SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes

• URL: http://arxiv.org/abs/2605.31148v1
• Date: Fri, 29 May 2026 10:59:26 GMT
• Title: SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes
• Abstract summary: Humans can effortlessly perceive spatial layouts, form cognitive representations, and translate such reasoning into actions in everyday 3D environments.<n>Recent vision-language models (VLMs) have shown promising performance on observation-conditioned spatial perception and reasoning tasks.<n>We introduce textbfSpatialAct, a simulator-grounded benchmark for probing textitaction-conditioned spatial reasoning in 3D scenes.
• Score: 15.076506371158876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans can effortlessly perceive spatial layouts, form cognitive representations, reason about spatial relations, and translate such reasoning into actions in everyday 3D environments. Although recent vision-language models (VLMs) have shown promising performance on observation-conditioned spatial perception and reasoning tasks, it remains unclear whether they can build coherent spatial understanding, act upon it, and refine their actions through multi-turn feedback. To study this problem, we introduce \textbf{SpatialAct}, a simulator-grounded benchmark for probing \textit{action-conditioned spatial reasoning} in 3D scenes. Starting from the most challenging setting, Multi-turn Interactive Refinement, we further design its decomposed counterpart, Single-step Error Detection and Fix, together with five fundamental spatial ability tasks to diagnose the underlying causes of model failures. Experiments reveal a clear reasoning-to-action gap: current VLMs can perform well on isolated spatial reasoning tasks, but struggle to maintain coherent spatial beliefs and produce reliable actions during multi-turn feedback, substantially underperforming humans. These results suggest that current VLM agents still lack robust spatial state tracking under action-induced environment changes, even when low-level control is abstracted away.

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