aDSL: Agentic 3D Creation via Joint Agent-Program Design

• URL: http://arxiv.org/abs/2608.17975v1
• Date: Tue, 18 Aug 2026 16:27:44 GMT
• Title: aDSL: Agentic 3D Creation via Joint Agent-Program Design
• Abstract summary: Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit structural control.<n>Yet, agentic 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.<n>We jointly design an Agent-centric Domain Language (aSpecific) and a role-specialized multi-agent system to close this gap.
• Score: 27.28805461709447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
  We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
  arXiv  Detail & Related papers  (2026-09-04T23:32:11Z)
• Agentic Large Language Models for Automated Structural Analysis of 3D Frame Systems [9.941684692042632]
  Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains.<n>In structural engineering, recent efforts have developed agentic LLMs for automated analysis of plane frames.<n>This paper proposes an agentic LLM framework for automated structural analysis of 3D frames from natural inputs.
  arXiv  Detail & Related papers  (2026-06-02T20:34:11Z)
• The Semantic Training Gap: Ontology-Grounded Tool Architectures for Industrial AI Agent Systems [0.0]
  Large language model (LLM)-based AI agents are increasingly deployed in manufacturing environments for analytics, quality management, and decision support.<n>These agents demonstrate statistical fluency with domain terminology but lack grounded understanding of operational semantics.<n>This paper identifies and formalizes the semantic training gap: a structural disconnect between how AI systems acquire domain vocabulary through training and how manufacturing operations define meaning through ontological relationships.
  arXiv  Detail & Related papers  (2026-05-11T20:48:18Z)
• Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD [22.163726324892256]
  We propose a hierarchical and geometry-aware graph as an intermediate representation.<n>Instead of mapping text directly to code, our framework first predicts structure and constraints, then conditions action sequencing and code generation.<n>Our method consistently outperforms existing approaches in both geometric fidelity and accurate satisfaction of geometric constraints.
  arXiv  Detail & Related papers  (2026-04-11T07:49:16Z)
• Agentic Planning with Reasoning for Image Styling via Offline RL [66.10749901925941]
  Direct prompt-based editing fails on complex transformations because vague and subjective prompts often require nuanced understanding of what should be changed in the image.<n>We present a tool-based agentic RL post-training framework that addresses this through structured planning with chain-of-thought reasoning.
  arXiv  Detail & Related papers  (2026-03-07T11:14:37Z)
• Theory of Code Space: Do Code Agents Understand Software Architecture? [0.0]
  Code agents excel at isolated tasks yet struggle with multi-file software engineering requiring architectural understanding.<n>We introduce Theory of Code Space (ToCS), a benchmark that evaluates whether agents can construct, maintain, and update coherent architectural beliefs during AI exploration.
  arXiv  Detail & Related papers  (2026-02-28T11:40:17Z)
• Zero-shot 3D Map Generation with LLM Agents: A Dual-Agent Architecture for Procedural Content Generation [8.398818816613806]
  We propose a training-free architecture that utilizes LLM agents for zero-shot PCG parameter configuration.<n>Our system pairs an Actor agent with a Critic agent, enabling an iterative workflow where the system autonomously reasons over tool parameters.
  arXiv  Detail & Related papers  (2025-12-11T10:22:02Z)
• Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework [54.447408954009454]
  Physics-informed neural networks (PINNs) provide a powerful approach for solving partial differential equations (PDEs)<n>We present Lang-PINN, an LLM-driven multi-agent system that builds trainable PINNs directly from natural language task descriptions.<n>Experiments show that Lang-PINN achieves substantially lower errors and greater robustness than competitive baselines.
  arXiv  Detail & Related papers  (2025-10-03T08:20:02Z)
• DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis [76.7196710324494]
  3D indoor layout synthesis is crucial for creating virtual environments.<n>DisCo is a novel framework that disentangles and coordinates physical and semantic refinement.
  arXiv  Detail & Related papers  (2025-10-02T16:30:37Z)
• MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds [50.98900790623827]
  MeshCoder is a novel framework that reconstructs complex 3D objects from point clouds into editable Blender Python scripts.<n>We train a multimodal large language model (LLM) that translates 3D point cloud into executable Blender Python scripts.<n>Our approach achieves superior performance in shape-to-code reconstruction tasks and also facilitates intuitive geometric and topological editing.
  arXiv  Detail & Related papers  (2025-08-20T17:50:15Z)
• Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams [54.528185120850274]
  We propose a novel step-by-step code generation framework named API2Dep.<n>First, we introduce an enhanced Unified Modeling Language (UML) API diagram tailored for service-oriented architectures.<n>Second, recognizing the critical role of data flow, we introduce a dedicated data dependency inference task.
  arXiv  Detail & Related papers  (2025-08-05T12:28:23Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [91.17994756436259]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.