AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or safety handling before responding. These routing steps are documented product surfaces in several widely used AI platforms and serving stacks. Routing helps AI services stay affordable, fast, and available at scale, and it shapes trust. Trust can break when routing changes the cost, quality, or accountability of a response without the user being able to tell what happened. "Which model answered?" is only part of the audit question. The runtime path matters. Adaptive AI systems should produce a runtime transparency artifact called the route receipt. A route receipt is a compact record of the route that served a request. It should capture enough material facts for people relying on the output to reconstruct important routing decisions without exposing proprietary internals or hidden reasoning. Route transparency should be part of model documentation. Model cards describe trained model artifacts, while route receipts describe the runtime conditions under which a particular answer was produced. The paper introduces the route-receipt concept, a minimal schema and redaction model, and a documentation-based survey of selected platforms showing that receipt fragments already exist without a portable per-answer record.

翻译：AI产品在响应前常通过版本别名、服务层级、工具选择、区域端点、回退规则或安全处理来路由请求。这些路由步骤在多个广泛使用的AI平台和服务栈中被记录为产品界面。路由有助于保持AI服务的可负担性、快速性和可扩展性，并塑造信任。当路由改变响应的成本、质量或问责性，而用户无法了解实际过程时，信任可能瓦解。“哪个模型回答了？”只是审计问题的一部分。运行时路径至关重要。自适应AI系统应生成一种称为“路由凭证”的运行时透明度工件。路由凭证是服务请求路由的紧凑记录，应捕获足够的关键事实，使依赖输出的人员能重建重要路由决策，同时不暴露专有内部机制或隐藏推理过程。路由透明度应成为模型文档的一部分。模型卡片描述训练好的模型工件，而路由凭证描述生成特定答案时的运行时条件。本文介绍路由凭证概念、最小模式与脱敏模型，并通过基于文档的选定平台调查表明，凭证片段已存在，但缺乏便携式的按答案记录。