City Navigation in the Wild: Exploring Emergent Navigation from Web-Scale Knowledge in MLLMs

• URL: http://arxiv.org/abs/2512.15933v1
• Date: Wed, 17 Dec 2025 19:59:31 GMT
• Title: City Navigation in the Wild: Exploring Emergent Navigation from Web-Scale Knowledge in MLLMs
• Authors: Dwip Dalal, Utkarsh Mishra, Narendra Ahuja, Nebojsa Jojic,
• Abstract summary: Task is designed to evaluate sequential decision-making abilities of MLLMs in challenging, knowledge-intensive real-world environments.<n>We operationalize this task with CityNav, a benchmark encompassing four diverse global cities.<n>Agents are required to rely solely on visual inputs and internal multimodal reasoning to sequentially navigate 50+ decision points.<n>We propose Verbalization of Path (VoP), which explicitly grounds the agent's internal reasoning by probing an explicit cognitive map.
• Score: 13.863236619171174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging multimodal large language models (MLLMs) to develop embodied agents offers significant promise for addressing complex real-world tasks. However, current evaluation benchmarks remain predominantly language-centric or heavily reliant on simulated environments, rarely probing the nuanced, knowledge-intensive reasoning essential for practical, real-world scenarios. To bridge this critical gap, we introduce the task of Sparsely Grounded Visual Navigation, explicitly designed to evaluate the sequential decision-making abilities of MLLMs in challenging, knowledge-intensive real-world environments. We operationalize this task with CityNav, a comprehensive benchmark encompassing four diverse global cities, specifically constructed to assess raw MLLM-driven agents in city navigation. Agents are required to rely solely on visual inputs and internal multimodal reasoning to sequentially navigate 50+ decision points without additional environmental annotations or specialized architectural modifications. Crucially, agents must autonomously achieve localization through interpreting city-specific cues and recognizing landmarks, perform spatial reasoning, and strategically plan and execute routes to their destinations. Through extensive evaluations, we demonstrate that current state-of-the-art MLLMs and standard reasoning techniques (e.g., Chain-of-Thought, Reflection) significantly underperform in this challenging setting. To address this, we propose Verbalization of Path (VoP), which explicitly grounds the agent's internal reasoning by probing an explicit cognitive map (key landmarks and directions toward the destination) from the MLLMs, substantially enhancing navigation success. Project Webpage: https://dwipddalal.github.io/AgentNav/

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