人工智能整合型研究生态系统的未来愿景
A Vision for the Future of an AI-Integrated Research Ecosystem
AI总结：
本文探讨生成式AI渗透研究生涯后科学交流的演变，提出从管控AI转向构建溯源、校准与问责基础设施的愿景，并邀请学界参与相关对话。
AI中文摘要：
生成式AI已渗透至研究生涯的各个阶段：学术研究的开展、撰写、发表及评审。近期的政策应对，如ACM的作者身份政策，旨在解决关于负责任且透明披露AI使用的紧迫问题。我们认为，关注作者身份与披露虽有必要，但可能会掩盖并放大发表体系中一系列根深蒂固的问题与压力。核心问题并非论文及其他研究成果应如何融入AI，而是当所有相关方（作者、评审人、读者）都可能依赖AI辅助时，科学交流本身应如何演变。我们结合自身在这些及其他角色中的经验，围绕四个相互关联的问题阐述了2036年两种对比但可行的愿景，即关于论文作为成果的目的、评审、人类评审人以及约束所有相关方的激励机制。我们主张从管控生成式AI及其他颠覆性技术，转向构建溯源、校准与问责的基础设施，使可信学术成为默认状态。最后，我们提出三大重大挑战，并邀请整个学界展开更广泛的对话，探索研究路径。
英文摘要：
Generative AI has infiltrated every stage of the research lifecycle: how scholarship is conducted, written, published, and reviewed. Recent policy responses, such as ACM's authorship policy, address an immediate concern about responsible and transparent disclosure of AI use. We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems. The central question is not about how papers and other research artifacts should incorporate AI, but how scientific communication itself should evolve when all relevant parties (authors, reviewers, readers) may rely on AI assistance. We draw on our experience within these and other roles to illustrate two contrasting but feasible visions of 2036 with four entwined questions, namely about the purpose of papers as artifacts, reviews, human reviewers, and the incentives that bind all of them. We argue for a shift from policing GenAI and other disruptive technologies to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default. We conclude with three grand challenges and invite the community to a broader conversation and research pathways.
