Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and the branching exploration process are discarded to fit a linear narrative; and an Engineering Tax, where the gap between reviewer-sufficient prose and agent-sufficient specification leaves critical implementation details unwritten. Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work. We introduce the Agent-Native Research Artifact (Ara), a protocol that replaces the narrative paper with a machine-executable research package structured around four layers: scientific logic, executable code with full specifications, an exploration graph that preserves the failures compilation discards, and evidence grounding every claim in raw outputs. Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an Ara Compiler that translates legacy PDFs and repos into Aras; and an Ara-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste. On PaperBench and RE-Bench, Ara raises question-answering accuracy from 72.4% to 93.7% and reproduction success from 57.4% to 64.4%. On RE-Bench's five open-ended extension tasks, preserved failure traces in Ara accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.

翻译：科学出版将分叉迭代的研究过程压缩成线性叙事，丢弃了沿途发现的大部分内容。这种压缩施加了两种结构性成本：一是"叙事税"——失败的实验、被否定的假设以及分支探索过程被舍弃以符合线性叙述；二是"工程税"——审稿人可理解的散文与智能体可执行的规约之间存在鸿沟，导致关键实现细节未被记录。对于人类读者而言这些成本尚可容忍，但当AI智能体必须理解、复现和扩展已发表成果时，它们便成为关键瓶颈。我们提出智能体原生研究工件（Ara）协议，该协议以机器可执行的研究包替代叙事性论文，围绕四个层次构建：科学逻辑、包含完整规约的可执行代码、保留压缩所丢弃的失败记录的探索图，以及将每项声明锚定于原始输出的证据。三套机制支撑该生态系统：在常规开发过程中捕获决策与死胡同的实时研究管理器；将传统PDF和代码仓库转化为Aras的Ara编译器；以及实现客观检查自动化以便人类审稿人聚焦重要性、创新性与品味的原生Ara评审系统。在PaperBench和RE-Bench基准上，Ara将问答准确率从72.4%提升至93.7%，复现成功率从57.4%提升至64.4%。在RE-Bench的五个开放式扩展任务中，Ara保留的失败轨迹加速了研究进展，但根据智能体的能力差异，也可能限制其突破先前实验界限的能力。