Aug 2026· Artificial Intelligence Review· 7 citations
TL;DR
This survey provides a comprehensive and unified overview of recent advances in spatial intelligence for VLMs, summarize core concepts behind spatial reasoning in VLMs, analyze why spatial failures occur, and organize existing solutions into a clear framework spanning prompting-based techniques, model improvements, explicit 2D cues, 3D enrichment, and data-driven strategies.
Abstract
Vision-language models have achieved impressive progress, yet they still struggle with spatial intelligence–understanding where objects are, how they relate, and how space changes across viewpoints. This limitation matters for embodied AI, autonomous driving, and spatially consistent generation. Meanwhile, rapid advances in spatially enhanced VLMs have produced a scattered literature with inconsistent terminology, methods, and evaluation practices. In this survey, we provide a comprehensive and unified overview of recent advances in spatial intelligence for VLMs. We summarize core concepts behind spatial reasoning in VLMs, analyze why spatial failures occur, and organize existing solutions into a clear framework spanning prompting-based techniques, model improvements, explicit 2D cues, 3D enrichment, and data-driven strategies. We also examine how spatial ability is currently measured and report an empirical study across 37 models and 9 representative benchmarks. Our analysis highlights current best-performing approaches, clarifies when different strategies help or fail, shows the existence of performance gaps across different evaluation datasets and reveals the potential design biases in current spatial understanding benchmarks. By consolidating evidence and outlining open challenges, this survey offers a practical roadmap for building more spatially capable VLMs. We release our
evaluation code
and maintain a curated
paper repository
to support the rapidly growing research on spatial intelligence in vision-language models.
Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D prior inputs or external spatial encoders, which increase complexity and degrade the underlying VLMs'general-purpose capabilities after spatial fine-tuning. To this end, we propose a parameter-efficient \textit{\textbf{Spatio}-vision \textbf{L}anguage \textbf{M}odels (SpatioLM)}, that enhances spatial intelligence without extra 3D prior inputs or third-party spatial encoders. Concretely, we design a plug-and-play and non-invasive spatio-vision module that elicits the spatial knowledge inherent in VLMs. Furthermore, we innovatively leverage pseudo depth and camera information as supervision to guide the model in learning physically coherent representations. Extensive experiments show that SpatioLM achieves significant improvements in diverse tasks, including spatial perception and understanding while effectively limiting the degradation of general capabilities. Notably, the model achieves an impressive score of 71.6 on the VSI-Bench (the first model to surpass 70). In addition, it attains competitive performance when transferred to embodied manipulation tasks. Code is available at \href{https://github.com/xiaomi-research/spatio-lm}{\faGithub~spatio-lm}.
Jing Wu, Jianhua Wu, Jiayi Guan et al.· 0 citations
SpatialCLI is proposed, a framework that teaches VLMs to reason with spatial tools and progressively internalize the specialist perceptual capabilities they provide and introduces SpatialCLI-Bench, a 516-example benchmark for compositional perception across localization, segmentation, depth, and pose.
Yang Zhou, Zixuan Huang, Sunzhu Li et al.· arXiv.org· 0 citations
Commonsense reasoning in computer vision encompasses integrating visual data and contextual knowledge, crucial for enhancing AI's understanding of everyday scenarios. This understanding not only improves machine learning models but also enhances their ability to interact meaningfully with humans and the environment. Unlike CNN-based conventional vision models, which are designed to identify objects within a specific image, incorporating commonsense knowledge enables models to interpret scenes in a more holistic manner, thereby improving their spatial ability to reason about relationships among objects and actions. This integration not only enhances object recognition but also facilitates a deeper understanding of the contextual factors, ultimately leading to more precise predictions and interactions in real-world applications. This paper presents a comprehensive survey of recent developments that integrate commonsense knowledge into computer vision tasks. We systematically review approaches based on knowledge graphs, scene graphs, neuro-symbolic models, and commonsense-augmented transformers. We also outline current limitations related to dataset bias, knowledge incompleteness, and integration challenges. Finally, we highlight prospective research trajectories in cross-modal reasoning, scalable commonsense knowledge injection, and neuro-symbolic hybrid architectures to develop truly intelligent visual systems.
Bahar Uddin Mahmud, Sumit Barua, Guan Yue Hong et al.· 0 citations
Space Tokens is introduced, a lightweight, architecture-agnostic framework that equips VLMs with explicit continuous spatial representations without requiring additional inference-time modules, and demonstrates that continuous spatial tokens provide an effective, interpretable, and computationally efficient mechanism for integrating geometric reasoning into large vision-language models.
Hunter Schofield, Mohammed Elmahgiubi, Mohammad Mahdavian et al.· 0 citations
It is demonstrated that training models with chain-of-thought supervision over the authors' hierarchical sub-tasks, combined with reinforcement learning with verifiable rewards, significantly improves both spatial consistency across sub-tasks and target-task accuracy, supporting the value of the proposed decomposition as both a diagnostic tool and a training signal.
Patrick Rim, Tom Long, Ekta Prashnani et al.· arXiv.org· 0 citations
Vision-Language-Action (VLA) models encode visual observations as flat 2D patch tokens that carry no intrinsic geometric structure, and augmenting them with dense monocular depth injects per-pixel scalar values that encode neither surface orientation nor geometric confidence. This leaves the policy with limited structured spatial reasoning for action prediction. We propose GaussVLA, a Mamba-based VLA that incorporates two custom modules: Gaussian Spatial Tokenizer (GST) to lift frozen semantic and depth features into compact 3D Gaussian tokens, pools geometrically salient regions with learned queries, and \emph{Depth-Aware Chain-of-Thought (DA-CoT)} that performs structured, non-autoregressive geometric reasoning under language and flow-time conditioning. Across both simulation and real-world evaluations, GaussVLA demonstrates strong spatial-manipulation performance while remaining parameter-efficient. On LIBERO, it achieves 93.5% average success and 100.0% success on the Spatial suite with only 200M parameters, improving over SpatialVLA by 19.7% relative average success while remaining significantly more parameter-efficient.
MD SELIM SAROWAR, Md Tanvir Islam, Sungho Kim et al.· 0 citations
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