# SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition

arXiv: https://arxiv.org/abs/2511.21471 · HTML: https://mlsanity.com/paper/2511.21471/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 67 of its 81 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Placed in

- 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, 30 of 67)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [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)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [Training Diffusion Models with Reinforcement Learning](https://mlsanity.com/paper/2305.13301/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)

## Built on by (most cited first, 30 of 35)

- [SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning](https://mlsanity.com/paper/2606.13673/) (2026)
- [SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks](https://mlsanity.com/paper/2606.09669/) (2026)
- [Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding](https://mlsanity.com/paper/2606.10738/) (2026)
- [CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation](https://mlsanity.com/paper/2602.18424/) (2026)
- [OVO-S-Bench: A Hierarchical Benchmark for Streaming Spatial Intelligence in Multimodal LLMs](https://mlsanity.com/paper/2606.03890/) (2026)
- [VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents](https://mlsanity.com/paper/2512.24851/) (2025)
- [SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments](https://mlsanity.com/paper/2604.14144/) (2026)
- [TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning](https://mlsanity.com/paper/2601.16520/) (2026)
- [GPSBench: Do Large Language Models Understand GPS Coordinates?](https://mlsanity.com/paper/2602.16105/) (2026)
- [See, Remember, Explore: A Benchmark and Baselines for Streaming Spatial Reasoning](https://mlsanity.com/paper/2603.23864/) (2026)
- [CRAFT: Grounded Multi-Agent Coordination Under Partial Information](https://mlsanity.com/paper/2603.25268/) (2026)
- [SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning](https://mlsanity.com/paper/2603.28730/) (2026)
- [CVT-Bench: Probing Spatial-State Integrity through Counterfactual Viewpoint Transformations](https://mlsanity.com/paper/2603.21114/) (2026)
- [3D Primitives are a Spatial Language for VLMs](https://mlsanity.com/paper/2605.12586/) (2026)
- [Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?](https://mlsanity.com/paper/2605.30557/) (2026)
- [Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text](https://mlsanity.com/paper/2607.21072/) (2026)
- [Predicting Camera Pose from Perspective Descriptions for Spatial Reasoning](https://mlsanity.com/paper/2602.06041/) (2026)
- [From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning](https://mlsanity.com/paper/2603.26839/) (2026)
- [Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models](https://mlsanity.com/paper/2605.07148/) (2026)
- [Visual Credit Audit for Multimodal Spatial Reasoning](https://mlsanity.com/paper/2607.27069/) (2026)
- [CoMa: Contextual Massing Generation with Vision-Language Models](https://mlsanity.com/paper/2601.08464/) (2026)
- [SPATIALALIGN: Aligning Dynamic Spatial Relationships in Video Generation](https://mlsanity.com/paper/2602.22745/) (2026)
- [SGR3 Model: Scene Graph Retrieval-Reasoning Model in 3D](https://mlsanity.com/paper/2603.04614/) (2026)
- [Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning](https://mlsanity.com/paper/2604.01764/) (2026)
- [TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables](https://mlsanity.com/paper/2604.03660/) (2026)
- [ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search](https://mlsanity.com/paper/2604.12762/) (2026)
- [SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems](https://mlsanity.com/paper/2604.16022/) (2026)
- [Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT](https://mlsanity.com/paper/2605.09719/) (2026)
- [Brick-Composer: Using MLLMs for Assembly with Diverse Bricks](https://mlsanity.com/paper/2606.05445/) (2026)
- [OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning](https://mlsanity.com/paper/2606.30378/) (2026)

## Abstract (arXiv)

Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal large language models (MLLMs) have made significant strides, existing benchmarks often oversimplify spatial cognition, reducing it to a single-dimensional metric, which fails to capture the hierarchical structure and interdependence of spatial abilities. To address this gap, we propose a hierarchical spatial cognition framework that decomposes spatial intelligence into five progressively complex levels from basic observation to high-level planning. Building upon this taxonomy, we construct SpatialBench, a large-scale, fine-grained benchmark covering 15 tasks aligned with these cognitive levels. To provide a unified evaluation across heterogeneous tasks, we further introduce a high-level capability-oriented metric that reliably assesses a model's overall spatial reasoning ability. Extensive experiments over massive MLLMs reveal distinct performance stratification across cognitive levels: models exhibit strong perceptual grounding yet remain limited in symbolic reasoning, causal inference, and planning. Additional human tests demonstrate that humans perform selective, goal-directed abstraction, while MLLMs tend to over-attend to surface details without coherent spatial intent. Our work establishes the first systematic framework for measuring hierarchical spatial cognition in MLLMs, laying the foundation for future spatially intelligent systems.