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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

Indonesian

Addontier Code adalah agen pengkodean yang semakin terampil dalam pemodelan 3D. Namun, mereka masih menghadapi tantangan ketika objek bangunan dimaksudkan untuk dipasang di dunia fisik, sering menciptakan assemblies yang tidak sah dengan bagian yang mengambang, bertabrakan, atau tidak terhubung. Kami berpendapat bahwa menyediakan agen dengan alat untuk mengumpulkan bangunan dan memverifikasi kelayakan fisik mereka cukup bagi mereka untuk menghasilkan assemblies yang valid secara fisik jika masih menggunakan bagian-bagian nyata, tidak memiliki bagian yang bertabrakan atau mengambang, dan membentuk struktur yang terhubung jika memungkinkan. BrickWright menyajikan BrickWright, sebuah kerangka kerja untuk menghasilkan bangunan batu bata yang dapat dilakukan secara fisik dari prompt teks atau gambar menggunakan prompen utama, di mana sebuah bangunan dapat dilakukan secara fisik jika hanya menggunakan bagian-bagian nyata, tidak memiliki bagian-tugas yang

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

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