Multimodal Large Language Models (MLLMs) show strong visual perception, yet remain limited in reasoning about space under changing viewpoints. We study this challenge as Perspective-Conditioned Spatial Reasoning (PCSR) in 360-degree omnidirectional images, where broad scene coverage reduces ambiguity from partial observations without eliminating the need for viewpoint-dependent inference. To assess this capability, we introduce PCSR-Bench, a diagnostic benchmark of 84,373 question-answer pairs from 2,600 omnidirectional images across 26 indoor environments. PCSR-Bench contains eight tasks spanning foundational perception (e.g., object counting, relative distance, and relative direction) and advanced PCSR, including compositional chains, egocentric rotation, perspective re-anchoring, ego-distortion, and limited-FOV visibility. We evaluate 14 representative MLLMs and observe a substantial perception-reasoning gap: accuracy reaches 57.59% on foundational relative direction, but drops to 13.49% on egocentric rotation, 7.13% on egocentric distortion, and 0.64% on open-ended compositional reasoning. To probe the plasticity of this gap, we conduct an RL-based diagnostic study on a 7B-scale model. Reward shaping improves a matched 7B baseline from 31.10% to 60.06% under a controlled setting, suggesting that PCSR is partial plasticity rather than being fully immutable. Still, the gains are task-selective, sensitive to reward design including both weight allocation and reward formulation, and partially dependent on the evaluation protocol. These results position PCSR as a key bottleneck in current MLLMs and highlight limited but meaningful room for recovery under targeted optimization.

翻译：多模态大语言模型（MLLMs）展现出强大的视觉感知能力，但在视角变化条件下的空间推理方面仍存在局限。我们以360度全方位图像中的视角条件空间推理（PCSR）为切入点研究该挑战，此类图像凭借广阔的场景覆盖减少了局部观测的歧义性，但并未消除对视角依赖型推理的需求。为评估该能力，我们构建了PCSR-Bench诊断基准，包含来自26个室内环境中2600张全方位图像的84373个问答对。PCSR-Bench涵盖八个任务，横跨基础感知（如物体计数、相对距离、相对方向）与高级PCSR任务，包括组合链推理、自我中心旋转、视角重定向、自我中心畸变及有限视野可见性判断。我们评估了14个代表性MLLMs，观察到显著的感知-推理鸿沟：基础相对方向任务准确率达57.59%，但在自我中心旋转任务中降至13.49%，自我中心畸变任务中为7.13%，而开放式组合推理任务仅达0.64%。为探究该鸿沟的可塑性，我们基于7B规模模型开展了强化学习诊断研究。在受控条件下，奖励塑形使匹配的7B基线模型从31.10%提升至60.06%，表明PCSR具有部分可塑性而非完全固定不变。然而，性能提升呈现任务选择性，对包含权重分配和奖励公式化的奖励设计敏感，且部分依赖评估协议。这些结果将PCSR定位为当前MLLMs的关键瓶颈，并揭示了在针对性优化下存在有限但重要的改善空间。