AuthorsLe Zhang†**, Jihan Yang‡, Soundarya Krishnan, Jimit Majmudar, Xiou Ge, Prasoon Puri, Prathamesh Saraf, Shruti Bhargava, Dhivya Piraviperumal, Yinan Ling, Cindy Pan, Hong Yu, Aishwarya Agrawal†, Bo-Hsiang Tseng
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 short of probing the higher-order cognitive abilities essential for grounded intelligence. To bridge this gap, we introduce the Spatial-Functional Intelligence Benchmark (SFI-Bench), a video-based benchmark with over 1700 questions derived from diverse, egocentric indoor video scans. SFI-Bench is designed to systematically evaluate two complementary dimensions of advanced reasoning: (1) Structured Spatial Reasoning, understanding complex layouts and forming coherent spatial representations, and (2) Functional Reasoning, inferring object affordances and context-dependent utility. Its tasks, including conditional counting, multi-hop relational reasoning, functional pairing, and knowledge-grounded troubleshooting, directly challenge a model’s ability to integrate perception, memory, and inference. Our experiments reveal that current MLLMs consistently struggle to integrate spatial memory with functional and external knowledge, highlighting a critical bottleneck. SFI-Bench thus provides an essential tool for measuring and driving progress towards more cognitively capable and truly grounded multimodal agents.
Not yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate…
We introduce ImmerseDiffusion, an end-to-end generative audio model that produces 3D immersive soundscapes conditioned on the spatial, temporal, and environmental conditions of sound objects. ImmerseDiffusion is trained to generate first-order ambisonics (FOA) audio, which is a conventional spatial audio format comprising four channels that can be rendered to multichannel spatial output. The proposed generative system is composed of a spatial…
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