elvis(@omarsar0)

// Scaling Laws for Agent Harnesses // If you build agent harnesses, this one is worth your time. ...

7.5内容质量
// Scaling Laws for Agent Harnesses // If you build agent harnesses, this one is worth your time. ...

TL;DR · AI 摘要

新研究表明，代理框架调优不应仅关注调用量，而应引入有效性的概念。

核心要点

  • 大多数代理框架调优错误地将调用量视为唯一关键指标。
  • 新研究提出有效性的概念，强调质量而非数量。
  • 研究引入了Effective工具，可优化代理框架性能。

结构提纲

按章节快速跳转。

  1. §引言

    介绍代理框架调优的重要性及常见误区。

  2. ·传统方法的问题

    传统方法仅关注调用量，忽略质量因素。

  3. ·新研究的核心发现

    新研究提出有效性的概念，强调质量的重要性。

  4. ·Effective工具的引入

    研究引入了Effective工具，用于优化代理框架性能。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • 代理框架调优研究

金句 / Highlights

值得收藏与分享的关键句。

#AI代理#调优#研究
打开原文

If you build agent harnesses, this one is worth your time.

(bookmark it)

Most harness tuning treats every token and tool call as if volume is all that counts. New research shows that most of it does not.

The work introduces Effective https://t.co/SDT2ZVUuuW" / X

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elvis on X: "// Scaling Laws for Agent Harnesses // If you build agent harnesses, this one is worth your time. (bookmark it) Most harness tuning treats every token and tool call as if volume is all that counts. New research shows that most of it does not. The work introduces Effective https://t.co/SDT2ZVUuuW" / X

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