mlsanityData to 7 Oct 2026

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

arXiv 2608.07978 ↗First posted 8 Aug 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 42 references that are in the map. First posted on arXiv 8 Aug 2026.

Builds on

14 of 42 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026A Novel Multi-Agent Architecture to Reduce Hallucinations of Large Language Models in Multi-Step Structural Modeling8 Mar
  2. 2025Automating Structural Engineering Workflows with Large Language Model Agents13 Oct
  3. 2025A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis6 Oct
  4. 2025A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis27 Jun

Other references · 10

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  4. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 965
  5. 2023Teaching Large Language Models to Self-Debugcited by 963
  6. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  7. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
  8. 2025GraphCompliance: Aligning Policy and Context Graphs for LLM-Based Regulatory Compliancecited by 9
  9. 2023Opportunities for Large Language Models and Discourse in Engineering Designcited by 6
  10. 2026Automating Structural Analysis Across Multiple Software Platforms Using Large Language Modelscited by 2

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  1. 2026StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
  2. 2024Collaboration Dynamics and Reliability Challenges of Multi-Agent LLM Systems in Finite Element Analysis
  3. 2026Agentic Large Language Models for Automated Structural Analysis of 3D Frame Systems
  4. 2024Large Language Model Agent as a Mechanical Designer
  5. 2026VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
  6. 2026ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods
  7. 2026Automating structural reliability analysis with a multi-agent large language model framework
  8. 2026Verification as an Architectural Layer for LLM Agents: A V-Model Design, and a Pilot Study of Its Deterministic Core