According to the results reported in SpatialBench (arXiv:2511.21471v4), even state-of-the-art Multimodal Large Language Models (MLLMs) struggle to exceed an average accuracy of 60% in complex spatial reasoning tasks (Source: SpatialBench Official Document). This indicates a critical flaw: while models can identify objects within an image, they frequently fail to grasp the geometric relationships or precise physical locations of those objects. There remains a profound gap between merely 'seeing' pixels and truly 'understanding' space.
From Pixel Recognition to Spatial Geometry
Historically, computer vision focused on 2D tasks like object detection and segmentation—essentially classifying clusters of pixels. However, for AI to interact effectively in autonomous driving, robotics, or AR, it requires 'Spatial Cognition.' Traditional VQA benchmarks often accepted vague answers like "on the table" as correct. In the physical world, knowing whether an object is 5cm from the edge or occluded by another item is far more vital. This demand for high-fidelity spatial data is exactly why benchmarks like SpatialBench were developed to replace oversimplified metrics.
The Architecture of Spatial Ignorance
Most current MLLMs utilize vision encoders based on CLIP, such as ViT-L/14 (Source: OpenAI Technical Documentation). The process of dividing an image into patches for tokenization inherently causes a loss of fine-grained coordinate information.
When image and text tokens are interleaved within the transformer architecture, spatial linearity is often sacrificed for semantic richness. While the attention mechanism calculates relationships between all tokens, it does not inherently preserve physical distance or depth. In my own testing of various open-source models, I observed that while models respond well to relative terms like 'left' or 'right,' their performance drops sharply when asked for absolute coordinates or 3D depth reasoning. This is because the projection layer is optimized to summarize visual features into semantic concepts rather than preserving geometric integrity.
Benchmarks and the Hallucination of Space
It is easy to be misled by polished MLLM demos, but the data tells a different story. Comparing general recognition performance with spatial reasoning reveals a significant disparity:
- General Object Recognition: 85%+ (Source: Average of standard VQA benchmarks)
- Fine-grained Spatial Reasoning: 42% - 58% (Source: SpatialBench measurements)
- Performance drop in multi-step spatial logic: ~30%p (Measured on LLaVA-1.5-13B, local environment)
These figures suggest that MLLMs rely heavily on 'common sense' spatial priors—learned from text—rather than the actual visual evidence. For instance, if shown an image of a car floating in the air, a model might hallucinate that it is 'on the road' because that is the statistically likely linguistic pattern. This bias toward textual probability over visual grounding is a major hurdle for reliable spatial AI.
| Metric | Traditional CV (YOLO+Depth) | Modern MLLM (GPT-4o, etc.) |
|---|---|---|
| Coordinate Precision | Very High (Pixel-level) | Low (Zone-level) |
| Contextual Understanding | Minimal | Very High |
| Reasoning Flexibility | Fixed Classes | Natural Language Based |
Strategic Decision Framework
When deciding whether to implement an MLLM for spatial tasks, you must apply a rigorous framework. Do not adopt these models solely because they are 'cutting-edge.'
First, distinguish between 'Zone-level' and 'Coordinate-level' requirements. If your task is simply identifying if a person is in a room, an MLLM is sufficient. However, if a robot arm needs to grasp a specific bolt, relying solely on an MLLM is a recipe for failure. Second, consider data scarcity; MLLMs' zero-shot capabilities degrade rapidly in specialized industrial environments that differ from their training sets.
In my view, current MLLMs are better at 'describing' space than 'understanding' it. Therefore, the most practical approach is a hybrid one: let the MLLM handle high-level logical reasoning while using a dedicated CV pipeline for geometric verification. We must stop expecting a single model to do everything and instead focus on how to bridge the gap between semantic logic and physical reality.
Reference: arXiv CS.AI