Peer review in computational fields remains centered on author-written manuscripts, even though the decisive evidence for many claims resides in executable code, data, configurations, and experiment pipelines. This manuscript-first workflow gives authors substantial control over narrative framing while leaving reviewers with limited time to inspect implementation details, reproduce results, or detect unsupported claims. This vision and protocol paper proposes code-first peer review: authors submit executable research artifacts and minimal claim manifests; a venue-controlled AI system builds the environment, executes experiments, audits code paths, maps claims to evidence, and generates a standardized Review Package for human reviewers. The goal is not to replace reviewers or to give authors an automatic writing assistant. Instead, AI serves as review infrastructure that shifts the target of peer review from polished narratives to executable evidence. We formalize a claim-evidence contract, define the Generated Review View and Review Package abstractions, give a worked example, outline a system architecture, and analyze evaluation and governance challenges including AI bias, prompt injection, model instability, auditability, and author appeal.

翻译：计算领域的同行评审仍然以作者手稿为中心，尽管许多主张的决定性证据存在于可执行代码、数据、配置和实验流程中。这种以手稿为中心的工作流程赋予作者对叙事框架的极大控制权，而留给评审员检查实现细节、复现结果或检测无依据主张的时间十分有限。本文提出一项愿景与协议——代码优先的同行评审：作者提交可执行的研究工件及最小化的主张清单；由会议主办方控制的AI系统构建环境、执行实验、审计代码路径、将主张与证据进行映射，并生成标准化的评审包供人类评审员使用。目标并非取代评审员，也并非为作者提供自动写作助手。相反，AI作为评审基础设施，将同行评审的目标从精雕细琢的叙述转向可执行的证据。我们形式化定义了主张-证据契约，提出了生成式评审视图与评审包抽象概念，给出了一个具体示例，概述了系统架构，并分析了评估与治理挑战，包括AI偏见、提示注入、模型不稳定性、可审计性以及作者申诉机制。