Thinking with Geometry: Active Geometry Integration for Spatial Reasoning

TL;DR

GeoThinker introduces an active perception framework for spatial reasoning in multimodal models, enabling selective geometric evidence retrieval to improve spatial understanding and generalization.

Contribution

It proposes GeoThinker, a novel active perception approach that enhances spatial reasoning by selectively integrating geometry conditioned on reasoning demands.

Findings

Achieves a new state-of-the-art score of 72.6 on VSI-Bench.

Demonstrates improved spatial perception in embodied referring and autonomous driving.

Shows robust generalization across complex downstream scenarios.

Abstract

Recent progress in spatial reasoning with Multimodal Large Language Models (MLLMs) increasingly leverages geometric priors from 3D encoders. However, most existing integration strategies remain passive: geometry is exposed as a global stream and fused in an indiscriminate manner, which often induces semantic-geometry misalignment and redundant signals. We propose GeoThinker, a framework that shifts the paradigm from passive fusion to active perception. Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands. GeoThinker achieves this through Spatial-Grounded Fusion applied at carefully selected VLM layers, where semantic visual priors selectively query and integrate task-relevant geometry via frame-strict cross-attention, further calibrated by Importance Gating that biases per-frame attention toward task-relevant structures. Comprehensive evaluation results show that GeoThinker sets a new state-of-the-art in spatial intelligence, achieving a peak score of 72.6 on the VSI-Bench. Furthermore, GeoThinker demonstrates robust generalization and significantly improved spatial perception across complex downstream scenarios, including embodied referring and autonomous driving. Our results indicate that the ability to actively integrate spatial structures is essential for next-generation spatial intelligence. Code can be found at https://github.com/Li-Hao-yuan/GeoThinker.