The paper introduces a network‑based spatial context retrieval pipeline that uses pedestrian street networks and open data (OpenStreetMap, GHS‑POP) to generate compact spatial briefs for open‑weight large language models. It then builds a faithfulness benchmark that labels each model claim by its source—whether grounded in the brief or drawn from training knowledge—and tests models against planted false premises across multiple cities and model configurations. The study finds that model family and generation influence resistance to false premises more than model size, revealing dimensions of spatial reasoning not captured by traditional correctness metrics.
arXiv:2508.04080v2 Announce Type: replace Abstract: Standard large language model prompting treats geospatial inference as independent, instance-wise prediction, ignoring the fundamental spatial depe...
arXiv:2608. 03882v1 Announce Type: cross Abstract: Geospatial reasoning, i.
MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.
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: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.
arXiv:2606. 15055v1 Announce Type: cross Abstract: Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making.
GeoRisk-RAG is a hierarchy‑aware framework that improves the reliability of Retrieval‑Augmented Generation (RAG) by incorporating geographic validity. It estimates geographic applicability using a Directed Acyclic Graph (DAG)‑based distance during context retrieval, enabling selective answering. Experiments on a wildfire‑related QA dataset show that GeoRisk‑RAG reduces false confidence rates from ~0.090 to 0.009 and aligns better with human preferences.
arXiv:2608. 07353v1 Announce Type: cross Abstract: Understanding concepts is fundamental to generalization.
Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone.
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.
arXiv:2606. 11719v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs).
arXiv:2505.11239v4 Announce Type: replace Abstract: Understanding human mobility through Point-of-Interest (POI) trajectory modeling is increasingly important for applications such as urban planning,...