MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the complex physical world. Existing benchmarks, however, probe only single-image relations and thus fail to assess the multi-image spatial reasoning that real-world deployments demand. We introduce MMSI-Bench, a VQA benchmark dedicated to multi-image spatial intelligence. Six 3D-vision researchers spent more than 300 hours meticulously crafting 1,000 challenging, unambiguous multiple-choice questions from over 120,000 images, each paired with carefully designed distractors and a stepwise reasoning process. We conduct extensive experiments and evaluate 37 open-source and proprietary MLLMs, observing a wide gap: the strongest open-source model attains roughly 30% accuracy and OpenAI's GPT-5 reasoning model reaches 40%, while humans score 97%. These results underscore the challenging nature of MMSI-Bench and the substantial headroom for future research. Leveraging the annotated reasoning processes, we also provide an automated error analysis pipeline that diagnoses four dominant failure modes, including (1) grounding errors, (2) overlap-matching and scene-reconstruction errors, (3) situation-transformation reasoning errors, and (4) spatial-logic errors, offering insights for advancing spatial intelligence. Project page: https://runsenxu.com/projects/MMSI_Bench .
Key facts. Cited by 169 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 47 of its 71 references that are in the map. First posted on arXiv 29 May 2025.
Builds on
47 of 71 references.
Closest ancestors · 8
Same direction, newest first
- 2025LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?25 Mar
- 2024Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces18 Dec
- 20243DSRBench: A Comprehensive 3D Spatial Reasoning Benchmark10 Dec
- 2024An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models9 Nov
- 2024Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models15 Sep
- 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models3 Jun
- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities22 Jan
- 2022Visual Spatial Reasoning30 Apr
Other references · 39
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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- 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
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- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
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- 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,075
- 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 942
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- 2025AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systemscited by 390
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- 2024RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Roboticscited by 133
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- 2024Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmarkcited by 53
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- 2023PATS: Patch Area Transportation with Subdivision for Local Feature Matchingcited by 33
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Citing papers per year: 2025: 40; 2026: 129.
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