VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control

Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret
it in a common spatial frame, and act on it. We introduce VA-BENCH to evaluate the complete observe–reason–act–revise loop. General-purpose MLLMs learn
procedural context from RGB-only demonstrations, actively select camera viewpoints, issue metric Cartesian commands, and revise them from execution feedback. Models receive no privileged object poses, oracle trajectories, or learned
action heads. A fixed model-agnostic controller executes only model-specified targets. VA-Bench contains 14 base task families (11 single-arm and three dual-arm),
seven held-out geometry/layout variants, and a long-horizon five-object composition track. We evaluate 12 primary model conditions in three independent runs
over the same 20 physically verified seeds per base task, reporting terminal success, nine trajectory-level behavioral diagnostics, and subtask progress. First,
the best-performing model scores 100.0% on target localization and 78.9%
on spatial relations in the annotated run. Its three-run macro-average task
success is only 53.93 ± 3.17%. Second, active camera control significantly
improves task success over passive multi-view observation. In one matched
comparison, success rises from 27.86% to 57.50%. Third, held-out geometric transfer can reduce task success by over 30 percentage points. No model
completes a strict long-horizon episode, despite substantial partial progress.
VA-Bench thus tests whether general-purpose MLLMs can turn visual demonstrations and actively acquired evidence into successful embodied action. Code and
benchmark: github.com/zhangzhongbo2213/VABench.