SOURCE-LINKED INTELLIGENCE

Agentic Search Spaces for Tabular Machine Learning

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains underexplored. In this paper, we investigate a concrete use case: whether state-of-the-art agentic AI systems can design extended HPO search spaces for established tabular models that outperform the standard search spaces provided by the model authors. Specifically, we represent each tabular model as a modular pipeline covering preprocessing, embeddings, architecture, training, and inference. We then task the agent to propose candidate code imp

Read original source ↗ Open in workspace

recordType

paper

region

Global