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README.md

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 ## 👀 MMR-V Overview
-The sequential structure of videos poses a challenge to the ability of multimodal large language models (MLLMs) to 🕵️locate multi-frame evidence and conduct multimodal reasoning. However, existing video benchmarks mainly focus on understanding tasks, which only require models to match frames mentioned in the question (hereafter referred to as ``question frame'') and perceive a few adjacent frames. To address this gap, we propose **MMR-V: A Benchmark for Multimodal Deep Reasoning in Videos**, which is characterized by the following features. 
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-* *Long-range, multi-frame reasoning*: Models are required to infer and analyze evidence frames that may be far from the question frame. 
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-* *Beyond perception*: Questions cannot be answered through direct perception alone but require reasoning over hidden information. 
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-* *Reliability*: All tasks are manually annotated, referencing extensive real-world user understanding to align with common perceptions. 
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-* *Confusability*: Carefully designed distractor annotation strategies to reduce model shortcuts. 
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-MMR-V consists of **317** videos and **1,257** tasks. Models like o3 and o4-mini have achieved impressive results on image reasoning tasks by leveraging tool use to enable 🕵️evidence mining on images. Similarly, tasks in MMR-V require models to perform in-depth reasoning and analysis over visual information from different frames of a video, challenging their ability to 🕵️**mine evidence across long-range multi-frame**.
+The sequential structure of videos poses a challenge to the ability of multimodal large language models (MLLMs) to 🕵️locate multi-frame evidence and conduct multimodal reasoning. However, existing video benchmarks mainly focus on understanding tasks, which only require models to match frames mentioned in the question (hereafter referred to as ``question frame'') and perceive a few adjacent frames. To address this gap, we propose **MMR-V: A Benchmark for Multimodal Deep Reasoning in Videos**. MMR-V consists of **317** videos and **1,257** tasks. Models like o3 and o4-mini have achieved impressive results on image reasoning tasks by leveraging tool use to enable 🕵️evidence mining on images. Similarly, tasks in MMR-V require models to perform in-depth reasoning and analysis over visual information from different frames of a video, challenging their ability to 🕵️**mine evidence across long-range multi-frame**.
 
 ## 🎬 MMR-V Task Examples