StreamSpatial: A Benchmark and Framework for Streaming 3D Visual-Spatial Reasoning

Embodied agents perceive the world as continuous streams,yet prevailing benchmarks evaluate spatial reasoning offline, presuppos-ing global spatial omniscience and overlooking real-time spatial intelli-gence. We present StreamSpatialBench, a testbed for streaming 3Dvisual-spatial reasoning built on three pillars: fine-grained online tempo-ral perspectives (past / current / future anchored to the query times-tamp), dynamic multi-agent interactions, and comprehensive 3D spatialreasoning. It comprises 5,280 millisecond-grounded QA pairs over 2,003egocentric/scene videos across 10 sub-tasks. Benchmarking 18 represen-tative MLLMs against humans reveals a substantial gap, rooted in threedeficiencies: limited spatiotemporal memory, absent incremental updat-ing, and poor adaptation to dynamic, multi-agent scenes. To close thisgap, we propose Stream3D-Mem, which (i) maintains a bounded mem-ory buffer with selective consolidation via round-decayed compressionto prune redundancy while preserving recent context, and (ii) distillscognitive-map knowledge into compact implicit latent tokens, bypassinglatency-heavy explicit map generation. Together they offer a rigoroustestbed and a practical step toward continuous, human-like 3D spatialintelligence for robotics and AR/VR. Code: https://github.com/OPPO-Mente-Lab/StreamSpatial.