MovieCORE: COgnitive REasoning in Movies
This paper introduces MovieCORE, a novel video question answering (VQA)
dataset designed to probe deeper cognitive understanding of movie content.
Unlike existing datasets that focus on surface-level comprehension, MovieCORE
emphasizes questions that engage System-2 thinking while remaining specific to
the video material. We present an innovative agentic brainstorming approach,
utilizing multiple large language models (LLMs) as thought agents to generate
and refine high-quality question-answer pairs. To evaluate dataset quality, we
develop a set of cognitive tests assessing depth, thought-provocation
potential, and syntactic complexity. We also propose a comprehensive evaluation
scheme for assessing VQA model performance on deeper cognitive tasks. To
address the limitations of existing video-language models (VLMs), we introduce
an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves
model reasoning capabilities post-training by up to 25%. Our work contributes
to advancing movie understanding in AI systems and provides valuable insights
into the capabilities and limitations of current VQA models when faced with
more challenging, nuanced questions about cinematic content. Our project page,
dataset and code can be found at
https://joslefaure.github.io/assets/html/moviecore.html.
68ae81ae364411bea07df810
Gueter Josmy Faure
claimed_verified
2025-09-03T19:26:05.475Z
64730cd36a972f252de08495
/avatars/fa3f7eb31eb7374364ebba248f8573b7.svg
Gueter Josmy Faure
user
Joslefaure
68ae81ae364411bea07df811
Min-Hung Chen
claimed_verified
2025-08-27T07:10:21.219Z
64ae22dd1aee69ece065cdcd
https://cdn-avatars.huggingface.co/v1/production/uploads/64ae22dd1aee69ece065cdcd/JG7QaHIrr4i2k4uwR4pZK.png
Min-Hung Chen
user
cmhungsteve
68ae81ae364411bea07df812
Jia-Fong Yeh
68ae81ae364411bea07df813
Ying Cheng
68ae81ae364411bea07df814
Hung-Ting Su
68ae81ae364411bea07df815
Yung-Hao Tang
68ae81ae364411bea07df816
Shang-Hong Lai
68ae81ae364411bea07df817
Winston H. Hsu
MovieCORE: COgnitive REasoning in Movies
This paper introduces MovieCORE, a novel video question answering (VQA)
dataset designed to probe deeper cognitive understanding of movie content.
Unlike existing datasets that focus on surface-level comprehension, MovieCORE
emphasizes questions that engage System-2 thinking while remaining specific to
the video material. We present an innovative agentic brainstorming approach,
utilizing multiple large language models (LLMs) as thought agents to generate
and refine high-quality question-answer pairs. To evaluate dataset quality, we
develop a set of cognitive tests assessing depth, thought-provocation
potential, and syntactic complexity. We also propose a comprehensive evaluation
scheme for assessing VQA model performance on deeper cognitive tasks. To
address the limitations of existing video-language models (VLMs), we introduce
an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves
model reasoning capabilities post-training by up to 25%. Our work contributes
to advancing movie understanding in AI systems and provides valuable insights
into the capabilities and limitations of current VQA models when faced with
more challenging, nuanced questions about cinematic content. Our project page,
dataset and code can be found at
https://joslefaure.github.io/assets/html/moviecore.html.
MovieCORE is a video question answering dataset that uses multiple large language models to generate deep cognitive questions, and introduces an agentic enhancement module to improve VQA model performance.
video question answering VQA dataset System-2 thinking large language models LLMs thought agents cognitive tests syntactic complexity video-language models VLMs agentic enhancement module Agentic Choice Enhancement ACE