arXiv:2603.26839v1 Announce Type: new
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 strategy. textsc{MazeBench} therefore shows that high accuracy on visual planning tasks does not imply human-like spatial understanding.