研究人员引入了新的基准测试，用于评估多模态大语言模型（MLLMs）的空间和功能推理能力。这些基准测试旨在超越基本的几何感知，评估结构化空间推理和理解物体在特定情境下的效用等更高级的认知能力。实验表明，当前的多模态大语言模型在整合空间记忆、功能推理和外部知识方面存在困难，这凸显了实现具身智能的重大瓶颈。 AI
影响 新的基准测试将推动更具认知能力的多模态智能体的开发，提升它们与现实世界的交互和规划能力。
排序理由 多篇arXiv论文介绍了用于评估多模态大语言模型空间和功能智能的新基准测试和模型。
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True spatial intelligence for multimodal agents transcends low-level geometric perception, evolving from knowing where things are to understanding what they are for. While existing benchmarks, such as VSI-Bench, effectively evaluate this foundational geometric stage, they fall sh…
arXiv:2511.21471v4 Announce Type: replace Abstract: Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal large language models (MLLMs) have made significant strides, existin…
arXiv:2605.04128v1 Announce Type: cross Abstract: We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples a spatially enhanced Multimodal Large Language Model (MLLM…
arXiv:2605.02130v1 Announce Type: new Abstract: Human-level agentic intelligence extends beyond low-level geometric perception, evolving from recognizing where things are to understanding what they are for. While existing benchmarks effectively evaluate the geometric perception c…