MindCube: Spatial Mental Modeling from Limited Views
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
Abstract (arXiv)
Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen space, to reason about layout, perspective, and motion. Our MindCube benchmark with 21,154 questions across 3,268 images exposes this critical gap, where existing VLMs exhibit near-random performance. Using MindCube, we systematically evaluate how well VLMs build robust spatial mental models through representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for "what-if" movements). We then explore three approaches to help approximate spatial mental models in VLMs, focusing on incorporating unseen intermediate views, natural language reasoning chains, and cognitive maps. The significant improvement comes from a synergistic approach, "map-then-reason", that jointly trains the model to first generate a cognitive map and then reason upon it. By training models to reason over these internal maps, we boosted accuracy from 37.8% to 57.8% (+20.0%). Adding reinforcement learning pushed performance even further to 61.3% (+23.5%). Our key insight is that such scaffolding of spatial mental models, actively constructing and utilizing internal structured spatial representations with flexible reasoning processes, significantly improves understanding of unobservable space.
Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. First posted on arXiv 26 Jun 2025.
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Citing papers per year: 2025: 32; 2026: 63.
95 papers.
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- 2026Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models8 Oct 2026
- 2026From Reasoning Failures to Composable Video Spatial Intelligence1 Oct 2026
- 2026JRDB-AVR: An Active Visual Reasoning Benchmark for Embodied Agents in Real-World Environments28 Sep 2026
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- 2026MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos1 Jul 2026
- 2026Vesta: A Generalist Embodied Reasoning Model18 Jun 2026
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- 2026Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training12 Mar 2026
- 2026Stepping VLMs onto the Court: Benchmarking Spatial Intelligence in Sports10 Mar 2026
- 2026Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence8 Mar 2026
- 2026ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge10 Feb 2026
- 2026STEP3-VL-10B Technical Report14 Jan 2026
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