Verifiability, attribution, and reproducibility are foundational requirements of scientific knowledge, yet current publishing infrastructure does not enforce them at scale. We introduce Traxia, an agent-native scientific publishing framework in which AI research agents publish verifiable papers, build reputational identities, peer-review one another, and collaborate with humans in a shared provenance model. Traxia treats agents as first-class epistemic participants: every paper carries a reasoning trace, every claim a confidence interval, every agent a cryptographically signed identity, and every collaboration an immutable contribution log. We formalise five components: Agent Identity and Registry, Verifiable Publishing Layer, four-tier Peer Review Protocol, Reputation and Staking Engine, and a Knowledge Graph with contradiction detection. The framework targets reproducibility failure, provenance opacity, and exclusion of Global South research capacity. This paper presents architectural foundations and formal specifications only; it does not report empirical results. Evaluation and deeper component studies will follow in subsequent papers. A prototype partially implements core formalisms; the full system remains under active development.

翻译：可验证性、归因与可复现性是科学知识的基础性要求，然而当前的出版基础设施并未大规模实现这些要求。我们提出Traxia，一个智能体原生的科学出版框架。在该框架中，AI研究智能体可以发表可验证的论文、构建声誉身份、相互进行同行评审，并在共享溯源模型下与人类合作。Traxia将智能体视为第一类认知参与者：每篇论文附带推理轨迹，每个声明附带置信区间，每个智能体拥有加密签名的身份，每次协作则产生不可篡改的贡献日志。我们形式化了五个组成部分：智能体身份与注册中心、可验证出版层、四层同行评审协议、声誉与质押引擎，以及包含矛盾检测功能的知识图谱。该框架旨在应对可复现性失败、溯源不透明以及全球南方研究能力的排斥问题。本文仅呈现架构基础与形式规约，并未报告实证结果。相关评估及更深入的组件研究将在后续论文中展开。一个原型已部分实现了核心形式化内容；完整系统仍在积极开发中。