# SkVM：面向高效可移植执行的技能编译系统

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-06 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo1jd5hu00soslrrxywcesns
- 原文链接：https://arxiv.org/abs/2604.03088

## AI 摘要

针对大模型智能体技能跨平台行为不一致与执行效率问题，研究团队提出SkVM编译与运行时系统。该系统借鉴传统编译器设计，将技能视为代码、大模型视为异构处理器，基于对11.8万个技能的能力剖析，在编译时进行能力匹配与环境绑定，运行时实施JIT代码固化与自适应重编译。实验覆盖8种大模型及3种agent harness，结果显示SkVM在提升任务完成率的同时，可降低40%的token消耗，实现最高3.2倍加速及19-50倍延迟缩减。

## 正文

LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing the same skill to behave inconsistently for different agents. This fragility undermines skill portability and execution efficiency. To address this challenge, we analyze 118,000 skills and draw inspiration from traditional compiler design. We treat skills as code and LLMs as heterogeneous processors. To make portability actionable, we decompose a skill's requirements into a set of primitive capabilities, and measure how well each model-harness pair supports them. Based on these capability profiles, we propose SkVM, a compilation and runtime system designed for portable and efficient skill execution. At compile time, SkVM performs capability-based compilation, environment binding, and concurrency extraction. At runtime, SkVM applies JIT code solidification and adaptive recompilation for performance optimization. We evaluate SkVM across eight LLMs of varying scales and three agent harnesses, covering SkillsBench and representative skill tasks. Results demonstrate that SkVM significantly improves task completion rates across different models and environments while reducing token consumption by up to 40%. In terms of performance, SkVM achieves up to 3.2x speedup with enhanced parallelism, and 19-50x latency reduction through code solidification.