXiv:2406.04264, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.029729Z"},"links":{"cited_paper":"/paper/2406.04264","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:513cb563b00807efcc265c43925afbe0e4f78dc75637137fee02bcdf6043fd85","observation_id":"1bede31b-70d3-40c3-9001-27aa1b135876","resolution":{"observed_at":"2026-08-07T06:00:57.029729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.10479","last_updated":"2025-04-19T03:47:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-14T17:59:25Z","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10479","snapshot_observed_at":"2026-08-07T06:00:57.032403Z","title":"Pool” of movies (the “Pool","venue":null,"work_id":null,"year":1945},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.032403Z"},"links":{"cited_paper":"/paper/2504.10479","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:f8d83b66a8973464ce2b1d1e711bdb47b3d7b6bf2fe28a71e1d524120882fa68","observation_id":"ef4cd2f1-7031-42bb-99f4-518c23f15f8a","resolution":{"observed_at":"2026-08-07T06:00:57.032403Z","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":"2026-08-07T06:00:57.383704Z","title":"The two claims should differ by minimal edits, meaning they should be as similar as possible while maintaining contrast","venue":null,"work_id":"1425ee4b-cacf-47e4-80de-3268b4a2fbfb","year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.035514Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:7816b51aafe393ae5116e328d5d6ec07ffe67949d62aa46065111dc8f0436fb5","observation_id":"b500fd35-3dca-4ee8-9c01-3246711c998a","resolution":{"observed_at":"2026-08-07T06:00:57.386454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.376331Z","title":"Examples for Reasoning Granularity","venue":null,"work_id":"518cf20f-a0a5-4286-be35-9dd16b7ce1cf","year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.037997Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:65af673c7ce34313989b75fc6698e2dec44edbba1e8580877c404854026b4254","observation_id":"9ae08d2f-83f7-4ef2-b045-6e00a89a15c4","resolution":{"observed_at":"2026-08-07T06:00:57.378904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.368930Z","title":"Other\" and suggest a new category. Note:The categorization is based on both claims (fact and fib). Check the examples provided in the “Examples for Comprehension Dimensions","venue":null,"work_id":"3e23d7ec-5b08-459f-a590-7038e5b071fe","year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.040534Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:e0a96eecd12b92073c0a72c7163e21f66ba4bf8333a824cf5d3f5aa6ef0b9b96","observation_id":"18497632-1550-42c2-84ff-5226ef626da0","resolution":{"observed_at":"2026-08-07T06:00:57.371793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.361574Z","title":"Pay attention to details and context in the movie, as some claims may be subtle or require careful reasoning","venue":null,"work_id":"e81fea86-cbe5-47f5-9ed7-16de8292843e","year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.043281Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:da151edb668cd0683ba5eb30738f0d0be70ee5d5ae337d6699cf097f33fb90e4","observation_id":"dda476c8-6b53-4b78-8c16-5a594fce1e60","resolution":{"observed_at":"2026-08-07T06:00:57.364350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:57.354363Z","title":"Start Classifying Claims","venue":null,"work_id":"6b3d2e61-6106-4cff-8684-ab28046d2b0b","year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:57.046150Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:688d1aa50b9480dbeecd74d5fee195318517ac025e44a7e5c8c62064b20e9d3e","observation_id":"d9a24bfa-ba3a-4c3b-8cc8-b974e1502508","resolution":{"observed_at":"2026-08-07T06:00:57.356777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T06:00:56.881019Z","title":"doi: 10.18653/v1/2023.emnlp-main.308","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.881019Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:69f0e24814704750101aa3827d5a256cff5cac7cfa80273edd232e11c710a4b2","observation_id":"e2eb4797-bc4b-4baa-8094-4ec82f17dc74","resolution":{"observed_at":"2026-08-07T06:00:56.881019Z","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":"2026-08-07T06:00:56.962238Z","title":"doi: 10.18653/v1/2024.naacl-short.69","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.962238Z"},"links":{"citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:30c64fb4890d75bf4a5e13234f99d21e9e11888a1a121d397e388da2f516eadc","observation_id":"8dda0679-3ce2-4ba9-9350-a42df4b54a9f","resolution":{"observed_at":"2026-08-07T06:00:56.962238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13826","last_updated":"2025-01-23T16:51:47Z","snapshot_observed_at":"2026-07-06T20:25:03.950783Z","submitted_at":"2025-01-23T16:51:47Z","title":"Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13826","snapshot_observed_at":"2026-08-07T06:00:56.873858Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T06:00:56.873858Z"},"links":{"cited_paper":"/paper/2501.13826","citing_paper":"/paper/2506.06275"},"observation_digest":"sha256:f69eacd290c89dd9a695848e58bd9be94915fdb2edc74451a6c3587ffbb934b5","observation_id":"83866043-2fe4-421c-baad-5889f5fab71d","resolution":{"observed_at":"2026-08-07T06:00:56.873858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.06275","last_updated":"2025-06-06T17:58:36Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T21:48:03.079145Z","submitted_at":"2025-06-06T17:58:36Z","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding"},"reference_resolution":{"displayed":81,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":53,"verified_exact":2,"verified_fuzzy":26},"total_outbound_references":81},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 1 inbound Pith citation observation for arXiv:2506.06275."}