# Automated machine learning

A multi-agent large language model framework confines neural network tuning to designated functional blocks while the overall network structure stays fixed. Such key components were previously chosen by hand, and existing agent systems either optimize the entire machine learning script or build a network from a text description. With analyst, coder, and recorder roles plus a sandboxed environment and an experiment log, the framework beats its baselines on five tasks: MNIST classification accuracy rises 13 percentage points, digits 6 points, the relative error on the single-soliton generalized nonlinear Schrödinger equation drops about 2491-fold, the conservation error drops about 325-fold in the selected run, and mass drift on the Kuramoto–Sivashinsky problem drops 14-fold.

A multi-agent large language model framework confines neural network tuning to designated functional blocks while the overall network structure stays fixed. Such key components were previously chosen by hand, and existing agent systems either optimize the entire machine learning script or build a network from a text description. With analyst, coder, and recorder roles plus a sandboxed environment and an experiment log, the framework beats its baselines on five tasks: MNIST classification accuracy rises 13 percentage points, digits 6 points, the relative error on the single-soliton generalized nonlinear Schrödinger equation drops about 2491-fold, the conservation error drops about 325-fold in the selected run, and mass drift on the Kuramoto–Sivashinsky problem drops 14-fold.

The work presents CleanSurvival, a reinforcement-learning (Q-learning) based automated data-preprocessing framework tailored to time-to-event (survival analysis) models for censored data: it selects among combinations of data imputation, outlier detection, and feature extraction techniques to optimize performance for a Cox, random forest, neural network, or user-supplied time-to-event model; on real-world dataset benchmarks, this Q-learning-based preprocessing improved predictive performance relative to simple baselines, runtime behavior was condition-dependent and most clearly interpretable in the best-covered benchmark cells, and a simulation study showed effectiveness across different types and levels of missingness and noise.

The work presents CleanSurvival, a reinforcement-learning (Q-learning) based automated data-preprocessing framework tailored to time-to-event (survival analysis) models for censored data: it selects among combinations of data imputation, outlier detection, and feature extraction techniques to optimize performance for a Cox, random forest, neural network, or user-supplied time-to-event model; on real-world dataset benchmarks, this Q-learning-based preprocessing improved predictive performance relative to simple baselines, runtime behavior was condition-dependent and most clearly interpretable in the best-covered benchmark cells, and a simulation study showed effectiveness across different types and levels of missingness and noise.