Amaze: Diagnosing Visual-Spatial Reasoning with Matched Maze Twins

Vision-language models (VLMs) often fail on visual problems that require extended spatial computation, even when they can solve an equivalent symbolic problem. We introduce AMAZE , a diagnostic benchmark of procedurally generated mazes varying in scale, geometry, and visual texture. Its central design is a matched-twin protocol: each solvable maze is paired with a minimally modifiedunsolvable twin, and a model receives credit only if it classifies both correctly. This exposes a strong bias toward predicting that a path exists. To distinguish failures of visual access from failures of evidence integration, we equip agents with image-processing tools and compare monolithic execution with an orchestrated system of context-isolated specialists using local programmatic verification. Across 16 maze layouts and 1,600 matched pairs, the multi-step composition of low-level visual tools lifts paired accuracy from 12.9% to 31.6% for a monolithic agent. Orchestration with local verification further raises it to 67.6%. When a global reachability tool collapses connectivity reasoning to a single call, tools alone lift the monolithic agent to 75.8% and orchestration adds a smaller gain to 87.3%. The same pattern transfers to MentisOculi (1,000 puzzles), where accuracy rises from 58.0% without tools to 71.1% with a monolithic tool-using agent and 73.6% with orchestration. These results show that while visual tools yield large gains across visual reasoning tasks, tool access alone does not eliminate solvability bias: reliable integration of negative visual evidence is critical, and decentralization helps when that integration requires extended composition.