title: Can Large Vision Language Models Read Maps Like a Human?
summary: In this paper, we introduce MapBench-the first dataset specifically designed
  for human-readable, pixel-based map-based outdoor navigation, curated from
  complex path finding scenarios. MapBench comprises over 1600 pixel space map
  path finding problems from 100 diverse maps. In MapBench, LVLMs generate
  language-based navigation instructions given a map image and a query with
  beginning and end landmarks. For each map, MapBench provides Map Space Scene
  Graph (MSSG) as an indexing data structure to convert between natural language
  and evaluate LVLM-generated results. We demonstrate that MapBench significantly
  challenges state-of-the-art LVLMs both zero-shot prompting and a
  Chain-of-Thought (CoT) augmented reasoning framework that decomposes map
  navigation into sequential cognitive processes. Our evaluation of both
  open-source and closed-source LVLMs underscores the substantial difficulty
  posed by MapBench, revealing critical limitations in their spatial reasoning
  and structured decision-making capabilities. We release all the code and
  dataset in https://github.com/taco-group/MapBench.
paper_authors._id: 67e192ae359a7a8b5fb32877
paper_authors.name: Shuo Xing
paper_authors.status: claimed_verified
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paper_authors.fullname: Shuo Xing
paper_authors.type: user
paper_authors.user: shuoxing
paper_authors._id: 67e192ae359a7a8b5fb32878
paper_authors.name: Zezhou Sun
paper_authors._id: 67e192ae359a7a8b5fb32879
paper_authors.name: Shuangyu Xie
paper_authors._id: 67e192ae359a7a8b5fb3287a
paper_authors.name: Kaiyuan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:18:40.366Z
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paper_authors.fullname: Kaiyuan Eric Chen
paper_authors.type: user
paper_authors.user: keplerccc
paper_authors._id: 67e192ae359a7a8b5fb3287b
paper_authors.name: Yanjia Huang
paper_authors._id: 67e192ae359a7a8b5fb3287c
paper_authors.name: Yuping Wang
paper_authors._id: 67e192ae359a7a8b5fb3287d
paper_authors.name: Jiachen Li
paper_authors._id: 67e192ae359a7a8b5fb3287e
paper_authors.name: Dezhen Song
paper_authors._id: 67e192ae359a7a8b5fb3287f
paper_authors.name: Zhengzhong Tu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:55:25.632Z
paper_authors._id: 62548d5fef3debb2ddf91217
paper_authors.avatarUrl: /avatars/14975b45568f9c399c92c3986b6ce83e.svg
paper_authors.fullname: Zhengzhong Tu
paper_authors.type: user
paper_authors.user: vztu
paper_title: Can Large Vision Language Models Read Maps Like a Human?
paper_summary: In this paper, we introduce MapBench-the first dataset specifically designed
  for human-readable, pixel-based map-based outdoor navigation, curated from
  complex path finding scenarios. MapBench comprises over 1600 pixel space map
  path finding problems from 100 diverse maps. In MapBench, LVLMs generate
  language-based navigation instructions given a map image and a query with
  beginning and end landmarks. For each map, MapBench provides Map Space Scene
  Graph (MSSG) as an indexing data structure to convert between natural language
  and evaluate LVLM-generated results. We demonstrate that MapBench significantly
  challenges state-of-the-art LVLMs both zero-shot prompting and a
  Chain-of-Thought (CoT) augmented reasoning framework that decomposes map
  navigation into sequential cognitive processes. Our evaluation of both
  open-source and closed-source LVLMs underscores the substantial difficulty
  posed by MapBench, revealing critical limitations in their spatial reasoning
  and structured decision-making capabilities. We release all the code and
  dataset in https://github.com/taco-group/MapBench.
paper_ai_summary: MapBench challenges LVLMs with pixel-based map navigation problems, highlighting limitations in spatial reasoning and decision-making.
paper_ai_keywords: LVLMs Map Space Scene Graph (MSSG) zero-shot prompting Chain-of-Thought (CoT) spatial reasoning structured decision-making