GeoSense: Internalizing Geometric Necessity Perception for Multimodal Reasoning

TL;DR

GeoSense enhances multimodal reasoning by enabling models to autonomously recognize when geometric information is necessary, improving spatial understanding without unnecessary computation or input rigidity.

Contribution

The paper introduces a novel framework that incorporates perceptual awareness and autonomous engagement of geometric features in multimodal models.

Findings

Significant improvements in spatial reasoning benchmarks.

Models maintain 2D visual reasoning capabilities.

Efficient utilization of geometric features without added overhead.

Abstract

Advancing towards artificial superintelligence requires rich and intelligent perceptual capabilities. A critical frontier in this pursuit is overcoming the limited spatial understanding of Multimodal Large Language Models (MLLMs), where geometry information is essential. Existing methods often address this by rigidly injecting geometric signals into every input, while ignoring their necessity and adding computation overhead. Contrary to this paradigm, our framework endows the model with an awareness of perceptual insufficiency, empowering it to autonomously engage geometric features in reasoning when 2D cues are deemed insufficient. To achieve this, we first introduce an independent geometry input channel to the model architecture and conduct alignment training, enabling the effective utilization of geometric features. Subsequently, to endow the model with perceptual awareness, we curate a dedicated spatial-aware supervised fine-tuning dataset. This serves to activate the model's latent internal cues, empowering it to autonomously determine the necessity of geometric information. Experiments across multiple spatial reasoning benchmarks validate this approach, demonstrating significant spatial gains without compromising 2D visual reasoning capabilities, offering a path toward more robust, efficient and self-aware multi-modal intelligence.