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Beyond Entangled Planning: Task-Decoupled Planning for Long-Horizon Agents

Yunfan Li, Bingbing Xu, Xueyun Tian, Xiucheng Xu, Huawei Shen

arXiv:2601.07577v1cs.AI

TL;DR

Long-horizon planning is hindered by short-sighted or brittle paradigms and entangled contexts that increase cognitive load and propagate local errors. TDP decomposes tasks into a DAG and uses scoped Planner–Executor reasoning with local replanning. Across TravelPlanner, ScienceWorld, and HotpotQA, it improves robustness and efficiency, while its evaluation remains focused on verifiable objectives.

  • Problem

    Existing step-wise and one-shot planning methods use entangled contexts spanning multiple sub-tasks, increasing cognitive load and allowing local errors to propagate.

    Source: §1 · p. 1

  • Method

    TDP is a training-free framework that uses a Supervisor-built DAG and scoped Planner–Executor contexts to confine reasoning and replanning to active sub-tasks.

    Source: §1 · p. 2, §3 · p. 3, §3.1 · p. 3

  • Results

    TDP achieves superior or competitive performance across TravelPlanner, ScienceWorld, and HotpotQA while using significantly fewer tokens than prior planning methods.

    Source: §1 · pp. 2–3

  • Takeaways & Limitations

    Explicitly controlling planning scope balances global task coherence with local adaptive execution in complex environments.

    Source: §5 · p. 8

    The decomposition granularity may not always be optimized for execution efficiency, and evaluation has focused primarily on benchmarks with verifiable objectives.

    Source: Limitations · p. 9

Abstract

from arXiv · show

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.

1 Introduction

Long-horizon agent planning is limited by the trade-off between reactive but short-sighted step-wise methods and brittle one-shot plans, both of which entangle reasoning across sub-tasks. TDP addresses this with explicit task decoupling and shows stronger robustness and efficiency across three benchmarks.

1 Introduction

  • Step-wise planning reacts to feedback but can be short-sighted, whereas one-shot planning offers a global view but is brittle to execution errors.Both paradigms adjust planning granularity while retaining a largely monolithic workflow.Source: §1 · p. 1
  • Entangled histories impose cognitive load and allow local execution failures to propagate into independent decisions, causing unnecessary replanning.The paper attributes these limitations to tight coupling between sub-tasks rather than planning granularity itself.Source: §1 · pp. 1–2
  • TDP uses a Supervisor, Planner, and Executor to decompose tasks into a DAG, plan from node-relevant context, and replan locally after feedback.Execution feedback triggers replanning within one node without propagating to independent sub-tasks.Source: §1 · p. 2
  • TDP replaces monolithic planning with explicit task decoupling, separating task topology from execution logic.The framework localizes reasoning context and isolates off-plan outcomes during execution.Source: §1 · p. 2
  • TDP outperforms strong baselines in robustness and efficiency on TravelPlanner, ScienceWorld, and HotpotQA.The evaluation covers three diverse benchmarks and reports reduced token consumption compared with prior planning methods.Source: §1 · pp. 2–3

2 Related Work

Prior work spans prompted reasoning, interactive or global planning, feedback-driven refinement, and task decomposition. TDP combines decomposition with modular execution to isolate context and restrict replanning to affected sub-tasks.

2 Related Work

  • Classical planning and decision-making frameworks motivate structured approaches because LLMs remain imperfect as standalone planners.Related foundations include symbolic planning, policy learning, and collaborations between LLMs and classical methods.Source: §2 · p. 3
  • LLM agent planning includes prompted reasoning, action-interleaved methods, global planning, hierarchical structures, and feedback-driven refinement.Examples include CoT, ToT, GoT, ReAct, CodeAct, Pre-Act, Plan-and-Act, GoalAct, HiPlan, AdaPlanner, and MPO.Source: §2 · p. 3

Task Decomposition

  • Task decomposition methods divide targets into sub-tasks for execution planning or reasoning enhancement.Prior examples use LLM controllers, executable programs, specialized prompt handlers, or sequential subproblems.Source: Task Decomposition · p. 3
  • TDP combines task decomposition with modular agent design for sub-task-level decoupled planning and execution.Unlike fixed-granularity or implicit structures, it explicitly isolates context and confines replanning to affected regions.Source: Task Decomposition · p. 3

3 Method

TDP separates global task structuring from node-level planning and execution through a dependency graph, scoped contexts, local repair, and graph-level self-revision. This design preserves long-horizon coherence while limiting interference and unnecessary global replanning.

3 Method

  • TDP separates global task structuring from node-level decision making using a Supervisor, Planner–Executor pair, and Self-Revision module.The Supervisor builds the dependency graph, local modules execute dispatched nodes, and Self-Revision updates downstream specifications as execution unfolds.Source: §3 · p. 3

3.1 Motivation

  • Entangled histories make it difficult to focus on the active sub-task and can trigger broad replanning after local off-plan outcomes.TDP instead scopes each decision to the minimal affected region while preserving long-horizon coherence.Source: §3.1 · p. 3

3.2 Supervisor: Global Decomposition and Dependency-Aware Scheduling

  • The Supervisor decomposes objectives into semantically coherent DAG nodes, schedules ready nodes topologically, and constructs contexts from node specifications and predecessor outcomes.It also monitors node status after environment interactions and may provide macro-level guidance.Source: §3.2 · p. 4

3.3 Planner and Executor: Local Planning, Acting, and Local Repair

  • After dispatch, the Planner and Executor use only the current node specification, prerequisite outcomes, and the node’s interaction trace.The Planner creates intermediate objectives, while the Executor produces actions and incorporates observations step by step.Source: §3.3 · p. 4
  • When observations conflict with expected progress, replanning revises only the active node while leaving completed prerequisites and independent nodes unchanged.This confines recovery to the local execution trajectory rather than expanding its scope.Source: §3.3 · pp. 4–5

3.4 Self-Revision: Graph Maintenance and Specification Refinement

  • Self-Revision checks completed-node outcomes against graph assumptions and modifies unfinished nodes or dependencies when feasibility is threatened.It also propagates newly resolved information into dependent specifications, making subsequent goals more explicit.Source: §3.4 · p. 5
  • By restricting downstream modules to node-scoped context, TDP reduces cross-sub-task interference and avoids unnecessary global replanning during iterative execution.The resulting workflow anchors deviations to node-local recovery while enabling reliable long-horizon progress.Source: §3.4 · p. 5

4 Experiment

Across three heterogeneous long-horizon benchmarks, TDP remains stable or competitive while improving outcomes and sharply reducing replanning overhead. Its experiments evaluate scoped execution, localized recovery, and context isolation against established planning baselines.

4 Experiment

  • The Supervisor decomposes tasks into a DAG, while Planner and Executor operate on node-relevant context and localized replanning updates only the active sub-task.Algorithm 1 includes ready-node dispatch, node-local execution, localized replanning, and self-revision of the DAG.Source: §4 · p. 5

4.2 Experiment Results

  • TDP remains stable across all three benchmarks, improving completed-trajectory quality where global structure and constraint satisfaction matter while staying competitive in step-wise settings.The evaluation covers TravelPlanner, HotpotQA, and ScienceWorld under DeepSeek-V3.2 and GPT-4o.Source: §4.1 · pp. 5–6, §4.2 · p. 6
  • TDP achieves the best TravelPlanner Avg under DeepSeek-V3.2 (34%) and GPT-4o (30%), while retaining comparable delivery and strong constraint performance.It also reaches the highest hard-constraint micro pass rate under DeepSeek-V3.2 (32.5%) and strongest hard-constraint macro performance (10.83%).Source: §4.2 · p. 6
  • TDP achieves delivered-answer quality of 85.88% under DeepSeek-V3.2 and 82.14% among plan-based baselines under GPT-4o on HotpotQA.It improves Accurate Delivery over ReAct and CoT while remaining competitive with ReAct in overall answer accuracy.Source: §4.2 · p. 7
  • TDP achieves the best ScienceWorld average reward under GPT-4o (53.24) and consistently outperforms CoT and Plan-and-Act across both models.It preserves step-wise execution while confining deviations to local recovery, supporting closed-loop control.Source: §4.2 · p. 7

4.3 Cost Comparison

  • TDP reduces average output tokens by 82.4% and 82.1% on HotpotQA and 74.5% and 70.4% on ScienceWorld relative to Plan-and-Act.It also delivers stronger outcomes across both benchmarks and base models, improving delivery accuracy on HotpotQA and average reward on ScienceWorld.Source: §4.3 · pp. 7–8, Figure 3 · p. 7

4.4 Case Study

  • In the TravelPlanner case, independent flight and accommodation nodes share prerequisites without inheriting one another’s execution traces, and a flight failure triggers replanning only within its node.This isolates irrelevant context and allows unaffected sub-tasks to proceed without interruption.Source: §4.4 · p. 8

5 Conclusion

TDP separates global task decomposition and supervision from scoped sub-task execution for long-horizon tasks. Experiments indicate that controlling planning scope improves robustness, adaptability, context focus, and resistance to error propagation.

5 Conclusion

  • TDP separates global task decomposition and supervision from sub-task-level decoupled execution, mitigating error propagation through strict scope constraints.The conclusion presents explicit planning-scope control as a design principle balancing global awareness with local adaptive execution.Source: §5 · p. 8

Limitations

The framework shows strong stability, but its decomposition granularity and benchmark scope remain constrained.

  • Sub-task granularity is not always optimized for execution efficiency.Source: Limitations · p. 9
  • Evaluation primarily covers benchmarks with verifiable objectives, leaving open-ended real-world tasks for future exploration.Source: Limitations · p. 9

Ethical Considerations

The method targets planning and execution efficiency and robustness through architectural design rather than new decision objectives or value preferences.

  • TDP improves planning and execution capabilities through architectural design, without introducing new decision objectives or value preferences.Source: Ethical Considerations · p. 9
  • The method does not directly involve specific social value judgments or sensitive decision-making scenarios.Source: Ethical Considerations · p. 9

A Details of Benchmarks

The evaluation uses three complementary interactive benchmarks: constrained travel planning, multi-hop Wikipedia question answering, and scientific experimentation.

HotpotQA

ScienceWorld

TravelPlanner

  • TravelPlanner evaluates multi-turn travel planning under transportation, meals, attractions, accommodation, and other constraints.Source: A Details of Benchmarks · pp. 11–12
  • TravelPlanner supplies search, distance, accommodation, restaurant, attraction, city, notebook, and plan-generation tools.Source: A Details of Benchmarks · p. 11
  • NotebookWrite stores intermediate travel information, while MakePlan generates a detailed plan from the query and notebook contents.Source: A Details of Benchmarks · p. 11

B Implementation Details

The experiments impose fixed instance counts and maximum interaction steps for each benchmark.

  • HotpotQA uses 100 development instances with a 30-step limit, ScienceWorld uses 100 test instances with a 60-step limit, and TravelPlanner uses 120 validation tasks with a 50-step limit.Source: B Implementation Details · p. 12

C Prompt Templates

The prompt templates operationalize a Supervisor–Planner–Executor architecture for decomposing tasks into executable DAG subgoals, planning and executing each node, evaluating progress, replanning when necessary, and updating the DAG. Scoped prompts emphasize focused subgoal reasoning, efficient action selection, minimal replanning, and complete task coverage through a final action node.

DAG Construction Prompt for Supervisor

  • The Supervisor decomposes tasks into executable semantic-level subgoals with dependencies, constructing a DAG that supports direct task completion.Subgoal descriptions must be clear enough for the Planner and Executor, and dependencies define execution order.Source: C Prompt Templates · p. 13

Plan Generation Prompt for Planner

  • The Planner generates concise, high-level, step-by-step plans for only the active subgoal, using available actions and relevant history.Plans specify what to accomplish rather than how to execute it, while avoiding redundant actions and unnecessary steps.Source: C Prompt Templates · pp. 13–14

Executor Prompt

  • The Executor converts the current plan into one immediate action, tracking completed steps through history and prioritizing Supervisor guidance.Its output is restricted to an action string selected from the available actions.Source: C Prompt Templates · pp. 14–15

Evaluate Execution Result Prompt for Supervisor

  • The Supervisor evaluates execution histories to classify subgoals as completed, failed, or needing more steps, while supplying next-action guidance when continuation is appropriate.Replanning is minimized and triggered only by repeated failures, critical obstacles, or missing essential steps.Source: C Prompt Templates · p. 15

Replanning Prompt for Planner

  • The Planner revises a node plan when Supervisor feedback reveals a fundamental problem such as a wrong approach, invalid parameters, or missing prerequisites.Otherwise, it preserves the current plan and returns a structured decision indicating whether replanning is required.Source: C Prompt Templates · pp. 15–16

DAG Updating Prompt for Supervisor

  • The Supervisor updates the DAG by refining pending or active descriptions, adding necessary nodes, and removing unnecessary or impossible nodes while preserving complete task coverage.The DAG must remain efficient, avoid overlapping or redundant subgoals, and always contain a sink node representing the final action.Source: C Prompt Templates · pp. 13, 16, 17
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