# SkillZip：面向自进化智能体的免评估技能压缩方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-11 08:00
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmspk7gzl01wsrojepfdzyopu
- 原文链接：https://arxiv.org/abs/2608.11079

## AI 摘要

SkillZip 提出一种免评估的技能压缩方法，通过寻找最短忠实结构解释来压缩自进化智能体积累的技能，核心思路是“解释一次、多处引用”，将重复规则提升至适用作用域、重复动作序列拆分为共享流程、仅保留差异作为显式异常。该方法将压缩形式化为带硬覆盖约束的类型化最小描述长度目标，支持一次性模式和持续 Zip-on-Write 模式，实验显示其在压缩性能、泛化性和成本开销上均优于现有方法。

## 正文

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.