Introduction
Humans naturally excel at spatial reasoning by integrating multimodal sensory inputs such as vision and sound to understand complex environments and spatial relationships. Recent breakthroughs in artificial intelligence, particularly with large multimodal reasoning models, have begun to approximate this ability to perceive, interpret, and reason across diverse spatial tasks.
However, systematic reviews of such models remain sparse, and publicly available benchmarks to evaluate them comprehensively are just emerging. The recent survey by Zheng et al. (https://arxiv.org/abs/2510.25760) fills this gap by offering a thorough overview of multimodal spatial reasoning challenges, surveying architectures, methodologies, post-training strategies, explainability approaches, and evaluating a wide spectrum of 2D and 3D spatial understanding tasks. Complementing this, other works delve into spatial reasoning in language models Feng et al., 2025, geometric reasoning in visual language models Kazemi et al., 2023, and multimodal fusion involving audio and egocentric video Yang et al., 2024.
Foundations of Multimodal Spatial Reasoning
Spatial reasoning synthesizes sensory inputs to deduce the layout, location, relationships, and attributes of objects within environments. Classical AI approaches handled these tasks separately in 2D or 3D space, mainly relying on vision or geometric sensors.
Multimodal spatial reasoning leverages the fusion of multiple data streams—visual images, natural language, audio signals, egocentric videos, and 3D point clouds—to create richer, more accurate spatial representations. Large multimodal models, typically based on transformer architectures, jointly encode various modalities enabling sophisticated reasoning. This integration supports tasks spanning from spatial question answering and layout understanding to robotics and embodied navigational planning Xu et al., 2025.
Key Categories of Multimodal Spatial Reasoning
Post-Training and Architectural Innovations
Improving pretrained large models through post-training techniques such as instruction tuning, few-shot learning, and modular fine-tuning enhances their spatial reasoning abilities and generalization. Simultaneously, explainability methods provide insights into the models’ grounding of spatial concepts, improving interpretability. Architectures frequently combine convolutional and vision transformer backbones for encoding visual data, alongside large language model cores for semantic reasoning Xu et al., 2025.
Classic and 3D Spatial Tasks
In addition to established 2D visual tasks like object detection and semantic segmentation, modern approaches incorporate 3D reasoning using depth, LiDAR, or RGB-D sensors to reconstruct and understand spatial layouts and object configurations in volumetric spaces. Visual question answering (VQA) benchmarks now increasingly target spatial and relational understanding within 3D scenes Feng et al., 2025.
Spatial Relationship Reasoning and Scene Understanding
Reasoning about object relations (such as support, containment, adjacency) underpins scene understanding. Large multimodal models use joint embeddings of vision and language modalities to accurately infer relational contexts and produce descriptive scene layouts Kazemi et al., 2023.
Embodied AI and Vision-Language Navigation
Embodied intelligence systems operate within environments to perform tasks like vision-language navigation (VLN), where agents follow natural language instructions and interpret visual cues to navigate. Multimodal large models enable these agents to understand instructions, perceive their surroundings, and decide dynamic actions effectively Xu et al., 2025.
Emerging Modalities: Audio and Egocentric Video
Cutting-edge research integrates spatialized audio and egocentric (first-person) video, expanding the spatial reasoning ability beyond static visual scenes to encompass temporal, occluded, and multisensory perspectives, essential for applications in robotics, surveillance, and augmented reality Yang et al., 2024.
Open Benchmarks for Evaluation
Zheng et al. compile and introduce several benchmarks to systematically evaluate multimodal spatial reasoning capabilities:
- Visual Spatial Reasoning Tasks: Enhanced VQA datasets specifically focusing on spatial queries and relational understanding.
- 3D Scene Grounding: Tasks requiring textual grounding to spatially localized 3D objects or regions.
- Embodied Navigation: Simulated VLN environments to assess navigation efficacy based on spatial instruction comprehension.
- Multimodal Audio-Visual Reasoning: Datasets combining audio and visual inputs for spatial source localization and reasoning.
- Egocentric Video Reasoning: First-person perspective datasets for temporal and spatial environment understanding Xu et al., 2025.
These benchmarks promote consistent evaluation, data diversity, and allow comparative assessment across methods.
Synthesis of Advances and Persistent Challenges
The surveyed literature highlights remarkable advances in multimodal spatial reasoning:
- Architectural integration of visual and language modules has improved joint spatial understanding.
- Post-training schemes boost zero/few-shot performance across emergent tasks.
- Embodied AI benefits from large multimodal models enabling complex navigation and interaction behaviors.
However, significant challenges persist:
- Fine-grained 3D and dynamic scene reasoning remains limited, requiring richer geometric encoding.
- Efficient, uniformly scalable fusion of heterogeneous modalities is an open research challenge.
- Explainability and interpretability tools for spatial inferences lag behind model capabilities.
- Resource-intensive architectures hinder real-time or edge-device applicability.
- Dataset biases and scarcity of real-world complexity reduce generalization potential Feng et al., 2025; Kazemi et al., 2023.
Future Directions
Future research aims to expand multimodal spatial reasoning through:
- Unified models jointly trained on vision, language, audio, and 3D geometry for holistic cognition.
- Lifelong adaptive learning enabling spatial knowledge accumulation through interactions.
- Incorporation of spatial memory modules for sustained contextual awareness.
- Innovations in model efficiency, compression, and distillation for practical deployment.
- Extending evaluation benchmarks and datasets to mirror real-world variations and noise Yang et al., 2024; Xu et al., 2025.
Conclusion
The era of large multimodal models ushers in a profound leap in spatial reasoning capabilities. By conjoining sensory channels and language understanding, these models approach human-like spatial cognition across diverse challenging tasks. The comprehensive survey by Zheng et al. establishes a foundational framework, elucidates state-of-the-art methods, and offers benchmark resources critical for continued progress.
Interdisciplinary collaboration spanning AI architectures, cognitive science, and domain-specific applications is paramount to transcend existing limitations and fully realize multimodal spatial reasoning in real-world, dynamic environments.
For ongoing updates, datasets, and code associated with this survey and benchmarks, visit the official repository at https://github.com/zhengxuJosh/Awesome-Spatial-Reasoning.