用概率链统一分析不同智能体策略，看清它们如何影响成功概率。
Mathematical Framing for Different Agent Strategies
- 将智能体行为建模为概率链，量化策略对结果的影响。
- 提出“自由度”概念，区分各策略可优化的控制变量。
- 适合研究智能体设计与评估的学者，提升方法可比性。
我们提出一个统一的数学与概率框架，用于理解和比较多种AI智能体策略。该框架弥合了高层设计概念（如ReAct、多智能体系统、控制流）与严谨数学表述之间的鸿沟。将智能体过程建模为概率链，可细致分析不同策略如何操控这些概率以达成目标。框架提供一种共同语言，讨论各类智能体架构的内在权衡。核心贡献之一是引入“自由度”概念，直观区分各方法可优化的调控维度，从而指导特定任务下的策略选择。本工作旨在提升智能体设计与评估的清晰度和精确性，揭示在复杂智能体系统中最大化成功动作概率的路径。
原文摘要 · Abstract (English)
We introduce a unified mathematical and probabilistic framework for understanding and comparing diverse AI agent strategies. We bridge the gap between high-level agent design concepts, such as ReAct, multi-agent systems, and control flows, and a rigorous mathematical formulation. Our approach frames agentic processes as a chain of probabilities, enabling a detailed analysis of how different strategies manipulate these probabilities to achieve desired outcomes. Our framework provides a common language for discussing the trade-offs inherent in various agent architectures. One of our many key contributions is the introduction of the "Degrees of Freedom" concept, which intuitively differentiates the optimizable levers available for each approach, thereby guiding the selection of appropriate strategies for specific tasks. This work aims to enhance the clarity and precision in designing and evaluating AI agents, offering insights into maximizing the probability of successful actions within complex agentic systems.
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