How Far Are Vision-Language Models from Constructing the Real World? A Benchmark for Physical Generative Reasoning

The physical world is not merely visual; it is governed byrigorous structural and procedural constraints. Yet, the evaluation ofvision-language models (VLMs) remains heavily skewed toward percep-tual realism, prioritizing the generation of visually plausible 3D lay-outs, shapes, and appearances. Current benchmarks rarely test whethermodels grasp the step-by-step processes and physical dependencies re-quired to actually build these artifacts—a capability essential for au-tomating design-to-construction pipelines. To address this, we introduceDreamHouse, a novel benchmark for physical generative reasoning:the capacity to synthesize artifacts that concurrently satisfy geomet-ric, structural, constructability, and code-compliance constraints. Weground this benchmark in residential timber-frame construction, a do-main with fully codified engineering standards and objectively verifiablecorrectness. We curate over 26,000 structures spanning 13 architecturalstyles—each verified to construction-document standards (LOD 350)—and develop a deterministic 10-test structural validation framework. Un-like static benchmarks that assess only final outputs, DreamHouse sup-ports iterative agentic interaction. Models observe intermediate buildstates, generate construction actions, and receive structured environ-mental feedback, enabling a fine-grained evaluation of planning, struc-tural reasoning, and self-correction. Extensive experiments with state-of-the-art VLMs reveal substantial capability gaps that are largely invis-ible on existing leaderboards. These findings establish physical validityas a critical evaluation axis orthogonal to visual realism, highlightingphysical generative reasoning as a distinct and underdeveloped fron-tier in multimodal intelligence. The benchmark is available at https://luluyuyuyang.github.io/dreamhouse/.