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BrickWright: Agentic Creation of Physically Feasible Brick Builds

Ava Pun, Kevin Dai, Sally Chen, Maxwell Jones, Ruixuan Liu, Kangle Deng, Changliu Liu, Maneesh Agrawala, Deva Ramanan, Jun-Yan Zhu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31651 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

Addontier 编码代理人越来越擅长 3D 建模,然而,他们仍然面临挑战,当建筑物被设计用于在物理世界中组装时,通常会创建浮动、碰撞或分离的部件的无效组合。我们认为提供工具来组装构建并验证其物理可行性,足以使他们生成物理可行组合,如果它只使用实际部件,没有碰撞或浮动部件,并在可能的情况下形成连接结构,我们介绍 BrickWright,一个框架,用于生成从文本或图像请求中生成的物理可行砖构建,其中一个构建是物理可行,如果它只使用实际部件,没有碰撞或浮动部件,并形成一个连接的结构。 BrickWright 指导代理人创

Machine translation (Meta M2M100); the English original follows.

Abstract

Frontier coding agents are becoming increasingly adept at 3D modeling. Yet, they still face challenges when building objects meant to be assembled in the physical world, often creating invalid assemblies with floating, colliding, or disconnected parts. We argue that providing agents with tools for assembling builds and verifying their physical feasibility is sufficient for them to produce physically valid assemblies that still adhere to input prompts. Focusing on the domain of LEGO brick builds, we present BrickWright, a framework for generating physically feasible brick builds from text or image prompts using pre-trained agents, where a build is physically feasible if it uses only real parts, has no colliding or floating parts, and forms a connected structure if possible. BrickWright guides agents to create builds by writing code with BrickLayer, a Python toolkit with two main components: an ergonomic brick assembly API that halves inference cost compared to working in raw LDraw, and a validator that provides feedback on physical feasibility. Whereas baseline agents achieve physical validity rates ranging from only 9% (GPT-6 Luna) to 69% (GPT-6 Astra), BrickWright-guided agents achieve 100% validity on a sample of 512 prompts, while improving prompt adherence for lightweight agents and maintaining it for stronger agents. Additionally, they outperform specialized models on the text-to-brick task. Code is available on our project page: https://avalovelace1.github.io/brickwright/

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Topics: Validator, Computer science, Python (programming language), Code (set theory), Software engineering, Human–computer interaction

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange