A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models

Key points

• Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usually manual.1
• This paper addresses the automatic tuning of these components and proposes a framework transferable across models of different nature.1
• Unlike systems that optimize the entire ML script or search architectures in a discrete space, the framework operates over an open but semantically narrow space: the agent generates code only for the specified components, leaving the overall network structure unchanged.1
• The approach is validated in two directions: tuning a classification spiking neural network with correlation learning, and solving high-order differential equations with Physics-Informed Neural Networks (PINN).1

Sources (1)

• [1]A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 02:34 PM

  The multi-agent large language model (LLM) framework has three roles---analyst, coder, and recorder ---with an experiment log and a sandboxed execution environment.

  Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usually manual.

Extractive summary: sentences quoted from the sources.