Stand Up and Move: Benchmarking Interactive Spatial Intelligence in WalkerBench

The ultimate goal of spatial intelligence is to enable embod-ied agents to actively interact with the physical world. However, exist-ing benchmarks adopt a “Spectator View”—relying on single images orpassive video—neglecting the necessity of resolving geometric ambigu-ity through active viewpoint transformation. To address this, we presentWalkerBench, a global-scale interactive benchmark built on real-worldstreet-view and map data spanning 161 cities, with two categories ofhierarchically structured spatial tasks. Our empirical evaluation revealsa critical “representation misalignment”: VLMs’ one-dimensional linearcontext structures are fundamentally incompatible with the topologicalnature of 3D environments, causing inevitable spatial forgetting duringnavigation. To overcome this, we propose Spatial-IDE, which breaks fromlinear dialogue history via two mechanisms: (1) State Externalization,transforming implicit observations into an Explicit Topological Memory(ETM); and (2) Cognitive Decoupling, disentangling visual understand-ing into Goal-Directed Perception and Spatial Reasoning, letting theVLM focus on pure high-level decision-making. Spatial-IDE achieves sub-stantial improvements on WalkerBench and successfully generalizes zero-shot to real-world urban navigation on the Unitree humanoid robot. Theproject is available on https://github.com/lalayang123456- ctrl/WalkerBench.