MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence
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Abstract (arXiv)
Spatial perception and reasoning are core components of human cognition, encompassing object recognition, spatial relational understanding, and dynamic reasoning. Despite progress in computer vision, existing benchmarks reveal significant gaps in models' abilities to accurately recognize object attributes and reason about spatial relationships, both essential for dynamic reasoning. To address these limitations, we propose MIRAGE, a multi-modal benchmark designed to evaluate models' capabilities in Counting (object attribute recognition), Relation (spatial relational reasoning), and Counting with Relation. Through diverse and complex scenarios requiring fine-grained recognition and reasoning, MIRAGE highlights critical limitations in state-of-the-art models, underscoring the need for improved representations and reasoning frameworks. By targeting these foundational abilities, MIRAGE provides a pathway toward spatiotemporal reasoning in future research.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 15 May 2025.
Builds on
16 of 22 references.
References · 16
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- 2025STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?cited by 79
4 more
- 2025MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMscited by 73
- 2024GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMscited by 33
- 2025iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMscited by 25
- 2025ST-Think: How Multimodal Large Language Models Reason About 4D Worlds from Ego-Centric Videoscited by 21
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2024SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation
- 2026SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models
- 2025OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models
- 2025Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
- 2025CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting
- 2026Spatial-IQ: Deconstructing Spatial Intelligence via Hierarchical Capability Tests
- 2026From Reasoning Failures to Composable Video Spatial Intelligence
- 2025Spatial Reasoning in Foundation Models: Benchmarking Object-Centric Spatial Understanding