SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
arXiv:2606.09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predominantly rely on passive evaluation (e.g., static VQA) or simulator-specific pipelines, failing to assess general interactive spatial understanding. We introduce SpatialWorld, a unified benchmark designed specifically for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks. Integrating eight heterogeneous simulation backends un
The rapid advancement of MLLMs necessitates more robust evaluation benchmarks to validate their interactive capabilities in real-world scenarios, moving beyond passive tasks.
A benchmark for interactive spatial reasoning directly addresses a critical gap in evaluating sophisticated AI agents, which are increasingly expected to operate autonomously in physical environments.
The introduction of SpatialWorld provides a standardized, unified testing ground for MLLMs' interactive spatial understanding, potentially accelerating development and deployment of truly agentic AI.
- · AI model developers
- · Robotics companies
- · Simulation platform providers
- · Multimodal AI research
- · Developers relying solely on static benchmarks
- · AI models lacking interactive spatial reasoning
SpatialWorld will enable more rigorous and comparative evaluation of multimodal AI agents.
Improved benchmarks will accelerate the development of more capable and reliable AI agents for real-world applications.
The widespread adoption of such agents could lead to significant automation advancements across various industries.
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Read at arXiv cs.AI