Verication-driven closed-loop multi-agent large language modelframework for code-compliant structural design

arXiv:2608.07978 · cs.SE, cs.AI · Submitted 2026-08-08 · Read on arXiv

Jianbin Luo, Weibin Lin, Yiran Lin, Qing Wei, Wei Guo

cs.SE, cs.AI

Submitted: 2026-08-08

Updated: 2026-08-11

Comments: 20 pages, 21 figures

Code: https://github.com/fzuKilo/structural-design-system

License: http://creativecommons.org/licenses/by/4.0/

The gist: Multi-agent large language model(LLM)systems are applied to structural design,yet most use one-shot generation and cannot verify their output,leaving themill-suited to safety-critical tasks.Rather

Terminology

Abstract

Multi-agent large language model(LLM)systems are applied to structural design,yet most use one-shot generation and cannot verify their output,leaving themill-suited to safety-critical tasks.Rather than trusting LLM self-correction,thisframework injects feedback from an external physics-based verier into a closedrepair loop.The framework couples a three-layernite-element verication systemwith a dual-node loop.Node 1 turns code violations into hard repair constraints,Node 2 turns a four-dimensional quality score into safety-rst soft constraints,and a retrieval-augmented code base makes every violation traceable to a clause.Overve structure types and 44 cases,code compliance rises from 56.8%to 98.6%and the composite score from 63.8 to 71.4(p<0.000001),using about 5.8%lessmaterial.Removing either node degrades performance,and compliance does notchange detectably across the two backbone LLMs tested,indicating that it ishere attributed to the external verier rather than the model.The framework,the 44-case benchmark and all experiment scripts are released as open source forreplicability.

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