# 新研究提出智能体协同进化框架，解决长期任务规划与技能库僵化难题

- 来源：elvis (@omarsar0)
- 发布时间：2026-04-27 00:36
- AIHOT 分数：54
- AIHOT 链接：https://aihot.virxact.com/items/cmog0dqap00i5slr33eazog3o
- 原文链接：https://x.com/omarsar0/status/2048440985726955998

## AI 摘要

构建复杂智能体时，长期任务智能体常因决策者分解能力不足或技能库过时而失败。新研究提出一种协同进化框架，让LLM决策智能体与动态技能库通过迭代优化共同改进。决策智能体负责选取和串联技能，性能反馈同时更新其策略和技能库本身。新技能通过归纳成功序列自动生成，而非预先手动编码。传统方法将技能与决策作为独立问题优化，容易陷入瓶颈。协同进化则能在单一循环中实现自适应规划，并持续增长可复用行为库，这对任务结构不确定的领域（如机器人、游戏智能体、复杂规划）至关重要。

## 正文

Here is a very common problem when building complex agents.

Long-horizon agents （in particular） fail in two ways： the decision-maker can't decompose well， or the skill library goes stale.

This new research tackles both at once.

The paper introduces a co-evolution framework where an LLM decision agent and a dynamic skill bank improve each other through iterative refinement.

The decision agent picks and chains skills. Performance feedback updates both the policy and the skills. New skills emerge by generalizing successful sequences instead of being hand-coded upfront.

Why does it matter？

Most long-horizon agent stacks treat skills and decision-making as separate optimization problems， which is why they plateau.

Co-evolution gives you adaptive planning and a growing library of reusable behaviors from a single loop， which is what you actually want when task structure isn't predetermined： robotics， game agents， complex planning.

Paper： https://arxiv.org/abs/2604.20987

Learn to build effective AI agents in our academy： https://academy.dair.ai/