Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information. This study examines whether choropleth maps remain useful for machine spatial understanding when models can directly process structured geodata.
arXiv:2607. 17999v1 Announce Type: new Abstract: Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information.
arXiv:2608. 15736v1 Announce Type: new Abstract: Foundation models (FMs) increasingly support multimodal and geospatial reasoning, yet it remains unclear whether cartographic principles designed for human perception are equally effective for machines.
The paper introduces GeoSpatialBench, a benchmark for evaluating spatial reasoning and constrained spatial editing in multimodal large language models. It tests 28 spatial relations across GeoJSON and map image data from six cities, revealing that models perform better on GeoJSON than images and struggle with quantitative metric relations and editing tasks that must satisfy spatial constraints. The study highlights a significant gap between recognizing spatial relations and actively manipulating them in AI systems.
arXiv:2606. 04381v1 Announce Type: cross Abstract: Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space.
Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space. Because LLMs operate on discrete tokens, they lack native support for continuous spatial representations, explicit geometric computation, and structured spatial operators.
arXiv:2609.39588v1 Announce Type: new Abstract: We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical la...
arXiv:2510.13394v4 Announce Type: replace Abstract: Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, au...
arXiv:2606. 08952v1 Announce Type: new Abstract: Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world.
SpatialBlock introduces a synthetic dataset of 15,000 block‑stacking problems designed to improve spatial intelligence in Large Vision‑Language Models (LVLMs). The dataset covers 3D‑to‑2D projection, viewpoint transformation, and structural combination, and uses controlled color modulation to encourage anchor‑based reasoning. Experiments show that LVLMs trained on SpatialBlock outperform baselines and generalize to real‑world spatial tasks, despite the dataset’s synthetic and compact nature.
The paper introduces S‑Space, a linear subspace within multimodal language models where object‑token representations encode continuous spatial coordinates along horizontal, vertical, and distance axes. This subspace is intrinsic, readable under non‑spatial queries, and serves as an internal spatial workspace: perception maps object locations into it, reasoning transforms it for viewpoint changes, and verbal responses selectively access it. Diagnostic experiments using S‑Space reveal that while models can form spatial representations, they struggle with spatial transformations needed for reliable reasoning across viewpoint changes.
arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.
Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning. Recent reinforcement learning (RL) methods aim to close this gap with verifiable outcomes, yet they suffer from poor credit assignment across intermediate reasoning steps.