ReAcTree: Hierarchical LLM Agent Trees with Control Flow for Long-Horizon Task Planning

📝 Original Info

• Title: ReAcTree: Hierarchical LLM Agent Trees with Control Flow for Long-Horizon Task Planning
• ArXiv ID: 2511.02424
• Date: 2025-11-04
• Authors: - 논문에 명시된 저자 정보가 제공되지 않았습니다. (일반적으로 논문 PDF 혹은 arXiv 페이지에서 확인 가능)

📝 Abstract

Recent advancements in large language models (LLMs) have enabled significant progress in decision-making and task planning for embodied autonomous agents. However, most existing methods struggle with complex, long-horizon tasks because they rely on a monolithic trajectory that entangles all past decisions and observations to solve the entire task in a single unified process. To address this limitation, we propose ReAcTree, a hierarchical task-planning method that decomposes a complex goal into manageable subgoals within a dynamically constructed agent tree. Each subgoal is handled by an LLM agent node capable of reasoning, acting, and further expanding the tree, while control flow nodes coordinate the execution strategies of agent nodes. In addition, we integrate two complementary memory systems: each agent node retrieves goal-specific, subgoal-level examples from episodic memory and shares environment-specific observations through working memory. Experiments on the WAH-NL and ALFRED show ReAcTree consistently outperforms strong task-planning baselines such as ReAct across diverse LLMs. Notably, on WAH-NL, ReAcTree achieves a 61% goal success rate with Qwen 2.5 72B, nearly doubling ReAct's 31%. The code is available at https://github.com/Choi-JaeWoo/ReAcTree.git.

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