用可变状态取代不断增长的对话历史，提升长时序智能体的效率与准确率。
SKILL.state: Scalable Long-Horizon Agent Skills
- 以结构化状态替代追加式对话历史，每步仅传当前状态和观测。
- 在多个数据集上任务准确率提升，总令牌消耗大幅降低。
- 适用于各类模型与环境，是可扩展智能体的核心设计范式。
大型语言模型（LLMs）越来越多地作为自主智能体执行复杂、长时间的程序化技能。现有智能体运行时通过不断追加观察、动作和中间推理痕迹来维持执行，导致长时序下延迟加剧和上下文污染失败。我们提出 SKILL.state，一种将追加式对话历史替换为显式可变执行状态的运行时架构。每一步执行中，模型仅接收不可变的技能规范、当前结构化执行状态和最新观测，中间推理在生成有效状态更新后立即丢弃，避免提示词随执行历史无限增长。在多种数据集、模型和执行环境中，SKILL.state 在提升任务准确率的同时显著降低累计令牌消耗。结果表明，显式执行状态是一种高效且与架构无关的可扩展长时序智能体技能抽象。
原文摘要 · Abstract (English)
Large Language Models (LLMs) increasingly act as autonomous agents executing complex, long-running procedural skills. Existing agent runtimes maintain execution by continually appending observations, actions, and intermediate reasoning traces to an ever-growing conversation history, causing latency degradation and context-poisoning failures over long horizons. We present SKILL.state, a runtime architecture that replaces append-only conversational history with an explicit, mutable execution state. At each execution step, the model receives only the immutable skill specification, the current structured execution state, and the latest observation. Intermediate reasoning is discarded immediately after producing a validated state update, preventing prompt growth with execution history. Across diverse datasets, models, and execution environments, SKILL. state improves task accuracy while substantially reducing cumulative token consumption. Our results demonstrate that explicit execution state is an effective and architecture-agnostic abstraction for scalable long-horizon agent skills.
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