Spatial Tool Forests for Structured Spatial Reasoning

Existing tool-augmented spatial reasoning methods rely on predefined tools whose limited coverage restricts complex reasoning, while directly expanding the tool library increases selection ambiguity. We propose Spatial Tool Forest (STF), a hierarchical framework that organizes spatial tools into an operation-specific forest and uses constrained Monte Carlo Tree Search (MCTS) for tool-chain planning. Given an image sequence and a natural-language query, the framework analyzes the query to extract its spatial requirements and searches legal trunks from compatible tool-forest branches for a reliable, task-adaptive tool chain. When no existing candidate trunk fully covers the required spatial operations, the framework retains the candidate trunk that best matches the current task as a scaffold, and a constrained LLM-based module generates only the missing tool under explicit interface, spatial-semantic, and evidence constraints. The resulting chain is executed to produce traceable evidence and the final answer. We evaluate STF on MINDCUBE-1k, OMNI3D-BENCH, and SPAR-Bench, observing consistent improvements across datasets and task categories. On MINDCUBE-1k, it improves overall accuracy over pySpatial by 6.38 percentage points and increases accuracy in the challenging around category from 51.60% to 70.40%. These results demonstrate improvements in spatial reasoning accuracy, stability, and adaptability through hierarchical tool organization, constrained planning, and targeted tool generation.