SkillZip：通过发现可复用结构实现自进化智能体的免评估技能压缩

摘要

自我进化代理通过追加成功流程和失败修复来积累可复用技能。随着时间推移，相同需求常常在多个分支、示例和警告中被重复表述，而常见动作序列被复制而非复用。由此产生的技能变得注入成本高昂且难以维护。通用提示压缩并不适用于这一场景，因为技能并非一段平铺文本：其名称和描述定义了适用条件，工作流控制执行过程，工具和输出契约约束有效性，而罕见异常即使在没有采样任务激活它们时仍可能至关重要。评估引导的压缩可以检验这些行为，但会引入轨迹回放、成本开销以及对压缩时评估集的依赖。我们提出SkillZip，一种免评估的方法，通过寻找技能最短的忠实结构化解释来实现压缩。其直觉是：一次解释、多处引用——在适用作用域将重复规则陈述一次，将重复动作序列提取为共享过程，并仅将差异保留为显式异常。我们将这一直觉形式化为一个类型化的最小描述长度目标，作用于技能契约和残差，并施加硬覆盖约束以覆盖每个提取的触发器、工作流边、工具需求、义务和输出字段。该公式提供了简单的共享阈值，从构造上保留独特的罕见规则，并支持高效的局部更新。SkillZip具有单次模式（包含一次结构化提取调用和确定性优化）以及持续压缩即写（Zip-on-Write）模式（可整合每个自我进化补丁而无需重放任务或重新解析完整历史）。通过全面的实验评估，我们证明了SkillZip在压缩性能、泛化能力和成本开销方面的有效性与优越性。

English

Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, examples, and warnings, while common action sequences are copied rather than reused. The resulting skill becomes expensive to inject and difficult to maintain. Generic prompt compression is ill-suited to this setting because a skill is not a flat passage: its name and description define when it applies, its workflow controls execution, its tool and output contracts constrain validity, and rare exceptions may remain essential even when no sampled task activates them. Evaluation-guided compression can test these behaviors, but it introduces rollouts, cost, and dependence on the compression-time evaluation set. We present SkillZip, an evaluation-free method that compresses a skill by finding its shortest faithful structural explanation. The intuition is explain once, reference many: state a repeated rule once at the scope where it applies, factor a repeated action sequence into a shared procedure, and keep only the differences as explicit exceptions. We formalize this intuition as a typed minimum description-length objective over a skill contract and a residual, subject to a hard coverage constraint for every extracted trigger, workflow edge, tool requirement, obligation, and output field. The formulation provides simple sharing thresholds, preserves unique rare rules by construction, and supports efficient local updates. SkillZip has a one-shot mode with one structured extraction call and deterministic optimization, and a continual Zip-on-Write mode that integrates each self-evolution patch without replaying tasks or reparsing the full history. Through comprehensive experimental evaluations, we demonstrate the effectiveness and superiority of SkillZip in compression performance, generalizability, and cost overhead.