为自动化流程提供量化评估与优化框架，提升效率与可靠性。
Opus: A Quantitative Framework for Workflow Evaluation
- 用概率模型衡量流程的成功率、成本与收益，实现量化评估。
- 定义结构、语义和信号质量的可测量惩罚项，全面评估流程质量。
- 支持自动排名与强化学习引导的流程优化，适合系统开发者使用。
本文提出Opus工作流评估框架，一种基于概率-规范性的量化方法，用于衡量工作流的质量与效率。该框架将正确性、可靠性和成本整合为统一数学模型，支持工作流的直接比较、评分与优化。框架包含Opus工作流奖励（衡量成功可能性、资源消耗与输出收益的期望性能），以及一系列可度量的规范性惩罚项，涵盖凝聚度、耦合度、可观测性与信息卫生等属性。该方法可集成至现代自动化系统（如Opus）中，实现工作流的自动化评估、排序与优化，并能嵌入强化学习循环以指导工作流的发现与改进。本文还提出了联合奖励-惩罚权衡下的统一优化公式，用于识别与排序最优工作流。
原文摘要 · Abstract (English)
This paper introduces the Opus Workflow Evaluation Framework, a probabilistic-normative formulation for quantifying Workflow quality and efficiency. It integrates notions of correctness, reliability, and cost into a coherent mathematical model that enables direct comparison, scoring, and optimization of Workflows. The framework combines the Opus Workflow Reward, a probabilistic function estimating expected performance through success likelihood, resource usage, and output gain, with the Opus Workflow Normative Penalties, a set of measurable functions capturing structural and informational quality across Cohesion, Coupling, Observability, and Information Hygiene. It supports automated Workflow assessment, ranking, and optimization within modern automation systems such as Opus and can be integrated into Reinforcement Learning loops to guide Workflow discovery and refinement. In this paper, we introduce the Opus Workflow Reward model that formalizes Workflow success as a probabilistic expectation over costs and outcomes. We define measurable Opus Workflow Normative Penalties capturing structural, semantic, and signal-related properties of Workflows. Finally, we propose a unified optimization formulation for identifying and ranking optimal Workflows under joint Reward-Penalty trade-offs.
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