法学硕士擅长预测任务和复杂推理任务,，但许多高价值部署依赖于不确定性, 下的决策，例如, 使用哪种工具呼叫, 咨询哪位专家, 或投资多少资源。虽然贝叶斯方法对于 LLM 推理, 的有用性和可行性仍不清楚，但这篇立场文件认为，协调 LLM 和工具) 的代理人工智能系统 ( 的控制层是贝叶斯原则应该发挥作用的明显案例。贝叶斯决策理论为代理系统提供了一个框架，可以帮助维持对任务相关潜在量, 的信念，以根据观察到的代理和人类与人工智能交互, 更新这些信念并选择操作。使法学硕士本身明确的贝叶斯信念更新引擎仍然是计算密集型的，并且作为一般建模目标在概念上并不平凡。相比之下,，本文认为连贯决策需要在代理系统, 的编排层面上采用贝叶斯原理，而不一定是LLM 代理参数。本文阐明了适合现代代理 AI 系统和人机人工智能协作, 的贝叶斯控制的实用属性，并提供了具体示例和设计模式来说明校准信念和效用感知策略如何改进代理 AI 编排。
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many resources to invest. While the usefulness and feasibility of Bayesian approaches remain unclear for LLM inference, this position paper argues that the control layer of an agentic AI system (that orchestrates LLMs and tools) is a clear case where Bayesian principles should shine. Bayesian decision theory provides a framework for agentic systems that can help to maintain beliefs over task-relevant latent quantities, to update these beliefs from observed agentic and human-AI interactions, and to choose actions. Making LLMs themselves explicitly Bayesian belief-updating engines remains computationally intensive and conceptually nontrivial as a general modeling target. In contrast, this paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system, not necessarily the LLM agent parameters. This paper articulates practical properties for Bayesian control that fit modern agentic AI systems and human-AI collaboration, and provides concrete examples and design patterns to illustrate how calibrated beliefs and utility-aware policies can improve agentic AI orchestration.
科目: 人工智能 (cs.AI); 机器学习 (cs.LG); 机器学习 (stat.ML)
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)