这篇论文戳破了MLLM空间推理的泡沫——它们并不真正理解3D世界。做具身AI、多智能体系统或空间推理的开发者，看完会重新评估模型能力边界。
该论文揭示了多模态大语言模型（MLLM）在空间推理中存在的“笛卡尔幻觉”——依赖文本概率分布而缺乏真正的3D拓扑理解。作者设计了一个新颖的视听任务：让智能体A推断智能体B对A相对位置的估计，以测试MLLM的二阶心智理论能力。为解决此问题，他们提出了“锚定具身空间分解思维链”，引导模型先建立B的局部坐标系，再根据A是否在B视野内动态加权视觉和听觉模态。实验表明，当前MLLM在零样本下准确率仅42%，而该感知受限推理链显著优于纯自我中心或他中心基线。这项工作暴露了MLLM空间推理的当前极限，并为具身AI中的认知模态感知推理建立了基础范式。
Beyond the Cartesian Illusion: Testing Two-Stage Multi-Modal Theory of Mind under Perceptual Bottlenecks
While Multi-Modal Large Language Models (MLLMs) demonstrate impressive capabilities in general reasoning, their embodied spatial intelligence remains hampered by a "Cartesian Illusion" - a reliance on text-based probability distributions that lack grounded, 3D topological understanding. This limitation is starkly exposed in multi-agent environments, which demand more than just scene perception; they require second-order Theory of Mind (ToM). Specifically, an Agent A must be able to infer Agent B's belief about the environment, governed strictly by Agent B's physical orientation and sensory limitations. In this paper, we probe the limits of two-stage spatial inference in MLLMs through a novel audio-visual task: requiring Agent A to predict Agent B's estimation of A's relative location. To solve this, we propose an Epistemic Sensory Bottleneck module that abandons rigid, rule-based coordinate transformations. Instead, we introduce an Anchor-Based Embodied Spatial Decomposition Chain-of-Thought (CoT). This guides the MLLM through a "geometric-to-semantic" projection, forcing it to first establish B's local coordinate system and then dynamically weight visual and auditory modalities based on whether A falls within B's visual frustum. Extensive evaluations reveal that while current MLLMs fundamentally struggle with spatial symmetry and out-of-view ambiguities (establishing a rigorous zero-shot baseline of 42% accuracy), our sensory-bounded reasoning chain robustly outperforms pure egocentric and allocentric baselines. By systematically benchmarking these perceptual bottlenecks, our work exposes the current limits of MLLM spatial reasoning and establishes a foundational paradigm for epistemic, modality-aware inference in Embodied AI.