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. Our code is open-sourced at https://github.com/Orchestra-Research/Agent-Native-Research-Artifact.

翻译：科学出版物将分支迭代的研究过程压缩为线性叙事，丢弃了沿途发现的大部分内容。这种压缩机制带来了两种结构性代价：一是“叙事税”——失败的实验、被否定的假设和分支探索过程被迫舍弃以符合线性结构；二是“工程税”——评审者可见的通用描述与智能体所需的精确规范之间存在鸿沟，导致关键实现细节未被记录。这些代价对人类读者尚可容忍，但当AI智能体必须理解、复现和扩展已发表成果时便成为关键障碍。我们提出智能体原生研究文档（ARA），这是一种将叙事型论文替换为机器可执行研究包的新协议，其结构围绕四个层级：科学逻辑、完备规范的可执行代码、保留失败压缩过程的探索图谱，以及将每项主张锚定于原始输出的证据体系。支撑该生态系统的三项机制包括：在常规开发过程中捕获决策与死胡同的实时研究管理器、将遗留PDF与代码仓库转换为ARA格式的编译器，以及可自动化客观审查的ARA原生评审系统，使人类评审者能聚焦于重要程度、创新性与学术品味。在PaperBench和RE-Bench基准测试中，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的五项开放式扩展任务中，ARA保留的失败轨迹虽能加速进展，但也可能根据智能体的能力差异，限制高水平智能体突破既往实验框架的探索能力。我们的代码已开源在https://github.com/Orchestra-Research/Agent-Native-Research-Artifact。