Recent advances in large language models (LLMs) have enabled agents to autonomously execute complex, long-horizon tasks, yet planning remains a primary bottleneck for reliable task execution. Existing methods typically fall into two paradigms: step-wise planning, which is reactive but often short-sighted; and one-shot planning, which generates a complete plan upfront yet is brittle to execution errors. Crucially, both paradigms suffer from entangled contexts, where the agent must reason over a monolithic history spanning multiple sub-tasks. This entanglement increases cognitive load and lets local errors propagate across otherwise independent decisions, making recovery computationally expensive. To address this, we propose Task-Decoupled Planning (TDP), a training-free framework that replaces entangled reasoning with task decoupling. TDP decomposes tasks into a directed acyclic graph (DAG) of sub-goals via a Supervisor. Using a Planner and Executor with scoped contexts, TDP confines reasoning and replanning to the active sub-task. This isolation prevents error propagation and corrects deviations locally without disrupting the workflow. Results on TravelPlanner, ScienceWorld, and HotpotQA show that TDP outperforms strong baselines while reducing token consumption by up to 82%, demonstrating that sub-task decoupling improves both robustness and efficiency for long-horizon agents.

翻译：近年来，大型语言模型（LLM）的进展使得智能体能够自主执行复杂的长视野任务，然而规划仍然是可靠任务执行的主要瓶颈。现有方法通常分为两种范式：逐步规划，具有反应性但往往目光短浅；以及一次性规划，预先生成完整计划但对执行错误非常脆弱。关键的是，这两种范式都受到纠缠上下文的困扰，即智能体必须在跨越多个子任务的单一历史记录上进行推理。这种纠缠增加了认知负荷，并使局部错误在原本独立的决策中传播，导致恢复的计算成本高昂。为解决这一问题，我们提出了任务解耦规划（TDP），这是一种无需训练的框架，通过任务解耦取代纠缠式推理。TDP通过一个监督器将任务分解为有向无环图（DAG）形式的子目标。利用具有范围上下文的规划器和执行器，TDP将推理和重新规划限制在活跃子任务内。这种隔离防止了错误传播，并在不中断工作流的情况下局部纠正偏差。在TravelPlanner、ScienceWorld和HotpotQA上的实验结果表明，TDP优于强基线方法，同时将令牌消耗降低了高达82%，证明了子任务解耦能同时提升长视野智能体的鲁棒性和效率。