From Where Things Are to What They Are For: Benchmarking Spatial–Functional Intelligence in Multimodal LLMs

Human level agentic intelligence transcends low-level geometric perception, evolving from knowing where things are to understanding what they are for. While existing benchmarks effectively evaluate this foundational geometric perception capabilites of multimodal LLMs, 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 1500 expert-annotated 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.