Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866908818328256512 |
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| author | Zhang, Pingyue Huang, Zihan Wang, Yue Zhang, Jieyu Xue, Letian Wang, Zihan Wang, Qineng Chandrasegaran, Keshigeyan Zhang, Ruohan Choi, Yejin Krishna, Ranjay Wu, Jiajun Fei-Fei, Li Li, Manling |
| author_facet | Zhang, Pingyue Huang, Zihan Wang, Yue Zhang, Jieyu Xue, Letian Wang, Zihan Wang, Qineng Chandrasegaran, Keshigeyan Zhang, Ruohan Choi, Yejin Krishna, Ranjay Wu, Jiajun Fei-Fei, Li Li, Manling |
| contents | Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capacity for active, self-directed exploration remains understudied. We propose Theory of Space, defined as an agent's ability to actively acquire information through self-directed, active exploration and to construct, revise, and exploit a spatial belief from sequential, partial observations. We evaluate this through a benchmark where the goal is curiosity-driven exploration to build an accurate cognitive map. A key innovation is spatial belief probing, which prompts models to reveal their internal spatial representations at each step. Our evaluation of state-of-the-art models reveals several critical bottlenecks. First, we identify an Active-Passive Gap, where performance drops significantly when agents must autonomously gather information. Second, we find high inefficiency, as models explore unsystematically compared to program-based proxies. Through belief probing, we diagnose that while perception is an initial bottleneck, global beliefs suffer from instability that causes spatial knowledge to degrade over time. Finally, using a false belief paradigm, we uncover Belief Inertia, where agents fail to update obsolete priors with new evidence. This issue is present in text-based agents but is particularly severe in vision-based models. Our findings suggest that current foundation models struggle to maintain coherent, revisable spatial beliefs during active exploration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07055 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration? Zhang, Pingyue Huang, Zihan Wang, Yue Zhang, Jieyu Xue, Letian Wang, Zihan Wang, Qineng Chandrasegaran, Keshigeyan Zhang, Ruohan Choi, Yejin Krishna, Ranjay Wu, Jiajun Fei-Fei, Li Li, Manling Artificial Intelligence Computation and Language Machine Learning Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capacity for active, self-directed exploration remains understudied. We propose Theory of Space, defined as an agent's ability to actively acquire information through self-directed, active exploration and to construct, revise, and exploit a spatial belief from sequential, partial observations. We evaluate this through a benchmark where the goal is curiosity-driven exploration to build an accurate cognitive map. A key innovation is spatial belief probing, which prompts models to reveal their internal spatial representations at each step. Our evaluation of state-of-the-art models reveals several critical bottlenecks. First, we identify an Active-Passive Gap, where performance drops significantly when agents must autonomously gather information. Second, we find high inefficiency, as models explore unsystematically compared to program-based proxies. Through belief probing, we diagnose that while perception is an initial bottleneck, global beliefs suffer from instability that causes spatial knowledge to degrade over time. Finally, using a false belief paradigm, we uncover Belief Inertia, where agents fail to update obsolete priors with new evidence. This issue is present in text-based agents but is particularly severe in vision-based models. Our findings suggest that current foundation models struggle to maintain coherent, revisable spatial beliefs during active exploration. |
| title | Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration? |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2602.07055 |