HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation

TL;DR

HTAA introduces a hierarchical framework for large language models to efficiently plan and coordinate tool use, improving success rates and reducing operational costs in real-world applications.

Contribution

The paper presents a novel hierarchical toolset agentization paradigm and a trajectory-based training method for better LLM tool-use planning.

Findings

HTAA achieves higher task success rates on real-world datasets.

It reduces the length of tool calling trajectories.

It significantly lowers context overhead and operational costs.

Abstract

Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficiency and error accumulation inherent in flat tool-calling architectures. To address this, we propose Hybrid Toolset Agentization & Adaptation (HTAA), a hierarchical framework for scalable tool-use planning. We propose a novel toolset agentization paradigm, which encapsulates frequently co-used tools into specialized agent tools, thereby reducing the planner's action space and mitigating redundancy. To ensure effective coordination, we design Asymmetric Planner Adaptation, a trajectory-based training paradigm that aligns the high-level planner with agent tools via backward reconstruction and forward refinement. To validate the performance of HTAA, we conduct experiments on a real-world internal dataset, InfoVerify, based on the POI validation workflow of China's largest online large-scale ride-hailing platform, featuring long-horizon executable tool trajectories. Experiments on InfoVerify and widely-used benchmarks show that HTAA consistently achieves higher task success rates, requires short tool calling trajectories, and significantly reduces context overhead compared to strong baselines. Furthermore, in a production deployment, HTAA substantially reduces manual validation effort and operational cost, demonstrating its practical efficacy.