HTN-Orchestrate：分层任务网络多智能体编排框架
HTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration
arXiv 上新出的多智能体编排框架，用分层任务网络做规划，同一模型下在 BrowseComp 上比 Agent Team 高 2.45 个点，做智能体的可以看看思路。
arXiv 论文提出 HTN-Orchestrate，一个两层多智能体编排框架。Planner 负责维护子任务层级和依赖关系，把任务分解推迟到前置结果可用时再进行；局部 Orchestrator 调度 worker 执行叶子子任务。在 BrowseComp、SUPERChem 等 5 个基准上，同一基础模型下该框架超过 ReAct、Agent Team 和 DeepSeek Harness，BrowseComp 准确率达 89.73%，SUPERChem 达 73.44%，分别比 Agent Team 高 2.45 和 4.69 个百分点。
HTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration
Orchestrating multiple agents on a complex task requires reconciling local subtask execution with global task planning. Dynamic dispatch approaches provide execution flexibility but do not explicitly model subtask hierarchies or dependencies, while predefined workflows impose this structure but fix the decomposition before execution begins. Therefore, we introduce HTN-Orchestrate, a two-layer framework that separates these responsibilities through a hierarchical task network (HTN). The Planner maintains subtask hierarchies and dependencies, deferring decomposition until prerequisite results become available, while local Orchestrators schedule workers to execute each leaf subtask. Results propagate upward under \textbf{AND} semantics, and accumulated findings inform a rebuilt task network when the current decomposition cannot resolve the task. On five benchmarks spanning web research, scientific reasoning, and research workflows, HTN-Orchestrate outperforms ReAct, Agent Team, and DeepSeek Harness using the same base model, achieving 89.73% accuracy on BrowseComp and 73.44% on SUPERChem, exceeding Agent Team by 2.45 and 4.69 percentage points, respectively.
