yibie on X: "推荐这篇文章，Lilian Weng 的最新博客把"harness 工程"这个正在成为 AI agent 核心竞争的领域做了全面梳理。从上下文工程到进化式 harness 搜索，从 Meta-Harness 到 Self-Harness。如果你想理解 2026 年 agent 竞争的核心在哪里——不是模型本身，而是围绕模型的 harness——这篇是必读。 面向自改进的 Harness 工程 递归自改进（RSI）的概念可以追溯到 I. J. Good (1965) 的"超智能机器"——一个在所有智力活动中超越人类并能设计更好机器来自我改进的系统。 Yudkowsky
Quick Answer
Lilian Weng's latest blog emphasizes the importance of 'harness engineering' in AI agent competition, focusing on concepts like recursive self-improvement and the role of deployment systems.
Quick Take
It argues that the future of AI agents relies not just on models but on the harness that surrounds them, impacting how they think, plan, and act.
Key Points
- Harness engineering is becoming central to AI agent competition by enhancing model capabilities.
- Recursive self-improvement (RSI) allows AI to enhance its own cognitive mechanisms.
- Deployment systems are crucial for effective AI operation, influencing model performance.
- Harness design patterns include workflow design, evaluation, and persistent state management.
- Examples like Claude Code illustrate the success of harness in AI deployments.
Source Excerpt
推荐这篇文章，Lilian Weng 的最新博客把"harness 工程"这个正在成为 AI agent 核心竞争的领域做了全面梳理。 从上下文工程到进化式 harness 搜索，从 Meta-Harness 到 Self-Harness。 如果你想理解 2026 年 agent 竞争的核心在哪里——不是模型本身，而是围绕模型的 harness——这篇是必读。 面向自改进的 Harness 工程 递归自改进（RSI）的概念可以追溯到 I. J. Good (1965) 的"超智能机器"——一个在所有智力活动中超越人类并能设计更好机器来自我改进的系统。 Yudkowsky 用"递归自改进"描述一个特定的反馈循环：AI 使用它当前的智能来改进产生其智能的认知机制。 在现代 AI 中，这个反馈循环可能意味着模型直接重写自身权重，或更广泛地——模型改进训练流程和部署系统，这反过来使更好的后继模型能产生。 我特别提到"部署系统"，因为原始模型和现实上下文之间的那层似乎和模型原始智能本身一样重要。 Harness 是 AI 部署的重要组成部分，编码 agent 产品如 Claude Code 和 [...
] 产品如 Claude Code 和 Codex 的成功就是例证。 Harness 是围绕基础模型的系统，它编排执行并决定模型如何思考与规划、调用工具与行动、感知与管理上下文、存储制品、以及评估结果。 这篇文章将聚焦于 harness 工程的研究以及它如何贡献于 RSI。 …
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