The paper presents a proof‑of‑concept workflow that uses an AI agent to translate natural‑language prompts into early‑stage structural models. By connecting the agent to a COMPAS‑based structural modelling API via a Model Context Protocol server, the system generates and edits a graph‑based structural data model with explicit building elements. The resulting artifacts can be inspected, modified, serialized, and integrated into downstream AEC workflows, demonstrating the feasibility of combining natural language, agentic workflows, and domain‑specific structural data structures for AI‑assisted structural co‑design.
arXiv:2607. 15845v2 Announce Type: replace Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.
arXiv:2607. 15845v1 Announce Type: new Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.
arXiv:2606. 06525v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains.
The paper presents a framework of formal operations for assembling context in large language model (LLM)-based engineering design, involving modular context units such as policy prompts, reference units with persistence, and user questions with prompt vectoring. It also introduces a formal method for evaluating modelling-as-code LLM outputs, assessing compliance to intent from LLM answers and the support LLMs provide for systems architecture modelling.
AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.
Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains or resolve latent spatial dependencies across multiple building entities.
arXiv:2607. 09616v1 Announce Type: cross Abstract: As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development.
arXiv:2606. 20146v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions.
EvoDesign is an agentic framework that enables editable diagram creation by constructing a diagram manifest through coordinated structure, style, and layout agents, then rendering it in a diagnostic verification environment that provides defect signals and VLM-based critique. The system uses an evolution mechanism to distill refinement traces into a three-tier design expertise memory, promoting strategies only with repeated cross-context support. CanvasBench, a new benchmark and evaluation protocol, assesses content, visual, and cognitive dimensions, and experiments show EvoDesign achieves a strong balance across content fidelity, visual organization, and cognitive usability compared to pixel, code-based, agentic, one-shot schema, and canvas-native methods.
arXiv:2609.23386v1 Announce Type: new Abstract: Text-guided 3D building generation holds tremendous application potential, yet existing generative models typically output inseparable single meshes or...
Design-to-Plan is a large language model–based multi‑agent framework that automates end‑to‑end manufacturing process planning from 3D CAD models and 2D engineering drawings. The system uses an orchestrator to coordinate specialized agents for feature recognition, drawing analysis, context fusion, knowledge retrieval, process sequencing, tool selection, and report generation, integrating deterministic modules with LLM reasoning. Evaluation on 300 benchmark cases shows high success rates, strong tool selection accuracy, effective conflict detection, and reduced token usage, demonstrating the framework’s ability to produce consistent, traceable design‑to‑plan outputs.
arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.