发表机构
Peking University; Joy Future Academy（北京大学; 京东探索研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
多模态大语言模型推理空间关系易产生幻觉，现有方法效果有限。本文定义空间推理幻觉及常见场景，提出无训练的Geo3R框架，结合几何证据和结构化3D推理减轻幻觉，实验证明该框架显著减少幻觉，优于现有模型和方法。
AI 中文摘要
尽管视觉理解取得了显著进展，但多模态大语言模型在推理空间关系时仍容易产生幻觉，常做出与场景真实3D结构相矛盾的判断。现有方法在空间推理中效果有限，未能弥合2D视觉表示与3D空间现实的根本差距。基于此，定义了因空间结构建模不足产生的空间推理幻觉。还识别出三种常见场景。提出了无训练、即插即用的Geo3R框架，结合几何证据和结构化3D推理减轻幻觉。在三个基准测试上的实验表明，Geo3R显著减少了幻觉，优于现有模型和方法。
英文摘要
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.
CommentsAccepted by ACM MM 2026. This is the arXiv preprint version, not the camera-ready
