Geo3R: Mitigating Spatial Reasoning Hallucination in Multimodal Large Language Models
For researchers and practitioners using MLLMs, Geo3R addresses a persistent bottleneck in spatial reasoning without requiring additional training, offering a practical solution to improve reliability in 3D-aware tasks.
Geo3R is a training-free, plug-and-play framework that reduces spatial reasoning hallucinations in MLLMs by incorporating geometric evidence and structured 3D reasoning, outperforming existing methods across 18 tasks on three benchmarks.
Despite remarkable progress in visual understanding, Multimodal Large Language Models (MLLMs) remain prone to hallucinations when reasoning about spatial relationships, often producing judgments that contradict the true 3D structure of the scene. Though several existing works have proposed to mitigate hallucinations, our analysis indicates that they show limited effectiveness in spatial reasoning, as they fail to bridge the fundamental gap between 2D visual representations and 3D spatial reality. Based on this finding, we define hallucinations arising from insufficient spatial structure modeling as spatial reasoning hallucination, a subcategory of relation hallucination that existing mitigation methods fail to address. We further identify three typical scenarios where such hallucinations frequently occur: perspective effects, object orientation, and viewpoint changes. To this end, we propose Geo3R, a training-free, plug-and-play framework that incorporates geometric evidence and structured 3D reasoning to mitigate spatial reasoning hallucination. Experiments on three benchmarks, covering 18 tasks across all three scenarios, show that Geo3R substantially reduces spatial reasoning hallucination across diverse MLLMs without additional training, outperforming existing models and methods.