This paper introduces WalkRAG, a spatial Retrieval-Augmented Generation (RAG) framework that leverages Large Language Models (LLMs) to recommend personalized and walkable urban itineraries. It addresses known LLM limitations in spatial reasoning and factual accuracy by integrating spatial and contextual urban knowledge for enhanced route generation and point-of-interest information retrieval.

This paper introduces SpaceTools, a vision-language model trained with Double Interactive Reinforcement Learning (DIRL) to achieve precise spatial reasoning and real-world robot manipulation by effectively coordinating multiple external tools. It demonstrates state-of-the-art performance on various spatial understanding benchmarks.