mlsanityData to 7 Oct 2026

SemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation

arXiv 2608.18565 ↗First posted 19 Aug 2026cs.SEcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC project and then runs correctly has been checked only in limited tests. We present textscSemaPLC, a project-grounded and verification-gated agent harness assembled from conventional tools but governed by a strict completion rule. Rather than stopping when the model judges its own output adequate, textscSemaPLC declares a task complete only when logged external checks confirm it. Those checks cover the specification, the compilation, and the behavior on a live runtime. On 117 independent-POU tasks matching existing benchmarks, it attains the highest strict verified pass rate on all seven models (72.6% mean). On a project-context track of 65 tasks whose generated logic must compile and run inside a real project, it attains the highest mean on integrated compilation, static behavior, and dynamic behavior. Of the three layers, dynamic behavior is the most revealing. We measure it by deploying the generated and the reference logic to a live PLC runtime and comparing their executed traces. All methods fall within 10 static points of one another, whereas dynamic scores separate them sharply, from 22.4 to 31.4 for the baselines against 52.2 for textscSemaPLC. Overall, our verification-gated harness raises the mean at every layer and most sharply at runtime. Execution, not static scoring, is the faithful test of whether generated control logic actually works. textscSemaPLC is open-sourced at https://github.com/midea-ai/SemaPLC.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 25 references that are in the map. First posted on arXiv 19 Aug 2026.

Builds on

9 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026Sema Code: Decoupling AI Coding Agents into Programmable, Embeddable Infrastructure13 Apr
  2. 2026GLM-5: from Vibe Coding to Agentic Engineering17 Feb

Other references · 7

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  3. 2023Teaching Large Language Models to Self-Debugcited by 963
  4. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  5. 2026The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligencecited by 50
  6. 2026MiniMax Sparse Attentioncited by 34
  7. 2025Spec2Control: Automating PLC/DCS Control-Logic Engineering from Natural Language Requirements with LLMs - A Multi-Plant Evaluationcited by 3

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