# InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Models

arXiv: https://arxiv.org/abs/2506.18385 · HTML: https://mlsanity.com/paper/2506.18385/

## Key facts

- Cited by 30 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 49 references that are in the map.
- First posted on arXiv 23 Jun 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)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Are We on the Right Way for Evaluating Large Vision-Language Models?](https://mlsanity.com/paper/2403.20330/) (2024)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language](https://mlsanity.com/paper/2305.05662/) (2023)

## Built on by (most cited first, 30 of 30)

- [Visual Spatial Tuning](https://mlsanity.com/paper/2511.05491/) (2025)
- [Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning](https://mlsanity.com/paper/2510.27606/) (2025)
- [Holistic Evaluation of Multimodal LLMs on Spatial Intelligence](https://mlsanity.com/paper/2508.13142/) (2025)
- [Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning](https://mlsanity.com/paper/2510.11027/) (2025)
- [SpaceVista: All-Scale Visual Spatial Reasoning from mm to km](https://mlsanity.com/paper/2510.09606/) (2025)
- [Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?](https://mlsanity.com/paper/2602.07055/) (2026)
- [Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence](https://mlsanity.com/paper/2511.00108/) (2025)
- [Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training](https://mlsanity.com/paper/2603.12255/) (2026)
- [Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning](https://mlsanity.com/paper/2605.02913/) (2026)
- [Visually Prompted Benchmarks Are Surprisingly Fragile](https://mlsanity.com/paper/2512.17875/) (2025)
- [ACE-Brain-0: Spatial Intelligence as a Shared Scaffold for Universal Embodiments](https://mlsanity.com/paper/2603.03198/) (2026)
- [ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation](https://mlsanity.com/paper/2603.11421/) (2026)
- [MonoSR: Open-Vocabulary Spatial Reasoning from Monocular Images](https://mlsanity.com/paper/2511.19119/) (2025)
- [HiSpatial: Taming Hierarchical 3D Spatial Understanding in Vision-Language Models](https://mlsanity.com/paper/2603.25411/) (2026)
- [MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs](https://mlsanity.com/paper/2607.08970/) (2026)
- [Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization](https://mlsanity.com/paper/2511.16602/) (2025)
- [Spatial4D-Bench: A Versatile 4D Spatial Intelligence Benchmark](https://mlsanity.com/paper/2601.00092/) (2025)
- [VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations](https://mlsanity.com/paper/2603.16506/) (2026)
- [AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models](https://mlsanity.com/paper/2606.08952/) (2026)
- [Transferability Between Understanding and Generation in Unified Multimodal Models](https://mlsanity.com/paper/2607.04423/) (2026)
- [Stepping VLMs onto the Court: Benchmarking Spatial Intelligence in Sports](https://mlsanity.com/paper/2603.09896/) (2026)
- [SpaceDG: Benchmarking Spatial Intelligence under Visual Degradation](https://mlsanity.com/paper/2605.22536/) (2026)
- [A Multi-Modal Neuro-Symbolic Approach for Spatial Reasoning-Based Visual Grounding in Robotics](https://mlsanity.com/paper/2510.27033/) (2025)
- [SCP: Spatial Causal Prediction in Video](https://mlsanity.com/paper/2603.03944/) (2026)
- [SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images](https://mlsanity.com/paper/2605.11462/) (2026)
- [PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models](https://mlsanity.com/paper/2608.14741/) (2026)
- [SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes](https://mlsanity.com/paper/2609.16233/) (2026)
- [Evaluating the Semantic-to-Geometric Gap in Adversarial Defenses Against Vision-Language Model-Based Plagiarism](https://mlsanity.com/paper/2609.26733/) (2026)
- [SpatialASM: Internalizing Active Mentalization for Spatial Reasoning](https://mlsanity.com/paper/2610.13832/) (2026)
- [Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2610.19108/) (2026)

## Abstract (arXiv)

Recent benchmarks and datasets have been proposed to improve spatial reasoning in vision-language models (VLMs), yet existing open resources remain limited in scale, visual diversity, and instruction expressiveness. In this work, we introduce InternSpatial, the largest open-source dataset for spatial reasoning in VLMs, along with InternSpatial-Bench, a corresponding evaluation benchmark designed to assess spatial understanding under diverse instruction formats. InternSpatial comprises 12 million QA pairs spanning both single-view and multi-view settings, drawn from diverse visual environments and supporting 19 instruction formats that reflect varied query styles. For evaluation, we propose InternSpatial-Bench for single-view tasks and expand multi-view reasoning by introducing a novel rotation angle prediction task that has not been explored in prior work. Experimental results show that models trained on InternSpatial achieve 12.1% improvement on InternSpatial-Bench and 10.7% on VSI-Bench, while maintaining strong performance on general-purpose benchmarks. We hope these resources will support the development of spatially capable VLMs in practical applications such as robotics and embodied AI.