MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos (2506.04141v1)

Abstract: The sequential structure of videos poses a challenge to the ability of multimodal LLMs (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. The benchmark is characterized by the following features. (1) Long-range, multi-frame reasoning: Models are required to infer and analyze evidence frames that may be far from the question frame. (2) Beyond perception: Questions cannot be answered through direct perception alone but require reasoning over hidden information. (3) Reliability: All tasks are manually annotated, referencing extensive real-world user understanding to align with common perceptions. (4) Confusability: Carefully designed distractor annotation strategies to reduce model shortcuts. MMR-V consists of 317 videos and 1,257 tasks. Our experiments reveal that current models still struggle with multi-modal reasoning; even the best-performing model, o4-mini, achieves only 52.5% accuracy. Additionally, current reasoning enhancement strategies (Chain-of-Thought and scaling test-time compute) bring limited gains. Further analysis indicates that the CoT demanded for multi-modal reasoning differs from it in textual reasoning, which partly explains the limited performance gains. We hope that MMR-V can inspire further research into enhancing multi-modal reasoning capabilities.

Overview of the MMR-V Benchmark for Multimodal Deep Reasoning in Videos

The paper "MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos" introduces a novel benchmark designed to assess the deep reasoning capabilities of multimodal LLMs (MLLMs) in video contexts. The unique challenges that video content poses, such as the need for sequential reasoning across multiple frames and the integration of multimodal information, are central to this research. This benchmark addresses the gaps in existing video benchmarks, which primarily focus on perception and understanding tasks, by requiring models to engage in long-range, multi-frame reasoning and to extract and interpret hidden information in videos.

Key Features of MMR-V

MMR-V is characterized by several key features that differentiate it from previous benchmarks:

1. Long-range, Multi-frame Reasoning: The benchmark involves tasks that require models to infer and analyze evidence scattered across non-adjacent video frames, thus testing their ability to perform deep multimodal reasoning.
2. Beyond Perception: The questions designed for this benchmark cannot be answered through direct perception alone but require reasoning beyond surface-level visual cues, aiming to uncover hidden or implied information.
3. Reliability and Confusability: All tasks in MMR-V are meticulously manually annotated with reference to real-world user understanding, ensuring alignment with common perceptions. Distractor strategies are carefully crafted to minimize model shortcuts and increase challenge.

MMR-V encompasses 317 videos and 1,257 tasks, providing a diverse platform for evaluating multimodal reasoning. The benchmark categorizes tasks into implicit reasoning, which involves extracting underlying implications, and explicit reasoning, focusing on perceivable information and detailed analysis.

Experimental Findings

The research evaluates a range of proprietary and open-source models using the MMR-V benchmark. Results indicate that current models struggle significantly with multimodal reasoning in videos. Even the best-performing model, o4-mini, achieved only a 52.5% accuracy rate. This highlights the substantial challenge that MMR-V poses for existing models.

• Influence of Reasoning Enhancement Strategies: Strategies such as Chain-of-Thought (CoT) prompting and scaling test-time compute, despite their proven efficacy in textual reasoning, deliver limited improvements on MMR-V. This suggests fundamental differences in reasoning processes required for multimodal tasks compared to those centered on text alone.
• Modality Integration: Models capable of processing audio alongside video showed improved performance, which underscores the potential benefits of more comprehensive multimodal reasoning approaches.
• Human-Model Gap: Human performance significantly outpaced model results, achieving 86% accuracy, underscoring the gap in current AI's ability to emulate nuanced human-like reasoning, particularly for complex video reasoning tasks.

Implications and Future Directions

The introduction of MMR-V provides a new, rigorous benchmark for advancing multimodal reasoning in AI systems. It poses significant challenges that highlight the limitations of current models, paving the way for future research to explore new architectures and methods that can integrate reasoning across various modalities more effectively.

The findings from this benchmark underscore the need for approaches that extend beyond text-based reasoning and incorporate deeper visual paradigms and analytical processes. Exploring improvements in multimodal context understanding, such as employing enhanced tools and reasoning frameworks, could bridge the gap observed between AI models and human-like reasoning capabilities.

MMR-V is expected to inspire further research into enhancing the reasoning capabilities of MLLMs and to contribute to the development of AI systems capable of interacting with complex, real-world video data, advancing applications in fields such as embodied intelligence and intelligent security monitoring.