The Last Human-Written Paper: Agent-Native Research Artifacts
Authors: name: Jiachen Liu, name: Jiaxin Pei, name: Jintao Huang, name: Chenglei Si, name: Ao Qu, name: Xiangru Tang, name: Runyu Lu, name: Lichang Chen, name: Xiaoyan Bai, name: Haizhong Zheng, name: Carl Chen, name: Zhiyang Chen, name: Haojie Ye, name: Yujuan Fu, name: Zexue He, name: Zijian Jin, name: Zhenyu Zhang, name: Shangquan Sun, name: Maestro Harmon, name: John Dianzhuo Wang, name: Jianqiao Zeng, name: Jiachen Sun, name: Mingyuan Wu, name: Baoyu Zhou, name: Chenyu You, name: Shijian Lu, name: Yiming Qiu, name: Fan Lai, name: Yuan Yuan, name: Yao Li, name: Junyuan Hong, name: Ruihao Zhu, name: Beidi Chen, name: Alex Pentland, name: Ang Chen, name: Mosharaf Chowdhury, name: Zechen Zhang
Organization: Stanford University

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.