[Submitted on 18 Jan 2026 (v1), last revised 14 Oct 2026 (this version, v3)]
Abstract:To lower the expertise barrier in machine learning, the AutoML community has focused on the CASH problem, which jointly automates algorithm selection and hyperparameter tuning. While traditional methods like Bayesian Optimization (BO) struggle with cold-start issues, Large Language Models (LLMs) can mitigate these through semantic priors. However, existing LLM-based optimizers generalize poorly to high-dimensional, structured CASH spaces. CASH requires coordinated exploration and exploitation both across candidate algorithms and within their algorithm-specific hyperparameter spaces. Yet existing approaches often rely on flat optimization histories that mix observations across algorithms and obscure configuration-refinement trajectories. In this paper, we propose LB-MCTS, a trajectory-structured optimization framework that uses a Monte Carlo Tree Search tree as a shared state for algorithm selection, hyperparameter refinement, and BO-LLM proposer synergy. Within this shared state, BO provides algorithm-specific surrogate modeling for quantitative search, while the LLM exploits path-aware selective memory to generate semantic proposals and reflections. As the surrogate model improves, a reliability-aware proposer policy adaptively shifts from LLM-driven to BO-driven proposals within a unified search trajectory. Experiments on 104 AMLB datasets demonstrate that LB-MCTS consistently outperforms BO-based, LLM-based, and hybrid baselines. Code: this https URL.
Submission history
From: Beicheng Xu [view email]
[v1] Sun, 18 Jan 2026 11:26:31 UTC (3,841 KB)
[v2] Wed, 6 May 2026 21:35:23 UTC (3,923 KB)
[v3] Wed, 14 Oct 2026 16:07:08 UTC (3,892 KB)
