AI 中文总结
HyperAgent是一种基于工具-模式超图的LLM智能体框架，通过构建相关图引导任务规划与执行，在AppWorld实验中提升了任务完成性能并降低了资源消耗。
AI 中文摘要
大语言模型（LLM）智能体越来越依赖外部工具完成复杂的现实世界任务。然而，由于隐含推理的局限性以及现实执行环境的动态性，可靠的工具使用规划仍然具有挑战性。现有的工具使用智能体通常依赖LLM从文本描述中推断工具组合，这会在复杂任务中导致低效探索和不可靠执行。为应对这些挑战，我们在模式层对工具关系进行建模，构建了有向工具-模式超图，其中工具被表示为从其所需输入模式节点到输出模式节点的超边。此外，我们提出了HyperAgent，这是一种由工具-模式超图引导的动态规划与执行框架。给定一个任务，HyperAgent首先提取与任务相关的工具上下文图，并利用它来指导感知模式的任务有向无环图（DAG）的构建。在执行过程中，HyperAgent通过面向缺失项的扩展构建状态条件工具支持图，动态实现每个子任务，该扩展会识别未解决的需求并根据当前智能体状态检索支持性的生成工具。在AppWorld上进行的实验表明，与现有的智能体基线相比，HyperAgent在提高任务完成性能的同时，减少了冗余的API调用、LLM交互次数和token消耗。
英文摘要
Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains challenging due to the limitations of implicit reasoning and the evolving nature of real-world execution environments. Existing tool-use agents typically rely on LLMs to infer tool compositions from textual descriptions, which can lead to inefficient exploration and unreliable execution in complex tasks. To address these challenges, we model tool relations at the schema level and construct a directed Tool--Schema Hypergraph, in which tools are represented as hyperedges from their required input-schema nodes to their output-schema nodes. Furthermore, we propose HyperAgent, a Tool--Schema Hypergraph-guided framework for dynamic planning and execution. Given a task, HyperAgent first extracts a task-relevant tool context graph and uses it to guide the construction of a schema-aware Task DAG. During execution, HyperAgent dynamically realizes each subtask by constructing a state-conditioned tool support graph through deficit-oriented expansion, which identifies unresolved requirements and retrieves supporting producer tools according to the current agent state. Experiments on AppWorld demonstrate that HyperAgent improves task completion performance while reducing redundant API calls, LLM interactions, and token consumption compared with existing agent baselines.
