# aDSL：通过智能体与程序联合设计实现智能体3D内容生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-18 08:00
- AIHOT 分数：44
- AIHOT 链接：https://aihot.virxact.com/items/cmszieman0ev0rodpj84orfu2
- 原文链接：https://arxiv.org/abs/2608.17975

## AI 摘要

针对大语言模型编写3D程序时难以将高层意图转化为一致底层几何的问题，研究者联合设计了智能体中心领域特定语言aDSL与角色专业化多智能体系统。aDSL通过关系运算符替代绝对坐标，强调可组合性与空间推理；免训练系统采用Plan-Execute-Critic循环，利用执行反馈迭代修复错误。该方法在文本/图像生成3D形状任务上优于此前LLM基线，并支持铰接物体创建与结构化场景组合，代码已开源。

## 正文

Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit structural control. Yet, agentic workflows that rely on large language models (LLMs) to author 3D programs remain brittle, often failing to translate high-level intent into consistent low-level geometry. We attribute this fragility to a mismatch between existing programmatic interfaces and the reasoning strengths of LLMs, which favor semantic structure and spatial relations over fragile numeric choices. In this paper, we jointly design an Agent-centric Domain-Specific Language (aDSL) and a role-specialized multi-agent system to close this gap. aDSL bridges semantic logic and geometric constraints by emphasizing composability and spatial reasoning; it enables agents to manipulate geometry through relational operators instead of brittle absolute coordinates. Building on aDSL, our training-free multi-agent system follows a Plan-Execute-Critic loop to decompose requests, synthesize code, and iteratively repair errors and constraint violations using execution feedback. Experiments show that this co-design improves robustness, controllability, and faithfulness to user intent. Our method outperforms prior LLM-based baselines on text-to-shape and image-to-shape tasks while preserving explicit structure, editability, and interpretability. It also enables downstream applications such as articulated object creation and structured scene composition. Our code is available at https://github.com/sig-pku/aDSL.