# Vendor-Aware Industrial Agents: RAG-Enhanced LLMs for Secure On-Premise PLC Code Generation

arXiv: https://arxiv.org/abs/2511.09122 · HTML: https://mlsanity.com/paper/2511.09122/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 8 of 8)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://mlsanity.com/paper/2410.14209/) (2024)
- [Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation](https://mlsanity.com/paper/2508.14066/) (2025)
- [Spec2Control: Automating PLC/DCS Control-Logic Engineering from Natural Language Requirements with LLMs - A Multi-Plant Evaluation](https://mlsanity.com/paper/2510.04519/) (2025)
- [Training LLMs for Generating IEC 61131-3 Structured Text with Online Feedback](https://mlsanity.com/paper/2410.22159/) (2024)
- [AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers](https://mlsanity.com/paper/2412.02410/) (2024)

## Built on by (most cited first, 1 of 1)

- [RAG-Based Auto-Configuration for Industrial Fieldbus Devices](https://mlsanity.com/paper/2608.08618/) (2026)

## Abstract (arXiv)

Programmable Logic Controllers are operated by proprietary code dialects; this makes it challenging to train coding assistants. Current LLMs are trained on large code datasets and are capable of writing IEC 61131-3 compatible code out of the box, but they neither know specific function blocks, nor related project code. Moreover, companies like Mitsubishi Electric and their customers do not trust cloud providers. Hence, an own coding agent is the desired solution to cope with this. In this study, we present our work on a low-data domain coding assistant solution for industrial use. We show how we achieved high quality code generation without fine-tuning large models and by fine-tuning small local models for edge device usage. Our tool lets several AI models compete with each other, uses reasoning, corrects bugs automatically and checks code validity by compiling it directly in the chat interface. We support our approach with an extensive evaluation that comes with code compilation statistics and user ratings. We found that a Retrieval-Augmented Generation (RAG) supported coding assistant can work in low-data domains by using extensive prompt engineering and directed retrieval.