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.