arXiv:2505.23764v3 Announce Type: replace-cross Abstract: 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 ca
The proliferation of advanced MLLMs and the increasing demand for real-world robotic and agentic applications necessitate more rigorous and complex benchmarks for spatial reasoning.
This benchmark addresses a critical gap in evaluating MLLMs' ability to reason across multiple images, which is fundamental for advanced AI applications requiring true environmental understanding.
The introduction of MMSI-Bench will drive MLLM development towards more sophisticated multi-image spatial intelligence, potentially accelerating the capabilities of AI agents and robotics.
- · AI researchers in multi-modal LLMs
- · Developers of AI agents and robotics
- · Companies investing in advanced computer vision
- · 3D vision researchers
- · MLLMs limited to single-image reasoning
- · Benchmarks focusing only on single-image evaluations
Improved MLLMs capable of better understanding complex, dynamic environments.
Faster progress in the development of general-purpose AI agents and autonomous systems.
Enhanced safety and functionality of robots and AI systems operating in unstructured physical spaces.
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Read at arXiv cs.CL