Open Access

ARTICLE

LEAF: A Lightweight Edge Agent Framework with Expert SLMs for the Industrial Internet of Things

Qingwen Yang¹, Zhi Li², Jiawei Tang¹, Yanyi Liu¹, Tiezheng Guo¹, Yingyou Wen^1,*

1 School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China
2 School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, China

* Corresponding Author: Yingyou Wen. Email:

(This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)

Computers, Materials & Continua 2026, 87(2), 30 https://doi.org/10.32604/cmc.2025.074384

Received 10 October 2025; Accepted 23 December 2025; Issue published 12 March 2026

Abstract

Deploying Large Language Model (LLM)-based agents in the Industrial Internet of Things (IIoT) presents significant challenges, including high latency from cloud-based APIs, data privacy concerns, and the infeasibility of deploying monolithic models on resource-constrained edge devices. While smaller models (SLMs) are suitable for edge deployment, they often lack the reasoning power for complex, multi-step tasks. To address these issues, this paper introduces LEAF, a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge. LEAF employs a novel architecture where multiple expert SLMs—specialized for planning, execution, and interaction—work in concert, decomposing complex problems into manageable sub-tasks. To mitigate the resource overhead of this multi-model approach, LEAF implements an efficient parameter-sharing scheme based on Scalable Low-Rank Adaptation (S-LoRA). We introduce a two-stage training strategy combining Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) to significantly enhance each expert’s capabilities. Furthermore, a Finite State Machine (FSM)-based decision engine orchestrates the workflow, uniquely balancing deterministic control with intelligent flexibility, making it ideal for industrial environments that demand both reliability and adaptability. Experiments across diverse IIoT scenarios demonstrate that LEAF significantly outperforms baseline methods in both task success rate and user satisfaction. Notably, our fine-tuned 4-billion-parameter model achieves a task success rate over 90% in complex IIoT scenarios, demonstrating LEAF’s ability to deliver powerful and efficient autonomy at the industrial edge.

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Keywords

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