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This research introduces LF-VideoQA, a novel dataset that challenges video large language models to perform long-form reasoning using theory-of-mind questions derived from film narratives. The study reveals significant gaps in current visual-language models, showing that even with access to screenplays, models struggle to synthesize evidence across hours of content and that visual data remains superior to captions for these tasks.
LF-VideoQA: Evaluating Long-Video Understanding with Long-Form Theory-of-Mind Questions
arXiv:2611.00383v1 Announce Type: cross Abstract: The hardest part of understanding a long video is not pinpointing any one moment but synthesizing many, e.g., answering a question that requires tracking what develops across hours of content and articulating how the pieces fit together. To date, most long-video QA datasets require only a short answer or a multiple-choice selection, oversimplifying the long-video understanding task to retrieval. We introduce LF-VideoQA, a dataset for long-form, open-ended question answering over long narrative videos built on theory-of-mind questions: a character's belief depends on everything they have seen or been told, so each question demands assembling and reasoning from evidence distributed across the video. The dataset contains more than 6,300 questions on 94 recent films; each requires reasoning over scenes spanning an average of 23 minutes and comes with a question-specific rubric for recall and precision scoring. The dataset is created entirely automatically by annotating screenplays with scene events and each character's first-order beliefs and meta-beliefs, then generating (question, answer, rubric) triples through LLM-based question generation and verification; human studies validate their quality. We evaluate four families of state-of-the-art open-source VLMs across three settings and find the task highly challenging: (i) even an oracle with access to the screenplay falls well short of complete answers; (ii) the best non-oracle model attains barely half of the recall level of the oracle; (iii) unlike on prior long-video benchmarks, captions and subtitles underperform the video itself, indicating that the questions need visual evidence; and (iv) recall is the predominant bottleneck, with answers omitting many required rubric claims.
Abstract:The hardest part of understanding a long video is not pinpointing any one moment but synthesizing many, e.g., answering a question that requires tracking what develops across hours of content and articulating how the pieces fit together. To date, most long-video QA datasets require only a short answer or a multiple-choice selection, oversimplifying the long-video understanding task to retrieval. We introduce LF-VideoQA, a dataset for long-form, open-ended question answering over long narrative videos built on theory-of-mind questions: a character's belief depends on everything they have seen or been told, so each question demands assembling and reasoning from evidence distributed across the video. The dataset contains more than 6,300 questions on 94 recent films; each requires reasoning over scenes spanning an average of 23 minutes and comes with a question-specific rubric for recall and precision scoring. The dataset is created entirely automatically by annotating screenplays with scene events and each character's first-order beliefs and meta-beliefs, then generating (question, answer, rubric) triples through LLM-based question generation and verification; human studies validate their quality. We evaluate four families of state-of-the-art open-source VLMs across three settings and find the task highly challenging: (i) even an oracle with access to the screenplay falls well short of complete answers; (ii) the best non-oracle model attains barely half of the recall level of the oracle; (iii) unlike on prior long-video benchmarks, captions and subtitles underperform the video itself, indicating that the questions need visual evidence; and (iv) recall is the predominant bottleneck, with answers omitting many required rubric claims.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2611.00383 [cs.CV]
(or arXiv:2611.00383v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.00383 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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