AI 中文总结
HEXIS将智能体技能编译为扩展有限状态机，分离知识与控制流，通过增量编译和轨迹重放提升成功率，在四个基准上平均提高16.1个百分点，并显著减少执行令牌。
AI 中文摘要
智能体技能提供了可复用的知识与指令，然而智能体必须反复推断如何应用这些技能以及接下来应执行哪个操作。这导致任务推理与控制决策耦合在一起，使得规定的步骤可能被省略或错误地应用。我们提出了HEXIS，它将智能体技能编译为扩展有限状态机，从而将知识与控制流分离。技能知识被纳入局部指令中，用于指导状态内的推理与生成。该机器记录执行进度和中间结果，而显式的转移条件决定后续操作。我们的增量编译器首先将技能子句和工具接口映射为状态操作、局部指令、数据绑定和转移。然后，它将开发轨迹与现有状态对齐，以识别缺失的操作和依赖关系。这些通过添加或重用状态并细化其连接来纳入。更新仅在静态检查和重放当前及所有先前接受的轨迹后才会被接受。在四个基准测试和四个执行器上，HEXIS相比Skill + ReAct平均提高了16.1个百分点的成功率。Qwen3.8-27B在基准测试中减少了38.4%至88.9%的执行令牌。
英文摘要
Agent skills provide instructions and reusable knowledge, yet agents have to frequently infer which action to take next. This skill's execution paradigm combines task reasoning with control decisions, which could cause the agent to act improperly or omit necessary steps. To improve agent compliance for skills, we introduce HEXIS, which compiles agent skills into extended finite state machines (FSM) that separate knowledge from control flow. Skill knowledge is incorporated into local instructions that guide reasoning and generation within states. The machine records execution progress and intermediate results, while explicit transition conditions determine subsequent operations. Our incremental compiler first maps skill clauses and tool interfaces to state operations, local instructions, data bindings, and transitions. It then aligns development traces with existing states to identify missing operations and dependencies. These are incorporated by adding or reusing states and refining their connections. Updates are accepted only after static checks and replay of the current and all previously accepted traces. Across four benchmarks and four executors, HEXIS improves success over Skill + ReAct by 16.2 percentage points on average. Qwen3.8-27B reduces execution tokens by 38.4-88.9% across benchmarks.
