这篇论文提出了衡量AI智能体忠诚度的新模型DVM-HALL和评分NHAS，专门针对自主商业和DeFi场景，值得研究参考。
传统客户忠诚度模型在AI智能体时代失效，因为它们未考虑算法有限理性和构造自主性。为此，论文提出DVM-HALL（动态可验证多智能体人机忠诚循环）模型，通过softmax概率公式整合人类情感权益、智能体机器体验效用、校准信任、委派权限和可验证执行。模型引入递归更新机制，每次交互后动态校准信任与委派，并集成DeFi可验证执行层，将gas成本、滑点、MEV暴露和智能合约漏洞作为品牌偏好的核心预测因子。同时提出NHAS（净人机评分）指标，这是一个基于人类反馈、执行日志、基准比较和可验证收据的可审计风险加权度量。论文规划了三阶段实证验证计划，包括受控购物实验、多智能体市场模拟和DeFi测试平台。
The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.