将大模型视为自动机，用自然与形式语言统一编程。
The Ann Arbor Architecture for Agent-Oriented Programming
- 把大模型看作自动机，用其接受的语言进行编程
- 提出新框架，支持智能体编程和上下文学习的新理解
- 适合研究智能体系统与编程范式的开发者
本文从自动机理论视角重新审视大语言模型的提示工程。我们主张语言模型本质上是自动机，应使用其可接受的语言（包括自然语言与形式语言的统一集合）进行编程，因此传统软件工程中编程语言与自然语言的清晰分离需重新思考。为此，我们提出安阿伯架构（Ann Arbor Architecture），一种面向智能体编程的概念性框架，作为对原始标记生成的高层抽象，并为上下文学习提供新视角。基于该框架，我们设计了智能体平台Postline，报告了初步的智能体训练实验结果。
原文摘要 · Abstract (English)
In this paper, we reexamine prompt engineering for large language models through the lens of automata theory. We argue that language models function as automata and, like all automata, should be programmed in the languages they accept, a unified collection of all natural and formal languages. Therefore, traditional software engineering practices--conditioned on the clear separation of programming languages and natural languages--must be rethought. We introduce the Ann Arbor Architecture, a conceptual framework for agent-oriented programming of language models, as a higher-level abstraction over raw token generation, and provide a new perspective on in-context learning. Based on this framework, we present the design of our agent platform Postline, and report on our initial experiments in agent training.
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