AI在真实空间任务中表现堪忧，最强模型成功率仅17%
你让AI帮你找钥匙、规划路线、协作搬东西——这些日常空间任务，目前最强AI的成功率只有17.4%。研究者构建了一个叫SpatialWorld的测试场，包含760个真实世界任务（如家庭日常、旅行、社交协作），AI只能通过视觉观察和文本指令来行动。结果发现，即使是GPT-5也经常迷路或低效绕路，开源模型Qwen-3.5稍低。这不是你明天能用上的工具，但它告诉你：AI离真正理解物理空间还很远，别指望它帮你做需要空间判断的事。
📄 原文摘要(英文)
Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predominantly rely on passive evaluation (e.g., static VQA) or simulator-specific pipelines, failing to assess general interactive spatial understanding. We introduce SpatialWorld, a unified benchmark designed specifically for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks. Integrating eight heterogeneous simulation backends under a shared, simulator-agnostic protocol, SpatialWorld features 760 human-annotated tasks across diverse domains (e.g., household routines, travel, social collaboration). Agents must solve tasks under vision-only partial observability, actively gathering egocentric visual evidence and expressing decisions via a unified, text-based action interface native to MLLMs. For reliable evaluation, each task includes a human-validated initial state, a reference trajectory, and a terminal-state verifier. Evaluating 15 advanced agents reveals that robust spatial task solving remains challenging: the strongest model, GPT-5, achieves an average task success rate (TSR) of only 17.4%, while the leading open-source model, Qwen-3.5, reaches 14.1%. Further analysis exposes a clear mismatch between task success and execution efficiency, alongside substantial domain-specific performance variations. These bottlenecks in active exploration and long-horizon planning position SpatialWorld as a rigorous testbed for future spatial agents.