TaReD: Tool-Aware Recursive Decomposition for Long-Horizon Tasks

2026-10-08

AI Summary

This study addresses the challenges of cumulative error propagation in planning and the difficulty of retrieving from large-scale tool libraries when agents execute long-horizon tasks. To this end, we propose a tool-aware recursive decomposition method. The core innovation lies in constructing a hierarchical structure of tool capabilities to recursively decompose complex tasks into subtask trees. This architecture enables dynamic alignment between subtasks and available tools alongside on-demand retrieval, thereby optimizing context utilization efficiency and effectively mitigating error propagation. Experimental results demonstrate that the proposed approach improves the end-to-end success rate by up to 40 percentage points over baseline methods on complex real-world tasks.

Abstract

Agents combine reasoning with tools to interact with external systems and complete real-world tasks. Early agents typically interleave reasoning and actions along a single execution chain. On complex tasks, this chain becomes unreliable because growing histories obscure intermediate dependencies and allow early planning errors to propagate. Recursively decomposing a complex task into smaller subtasks offers a natural solution, yet effective decomposition must account for the system's capabilities so that each subtask can be executed by the available tools. In realistic systems, however, tool libraries can be too large to expose in full. Injecting every tool description consumes substantial context while making relevant tools harder to retrieve and useful task boundaries harder to identify. We propose tool-aware recursive decomposition, which organizes tools by functional relationships into a hierarchy of capabilities. During execution, the agent discovers tools on demand and uses the hierarchy to recursively decompose a complex task into a subtask tree whose levels are aligned with the capabilities required at each stage. Experiments on complex real-world tasks show that the proposed method improves end-to-end task success rate by up to 40 percentage points over the compared baselines. The implementation of TaReD is available on GitHub: https://github.com/WeiXiang-Mao/TaReD.

Problem

• long-horizon tasks
• task decomposition
• tool-aware agents
• large tool libraries
• error propagation

Innovation

• Tool-Aware Recursive Decomposition
• Long-Horizon Tasks
• Hierarchical Tool Organization
• Subtask Tree
• On-Demand Tool Discovery