# Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models

arXiv: https://arxiv.org/abs/2512.20557 · HTML: https://mlsanity.com/paper/2512.20557/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 14 of 14)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [π3: Permutation-Equivariant Visual Geometry Learning](https://mlsanity.com/paper/2507.13347/) (2025)
- [MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence](https://mlsanity.com/paper/2505.23764/) (2025)
- [VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction](https://mlsanity.com/paper/2505.20279/) (2025)
- [Cambrian-S: Towards Spatial Supersensing in Video](https://mlsanity.com/paper/2511.04670/) (2025)
- [OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models](https://mlsanity.com/paper/2506.03135/) (2025)
- [SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence](https://mlsanity.com/paper/2505.17012/) (2025)
- [EmbRACE-3K: Embodied Reasoning and Action in Complex Environments](https://mlsanity.com/paper/2507.10548/) (2025)

## Built on by (most cited first, 12 of 12)

- [Thinking with Geometry: Active Geometry Integration for Spatial Reasoning](https://mlsanity.com/paper/2602.06037/) (2026)
- [Make Geometry Matter for Spatial Reasoning](https://mlsanity.com/paper/2603.26639/) (2026)
- [4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding](https://mlsanity.com/paper/2605.05997/) (2026)
- [Keep It in Mind: User Centric Continual Spatial Intelligence Reasoning in Egocentric Video Streams](https://mlsanity.com/paper/2606.15200/) (2026)
- [DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs](https://mlsanity.com/paper/2607.12503/) (2026)
- [ViSTR-Bench: Can MLLMs Reason from Continuous Visual Cues in Dynamic Scenes?](https://mlsanity.com/paper/2607.20868/) (2026)
- [SpatialCORE: Confidence-Aware Grounded Spatial Reasoning in Large Vision--Language Models](https://mlsanity.com/paper/2609.38716/) (2026)
- [Soft Spatial Reasoning](https://mlsanity.com/paper/2609.38717/) (2026)
- [ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning](https://mlsanity.com/paper/2609.39665/) (2026)
- [From Reasoning Failures to Composable Video Spatial Intelligence](https://mlsanity.com/paper/2610.01999/) (2026)
- [What Makes a Good Visual Representation for Spatial Understanding in MLLMs?](https://mlsanity.com/paper/2610.17844/) (2026)
- [DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning](https://mlsanity.com/paper/2610.20598/) (2026)

## Abstract (arXiv)

Vision-language models (VLM) excel at general understanding yet remain weak at dynamic spatial reasoning (DSR), i.e., reasoning about the evolvement of object geometry and relationship in 3D space over time, largely due to the scarcity of scalable 4D-aware training resources. To bridge this gap across aspects of dataset, benchmark and model, we introduce DSR Suite. First, we propose an automated pipeline that generates multiple-choice question-answer pairs from in-the-wild videos for DSR. By leveraging modern vision foundation models, the pipeline extracts rich geometric and motion information, including camera poses, local point clouds, object masks, orientations, and 3D trajectories. These geometric cues enable the construction of DSR-Train for learning and further human-refined DSR-Bench for evaluation. Compared with previous works, our data emphasize (i) in-the-wild video sources, (ii) object- and scene-level 3D requirements, (iii) viewpoint transformations, (iv) multi-object interactions, and (v) fine-grained, procedural answers. Beyond data, we propose a lightweight Geometry Selection Module (GSM) to seamlessly integrate geometric priors into VLMs, which condenses question semantics and extracts question-relevant knowledge from pretrained 4D reconstruction priors into a compact set of geometry tokens. This targeted extraction avoids overwhelming the model with irrelevant knowledge. Experiments show that integrating DSR-Train and GSM into Qwen2.5-VL-7B significantly enhances its dynamic spatial reasoning capability, while maintaining accuracy on general video understanding benchmarks.