# MetaphorVU: Towards Metaphorical Video Understanding

arXiv: https://arxiv.org/abs/2605.25461 · HTML: https://mlsanity.com/paper/2605.25461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 92 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 30 of 33)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2503.06749/) (2025)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://mlsanity.com/paper/2509.23661/) (2025)
- [Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos](https://mlsanity.com/paper/2501.13826/) (2025)
- [VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning](https://mlsanity.com/paper/2505.12434/) (2025)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization](https://mlsanity.com/paper/2410.08815/) (2024)
- [VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos](https://mlsanity.com/paper/2506.10857/) (2025)
- [On the Evaluation of Machine-Generated Reports](https://mlsanity.com/paper/2405.00982/) (2024)
- [MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos](https://mlsanity.com/paper/2506.04141/) (2025)
- [Understanding Figurative Meaning through Explainable Visual Entailment](https://mlsanity.com/paper/2405.01474/) (2024)
- [SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models](https://mlsanity.com/paper/2505.12589/) (2025)
- [ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models](https://mlsanity.com/paper/2507.09876/) (2025)
- [Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models](https://mlsanity.com/paper/2209.08141/) (2022)
- [Multi-Granular Multimodal Clue Fusion for Meme Understanding](https://mlsanity.com/paper/2503.12560/) (2025)
- [AdsQA: Towards Advertisement Video Understanding](https://mlsanity.com/paper/2509.08621/) (2025)
- [Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors](https://mlsanity.com/paper/2506.06987/) (2025)
- [DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://mlsanity.com/paper/2502.20730/) (2025)
- [VideoAds for Fast-Paced Video Understanding](https://mlsanity.com/paper/2504.09282/) (2025)
- [Unveiling the Invisible: Captioning Videos with Metaphors](https://mlsanity.com/paper/2406.04886/) (2024)
- [Frame Sampling Strategies Matter: A Benchmark for small vision language models](https://mlsanity.com/paper/2509.14769/) (2025)
- [Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge](https://mlsanity.com/paper/2506.15504/) (2025)
- [Can Large Vision-Language Models Understand Multimodal Sarcasm?](https://mlsanity.com/paper/2508.03654/) (2025)
- [Self-Retrieval: End-to-End Information Retrieval with One Large Language Model](https://mlsanity.com/paper/2403.00801/) (2024)
- [Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models](https://mlsanity.com/paper/2403.09750/) (2024)
- [Ring That Bell: A Corpus and Method for Multimodal Metaphor Detection in Videos](https://mlsanity.com/paper/2301.01134/) (2022)
- [Concept Drift Guided LayerNorm Tuning for Efficient Multimodal Metaphor Identification](https://mlsanity.com/paper/2505.11237/) (2025)
- [Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning](https://mlsanity.com/paper/2506.07811/) (2025)

## Abstract (arXiv)

Metaphorical videos are prevalent across various real-world scenarios to convey complex ideas, and understanding them typically requires high-order cognitive capabilities. The lack of systematic studies on metaphorical video understanding not only constrains the real-world applicability of MLLMs but also impedes the thorough assessment of their high-order cognitive capabilities. To bridge this gap, we propose MetaphorVU-Bench, the first systematic and comprehensive benchmark dedicated to metaphorical video understanding. Through experiments, we find current MLLMs struggle with accurate metaphorical video understanding, lagging far behind human level, primarily due to defective cross-domain mapping. Motivated by this finding, we construct a metaphor knowledge graph as mapping augmentation and propose MetaphorBoost, an inference-time enhancement framework achieving consistent performance improvement. Our benchmark, analysis, and method provide useful insights and a foundation for future research on advancing MLLMs.