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 a novel framework to understand strategic behavior and market impact of AI agents in labor markets, incorporating real-world economic forces such as adverse selection, moral hazard, and reputation dynamics. Through simulations, it demonstrates how LLM agents with enhanced reasoning capabilities can strategically self-improve, adapt to market changes, and reproduce classic macroeconomic phenomena while also revealing potential AI-driven economic trends.