# DSI-Bench: A Benchmark for Dynamic Spatial Intelligence

arXiv: https://arxiv.org/abs/2510.18873 · HTML: https://mlsanity.com/paper/2510.18873/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence](https://mlsanity.com/paper/2505.23764/) (2025)
- [3DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark](https://mlsanity.com/paper/2412.07825/) (2024)
- [MindCube: Spatial Mental Modeling from Limited Views](https://mlsanity.com/paper/2506.21458/) (2025)
- [A Short Note on the Kinetics-700-2020 Human Action Dataset](https://mlsanity.com/paper/2010.10864/) (2020)
- [SpatialTrackerV2: 3D Point Tracking Made Easy](https://mlsanity.com/paper/2507.12462/) (2025)
- [Towards Understanding Camera Motions in Any Video](https://mlsanity.com/paper/2504.15376/) (2025)
- [VLM4D: Towards Spatiotemporal Awareness in Vision Language Models](https://mlsanity.com/paper/2508.02095/) (2025)
- [Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models](https://mlsanity.com/paper/2412.18605/) (2024)

## Built on by (most cited first, 23 of 23)

- [SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning](https://mlsanity.com/paper/2606.13673/) (2026)
- [TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers](https://mlsanity.com/paper/2601.14133/) (2026)
- [Thinking in Dynamics: How Multimodal Large Language Models Perceive, Track, and Reason Dynamics in Physical 4D World](https://mlsanity.com/paper/2603.12746/) (2026)
- [OVO-S-Bench: A Hierarchical Benchmark for Streaming Spatial Intelligence in Multimodal LLMs](https://mlsanity.com/paper/2606.03890/) (2026)
- [IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation](https://mlsanity.com/paper/2511.17384/) (2025)
- [InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning](https://mlsanity.com/paper/2606.12195/) (2026)
- [4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding](https://mlsanity.com/paper/2605.05997/) (2026)
- [Watch, Remember, Reason: Human-View Video Understanding with MLLMs](https://mlsanity.com/paper/2606.07433/) (2026)
- [Thinking in Structures: Evaluating Spatial Intelligence in Constraint-Governed Spaces](https://mlsanity.com/paper/2602.07864/) (2026)
- [Planning with the Views](https://mlsanity.com/paper/2605.29563/) (2026)
- [What if? Emulative Simulation with World Models for Situated Reasoning](https://mlsanity.com/paper/2603.06445/) (2026)
- [DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs](https://mlsanity.com/paper/2607.12503/) (2026)
- [ReSTI: A Source-Grounded Audit and Repair of STI-Bench](https://mlsanity.com/paper/2609.24727/) (2026)
- [DynaSolidGeo: A Dynamic Benchmark for Genuine Spatial Mathematical Reasoning of VLMs in Solid Geometry](https://mlsanity.com/paper/2510.22340/) (2025)
- [Modeling Cross-vision Synergy for Unified Large Vision Model](https://mlsanity.com/paper/2603.03564/) (2026)
- [ViSTR-Bench: Can MLLMs Reason from Continuous Visual Cues in Dynamic Scenes?](https://mlsanity.com/paper/2607.20868/) (2026)
- [Thinking Ahead: Foresight Intelligence in MLLMs and World Model](https://mlsanity.com/paper/2511.18735/) (2025)
- [SCP: Spatial Causal Prediction in Video](https://mlsanity.com/paper/2603.03944/) (2026)
- [From Reasoning Failures to Composable Video Spatial Intelligence](https://mlsanity.com/paper/2610.01999/) (2026)
- [SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models](https://mlsanity.com/paper/2610.12402/) (2026)
- [WOVEN: Weaving Visual World Modeling into Multimodal LLMs](https://mlsanity.com/paper/2610.12417/) (2026)
- [EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents](https://mlsanity.com/paper/2610.16679/) (2026)
- [DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning](https://mlsanity.com/paper/2610.20598/) (2026)

## Abstract (arXiv)

Reasoning about dynamic spatial relationships is essential, as both observers and objects often move simultaneously. Although vision-language models (VLMs) and visual expertise models excel in 2D tasks and static scenarios, their ability to fully understand dynamic 3D scenarios remains limited. We introduce Dynamic Spatial Intelligence and propose DSI-Bench, a benchmark with nearly 1,000 dynamic videos and over 1,700 manually annotated questions covering nine decoupled motion patterns of observers and objects. Spatially and temporally symmetric designs reduce biases and enable systematic evaluation of models' reasoning about self-motion and object motion. Our evaluation of 14 VLMs and expert models reveals key limitations: models often conflate observer and object motion, exhibit semantic biases, and fail to accurately infer relative relationships in dynamic scenarios. Our DSI-Bench provides valuable findings and insights about the future development of general and expertise models with dynamic spatial intelligence.