# Thinking in Dynamics: How Multimodal Large Language Models Perceive, Track, and Reason About Dynamic Changes in the 4D Physical World

> This article introduces a groundbreaking study accepted by CVPR 2026, which proposes the Dyn-Bench benchmark to systematically evaluate, for the first time, the ability of multimodal large language models (MLLMs) to perceive, track, and reason about spatiotemporal dynamics in the 4D physical world. It reveals key limitations of current models in dynamic scene understanding and directions for improvement.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-06T03:39:46.000Z
- 最近活动: 2026-05-06T03:48:02.731Z
- 热度: 154.9
- 关键词: 多模态大语言模型, 时空动态推理, CVPR 2026, Dyn-Bench, 物理四维世界, 视觉问答, 动态物体定位, 具身智能, 计算机视觉, 深度学习
- 页面链接: https://www.zingnex.cn/en/forum/thread/thinking-in-dynamics
- Canonical: https://www.zingnex.cn/forum/thread/thinking-in-dynamics
- Markdown 来源: floors_fallback

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## [Introduction] Research on Dynamic Scene Understanding of Multimodal Large Language Models: Dyn-Bench Benchmark and Key Findings

This article introduces a groundbreaking study accepted by CVPR 2026, which proposes the Dyn-Bench benchmark to systematically evaluate, for the first time, the ability of multimodal large language models (MLLMs) to perceive, track, and reason about spatiotemporal dynamics in the 4D physical world. It reveals key limitations of current models in dynamic scene understanding and directions for improvement.

## Research Background: The Unsolved Mystery of MLLMs' Dynamic Thinking Ability

Humans live in a 4D physical world and can understand the movement trajectories of objects, their interactions, and camera movements in dynamic scenes. Current MLLMs perform well in static visual understanding, but whether they excel at 'dynamic thinking' remains unclear—this is crucial for building embodied agents, autonomous driving systems, and robotic systems.

## Dyn-Bench: Detailed Explanation of the First Large-Scale Spatiotemporal Dynamic Reasoning Benchmark

Dyn-Bench is a large-scale benchmark for evaluating MLLMs' dynamic understanding ability, containing 1000 videos (real + synthetic), 7000 visual question-answer (VQA) pairs, and 3000 dynamic object localization pairs. It evaluates from three key dimensions:
1. Camera-Object Dimension: Understand the movement of objects relative to the camera;
2. Inter-Object Dimension: Reason about object interactions and relative dynamics;
3. Object-Scene Dimension: Analyze object-scene interactions and evolution.

## Key Findings: Common Limitations of Current MLLMs in Dynamic Understanding

Evaluations of models like GPT-4V, Gemini, and Claude 3 reveal:
1. It's hard to balance language reasoning and visual localization;
2. There are contradictions in explaining motion interactions in complex scenes;
3. Traditional prompting strategies (e.g., Chain of Thought) have limited improvement effects.

## Improvement Directions: Structured Integration Methods

Promising improvement directions include:
1. Mask-Guided Fusion: Incorporate object segmentation masks into reasoning to enhance dynamic object tracking ability;
2. Spatiotemporal Textual Cognitive Map (ST-TCM): Construct structured spatiotemporal relationship representations to simulate human spatiotemporal reasoning processes.

## Research Significance: Implications for Embodied Intelligence and Autonomous Driving, and Open-Source Contributions

Research Significance:
- Embodied Intelligence: Provides tools for evaluating and improving perceptual foundations;
- Autonomous Driving: Offers references for the design of perception systems.
Open-Source Contributions: HuggingFace dataset `kairunwen/DynamicVerse`, evaluation code, a framework supporting over 20 MLLMs, and an experimental leaderboard.

## Technical Details: Evaluation Metrics and Supported Model Range

Evaluation Metrics:
- QA Accuracy: Measures the matching degree of answers in VQA tasks;
- Mask J&F Score: Combines IoU and boundary F-measure to evaluate localization accuracy.
Supported Models: Covers over 20 mainstream MLLMs such as Sa2VA series, InternVL3/3.5, Qwen2.5-VL, LLaVA-OneVision, etc.