[2603.26839] From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning

Computer Science > Machine Learning arXiv:2603.26839 (cs) [Submitted on 27 Mar 2026] Title:From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning Authors:Alberto G. Rodriguez Salgado View a PDF of the paper titled From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning, by Alberto G. Rodriguez Salgado View PDF HTML (experimental) Abstract:How do multimodal models solve visual spatial tasks -- through genuine planning, or through brute-force search in token space? We introduce \textsc{MazeBench}, a benchmark of 110 procedurally generated maze images across nine controlled groups, and evaluate 16 model configurations from OpenAI, Anthropic, Google, and Alibaba. GPT-5.4 solves 91\% and Gemini 3.1 Pro 79\%, but these scores are misleading: models typically translate images into text grids and then enumerate paths step by step, consuming 1,710--22,818 tokens per solve for a task humans do quickly. Without added reasoning budgets, all configurations score only 2--12\%; on 20$\times$20 ultra-hard mazes, they hit token limits and fail. Qualitative traces reveal a common two-stage strategy: image-to-grid translation followed by token-level search, effectively BFS in prose. A text-grid ablation shows Claude Sonnet 4.6 rising from 6\% on images to 80\% when given the correct grid, isolating weak visual extraction from downstream search. When explicitly instructed not to construct a grid or perform graph search, models still revert to the same enumeration ...