arXiv Computation and Language November 4, 2026 By Shaden Shaar, Bradon Thymes, Majd Aldaye, Claire Cardie, Bharath Hariharan

LF-VideoQA: Evaluating Long-Video Understanding with Long-Form Theory-of-Mind Questions

Read the original on arXiv Computation and Language →

LF-VideoQA is a new dataset for long‑form, open‑ended question answering over narrative videos, built around theory‑of‑mind questions that require reasoning across scenes averaging 23 minutes. It contains over 6,300 question‑answer‑rubric triples generated automatically from annotated screenplays and validated by humans. The dataset challenges current vision‑language models, showing that even oracle models fall short and that visual evidence is essential for answering these questions.

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