SpatialTree: How Spatial Abilities Branch Out in MLLMs
Cognitive science suggests that spatial ability develops progressively-from perception to reasoning and interaction. Yet in multimodal LLMs (MLLMs), this hierarchy remains poorly understood, as most studies focus on a narrow set of tasks. We introduce SpatialTree, a cognitive-science-inspired hierarchy that organizes spatial abilities into four levels: low-level perception (L1), mental mapping (L2), simulation (L3), and agentic competence (L4). Based on this taxonomy, we construct the first capability-centric hierarchical benchmark, thoroughly evaluating mainstream MLLMs across 27 sub-abilities. The evaluation results reveal a clear structure: L1 skills are largely orthogonal, whereas higher-level skills are strongly correlated, indicating increasing interdependency. Through targeted supervised fine-tuning, we uncover a surprising transfer dynamic-negative transfer within L1, but strong cross-level transfer from low- to high-level abilities with notable synergy. Finally, we explore how to improve the entire hierarchy. We find that naive RL that encourages extensive "thinking" is unreliable: it helps complex reasoning but hurts intuitive perception. We propose a simple auto-think strategy that suppresses unnecessary deliberation, enabling RL to consistently improve performance across all levels. By building SpatialTree, we provide a proof-of-concept framework for understanding and systematically scaling spatial abilities in MLLMs.
694b58e3746a34b55dd53cff
Yuxi Xiao
claimed_verified
2025-12-25T20:45:54.635Z
6688a8f30bf195d6e53ac28d
https://cdn-avatars.huggingface.co/v1/production/uploads/6688a8f30bf195d6e53ac28d/5izbVmdCWWwA1wBjcxZPB.jpeg
Yuxi Xiao
Yuxihenry
user
Yuxihenry
694b58e3746a34b55dd53d00
Longfei Li
claimed_verified
2025-12-25T20:45:51.912Z
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/avatars/6aa505d6e82782085dd8b5ffcf0d4c15.svg
LiLongfei
LongfeiLi
user
LongfeiLi
694b58e3746a34b55dd53d01
Shen Yan
694b58e3746a34b55dd53d02
Xinhang Liu
694b58e3746a34b55dd53d03
Sida Peng
694b58e3746a34b55dd53d04
Yunchao Wei
694b58e3746a34b55dd53d05
Xiaowei Zhou
694b58e3746a34b55dd53d06
Bingyi Kang
SpatialTree: How Spatial Abilities Branch Out in MLLMs
Cognitive science suggests that spatial ability develops progressively-from perception to reasoning and interaction. Yet in multimodal LLMs (MLLMs), this hierarchy remains poorly understood, as most studies focus on a narrow set of tasks. We introduce SpatialTree, a cognitive-science-inspired hierarchy that organizes spatial abilities into four levels: low-level perception (L1), mental mapping (L2), simulation (L3), and agentic competence (L4). Based on this taxonomy, we construct the first capability-centric hierarchical benchmark, thoroughly evaluating mainstream MLLMs across 27 sub-abilities. The evaluation results reveal a clear structure: L1 skills are largely orthogonal, whereas higher-level skills are strongly correlated, indicating increasing interdependency. Through targeted supervised fine-tuning, we uncover a surprising transfer dynamic-negative transfer within L1, but strong cross-level transfer from low- to high-level abilities with notable synergy. Finally, we explore how to improve the entire hierarchy. We find that naive RL that encourages extensive "thinking" is unreliable: it helps complex reasoning but hurts intuitive perception. We propose a simple auto-think strategy that suppresses unnecessary deliberation, enabling RL to consistently improve performance across all levels. By building SpatialTree, we provide a proof-of-concept framework for understanding and systematically scaling spatial abilities in MLLMs.
SpatialTree, a cognitive-science-inspired hierarchy, evaluates and improves spatial abilities in MLLMs across multiple levels, revealing transfer dynamics and proposing an auto-think strategy for consistent performance enhancement.
SpatialTree low-level perception mental mapping simulation agentic competence capability-centric hierarchical benchmark targeted supervised fine-tuning negative transfer cross-level transfer naive RL auto-think strategy