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taxonomy · benchmark · Ability Study

SpatialTree : How Spatial Abilities
Branch Out in MLLMs

Yuxi Xiao^▲,★,*, Longfei Li^♦,★,*, Shen Yan^★, Xinhang Liu^★, Sida Peng^▲,
Yunchao Wei^♦, Xiaowei Zhou^▲, Bingyi Kang^★,†

▲Zhejiang University · ★ByteDance Seed · ♦Beijing Jiaotong University

*Equal Contribution †Project Lead

arXiv 📄 PDF 🏆 Leaderboard

SpatialTree organizes spatial abilities into L1–L4, linking perception to agentic competence.

How does spatial intelligence emerge?

L1 Perception

L2 Mental Mapping

L3 Mental Simulation

L4 Spatial Agent

“Each stage builds on the stage before it.”

— Jean Piaget’s cognitive development theory

Overview

TL;DR

We propose SpatialTree, a cognitive-science-inspired taxonomy of spatial intelligence in multimodal large language models(MLLMs), along with SpatialTree-Bench and a proof-of-concept study on improving spatial abilities.

Takeaways

Dependencies Structure: Low-level abilities are relatively orthogonal, whereas higher-level abilities are increasingly compositional and coupled.
Ability Acquisition: Exhaustive supervised fine-tuning (SFT) over a broad range of low-level perceptual data can maximize cross-level transfer, benefiting higher-level spatial abilities.
Auto-thinking: Naive post-training with RLVR does not work; explicitly encouraging reasoning at higher levels while suppressing it for low-level perceptual skills makes broad improvements.

Cognitive Hierarchy

We design the SpatialTree taxonomy, inspired by cognitive science, to scaffold spatial abilities across four hierarchical levels.

Click a layer to see its focus, representative abilities.

L4 · Agentic Competence

Converts perception + memory into executable behaviors through the Spatial Action Mapping, covering goal execution, open-world exploration, and manipulation.

Data Samples

SpatialTree-Bench

SpatialTree-Bench measures 16 closed/open MLLMs across L1–L4 with balanced weighting, revealing orthogonal perception atoms and tightly coupled higher layers.

Distribution of Benchmark Categories

Closed-source Open-source

Model	Avg.	Perception	Mental Mapping	Mental Simulation	Agentic Competence
Gemini 2.5 Pro	50.1	49.3	53.8	51.3	45.8
Gemini 2.5 Pro NT	41.4	38.8	46.9	42.4	37.6
Qwen3VL-235B	40.0	34.6	46.6	39.9	38.9
Gemini 2.5 Flash	39.0	38.3	46.3	37.8	33.5
GLM4.5V	36.0	35.0	34.4	36.3	38.3
Gemini 2.5 Flash NT	35.8	33.5	41.6	31.4	36.7
Seed1.6-Vision	35.7	36.3	36.6	35.4	34.6
Qwen3VL-30B	35.3	30.2	40.9	36.7	33.4
Qwen 2.5VL-72B	33.0	28.8	37.8	34.6	31.3
GPT-4o	31.9	26.8	34.9	33.2	32.5
Qwen 2.5VL-32B	27.9	24.0	26.8	34.3	26.4
Qwen 2.5VL-7B	27.5	20.9	28.9	32.4	27.8
Kimi-VL-A3B	24.4	20.3	25.9	27.2	24.2

Hierarchy insight: L1 abilities are largely orthogonal, while L3 and L4 scores are strongly correlated.

Ability Study

Ability Dependency Analysis

(Region A + Region B): Low-level abilities are largely orthogonal, while high-level abilities are tightly coupled through integration.

(Region C): Low-level abilities form clear prerequisites that higher-level abilities depend on.

Finding 1

Cross-Ability Transfer: Single-ability L1 SFT induces cross-level transfer, while yielding limited or slightly negative effects on same-level abilities.

Finding 2

Multi-ability Synergy: The holistic integration across multiple fundamental abilities achieves synergistic gains far exceeding their individual effects.

Methods	Avg.	L1 Perception					L2 Mental Mapping		L3 Mental Simulation		L4 Agentic Competence
Methods	Avg.	Geom.	Motion	Rel.	Local.	Orient.	Underst.	Memory	Caus. Reas.	Seq. Plan.	Goal Exec.	Open Expl.
Baseline	25.0	20.9	28.6	28.9	24.2	34.2	22.6	21.7	27.2	31.7	22.1	26.5
B+Dist.	24.5	24.1 +3.2	26.6 -2.0	23.2 -5.8	19.6 -4.6	34.3 +0.1	24.6 +2.0	21.8 +0.1	26.1 -1.1	30.8 -0.9	25.5 +3.4	26.1 -0.4
B+Corr.	25.2	17.6 -3.2	23.9 -4.7	30.2 +1.3	18.9 -5.3	35.6 +1.4	21.9 -0.7	24.6 +2.9	21.8 -5.4	33.9 +2.2	24.7 +2.6	35.9 +9.4
B+Size	23.5	24.3 +3.4	22.6 -6.0	21.4 -7.5	21.7 -2.5	34.5 +0.3	21.9 -0.8	19.2 -2.5	23.4 -3.8	30.3 -1.5	21.5 -0.6	24.3 +2.2
B+Dist.+Size+Corr.	26.1	25.5 +4.6	29.3 +0.7	29.4 +0.5	16.4 -7.8	33.7 +0.5	23.0 +0.4	24.2 +2.5	25.2 -2.0	34.2 +2.5	26.0 +3.9	28.5 +2.0
B+Dist.+Size+Corr.+Mot.	27.3	28.6 +7.7	24.6 -4.0	20.6 -8.3	26.3 +2.1	36.0 +1.8	22.2 -0.4	22.6 +0.9	28.2 +1.0	32.8 +1.1	23.3 +1.1	35.9 +9.4
Baseline+75@(all spat.)	23.6	24.9 +4.0	22.6 -6.0	25.9 -3.0	17.4 -6.8	31.2 -3.0	22.2 -0.4	20.6 -1.1	25.7 -1.5	30.2 -1.5	19.7 -2.4	22.8 -3.7

SFT Comparisons. "B+Dist.", "B+Corr.", and "B+Size" denote the baseline augmented with distance, correspondence, and size tuning data, respectively. Changes are color-coded as notable gains, neutral influence, and drops.

Finding 3

Auto-Think Strategy: We uncover a trade-off in RL where excessive reasoning hurts intuitive perception. We propose a hierarchy-aware "Auto-think" strategy that suppresses reasoning for low-level sensing while encouraging it for complex planning.

Methods	Avg.	L1 Perception					L2 Mental Mapping		L3 Mental Simulation		L4 Agentic Competence
Methods	Avg.	Geom.	Motion	Rel.	Local.	Orient.	Underst.	Memory	Caus. Reas.	Seq. Plan.	Goal Exec.	Open Expl.
SFT Baseline	27.3	28.6	24.6	20.6	26.3	36.0	22.2	22.6	28.2	32.8	23.3	35.9
L1 RL@think	26.6 -0.7	28.4 +0.9	30.0 +5.4	30.5 +9.9	19.5 -6.8	34.5 -1.4	24.9 +2.8	18.5 -4.1	25.7 -2.4	34.0 +1.1	24.1 +0.8	29.6 -6.3
L2 RL@think	26.7 -0.5	24.4 -4.1	22.7 -1.9	22.3 +1.6	17.0 -9.4	35.0 -0.9	26.6 +4.5	16.1 -6.5	29.0 +0.8	31.4 -1.4	25.6 +2.3	34.5 -1.4
L3 RL@think	27.7 +0.4	16.2 -12.3	24.0 -0.6	24.1 +3.5	21.4 -4.9	38.5 +2.5	26.0 +3.9	21.8 -0.8	31.3 +3.1	34.7 +1.9	26.5 +3.2	38.4 +2.5
L4 RL@think	28.5 +1.2	23.8 -4.8	25.3 +0.7	22.1 +1.4	23.5 -2.8	33.9 -2.1	25.1 +3.0	20.5 -2.2	32.0 +3.8	34.2 +1.4	27.1 +3.9	38.8 +2.9
Full RL@think	30.1 +2.9	29.7 +1.1	24.7 +0.1	27.2 +6.5	21.0 -5.3	34.8 -1.2	27.4 +5.2	16.7 -5.9	33.6 +5.5	37.6 +4.8	25.4 +2.1	41.7 +5.8
Full RL@auto-think	30.8 +3.6	31.9 +3.3	28.6 +4.0	22.0 +1.3	23.1 -3.2	36.8 +0.8	28.0 +5.8	22.6 -0.1	33.5 +5.4	35.6 +2.8	23.4 +0.1	44.1 +8.3

RLVR Comparisons. The table compares the SFT baseline with versions enhanced by RL on different tasks. Changes are color-coded as notable gains, neutral influence, and drops.

Citation

If you find our project helpful to your research, you can cite us with:

@article{spatialtree2025,
  title={SpatialTree: How Spatial Abilities Branch Out in MLLMs},
  author={Xiao, Yuxi and Li, Longfei and Yan, Shen and Liu, Xinhang and Peng, Sida and Wei, Yunchao and Zhou, Xiaowei and Kang, Bingyi},
  journal={arXiv preprint},
  year={2025}
}

Original Source | Taken Source

SpatialTree : How Spatial AbilitiesBranch Out in MLLMs