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New Tool Creates Vast Reservoir of Realistic Machine Learning Tasks

San Francisco, CA – October 8, 2025 – Researchers have developed an automated system called MLE-Smith that can generate a massive and diverse collection of machine learning engineering (MLE) tasks, overcoming a significant bottleneck in the development of advanced AI agents. Current MLE benchmarks often rely on manually curated tasks, which are time-consuming to create, limited in scalability, and may not fully reflect real-world complexities.

MLE-Smith addresses these limitations with a novel “generate-verify-execute” pipeline. This system transforms raw datasets into competition-style MLE challenges, ensuring each task is structurally sound, semantically coherent, and practically solvable by AI agents.

For example, imagine a raw dataset containing information about different electric vehicles, including their specifications and performance metrics. MLE-Smith could take this raw data and, through its automated pipeline, generate a task that challenges an AI agent to predict a vehicle’s range based on its features. This task would not only require the AI to process the raw data and build a predictive model but also to adhere to specific evaluation metrics and data formatting requirements, mimicking a real-world machine learning competition.

The system employs a multi-agent approach:

  • Brainstormer: This agent explores a given dataset and proposes multiple potential task formulations, considering various learning objectives and modeling strategies. It aims to identify diverse and meaningful challenges that can be derived from the data.
  • Designer: Taking a proposed task formulation, this agent instantiates a complete, end-to-end executable MLE task. This involves defining data preprocessing, creating training and testing splits, specifying input/output schemas, and generating evaluation scripts.
  • Refactor: This agent standardizes all generated tasks into a unified format, ensuring consistency across the entire benchmark.

A crucial aspect of MLE-Smith is its robust “hybrid verification mechanism.” This multi-layered approach includes:

  • Assertions: These are deterministic checks that ensure structural integrity, verifying file formats, directory layouts, and adherence to defined schemas.
  • Reviews: An LLM-based agent reviews the tasks for semantic soundness, assessing clarity of descriptions, appropriateness of metrics, and whether the task encourages genuine AI agent behavior.
  • Execution-based Validation: The system runs each generated task within an interactive MLE environment to empirically validate its solvability and ensure it provides meaningful signals for AI agent performance.

The researchers applied MLE-Smith to 224 real-world datasets, successfully generating 606 diverse tasks. These tasks span a wide range of data modalities (e.g., tabular, image, audio), learning objectives (e.g., classification, regression), and domains (e.g., healthcare, sports).

Evaluations using cutting-edge Large Language Models (LLMs) on these newly generated tasks showed a strong correlation with their performance on existing, human-designed benchmarks. This indicates that MLE-Smith can effectively scale the creation of MLE tasks while maintaining their quality, realism, and discriminative power, paving the way for more robust evaluation and development of future AI agents.