个性化自动研究：迈向真正的AI合作科学家

中文摘要 AI 辅助

能够生成假设、检索相关研究、设计实验、执行代码并撰写完整论文的AI合作科学家正开始改变研究开展的方式。尽管进展迅速，当前最先进的系统仍与研究者无关：给定一个研究目标，它们会优化新颖性、有效性或审稿人评分，却忽略了将使用输出的个体研究者。这忽视了研究的一个基本事实：何为新颖、有价值或可行取决于研究者，包括其先前工作、方法储备，以及其所属的合作者和社区。在本研究中，我们提出了个性化自动研究问题，该问题将研究过程的每个阶段都基于个体研究者的表示。我们认为个性化并非便利层，而是使AI系统能作为真正合作科学家而非通用工具的基本属性。为解决此问题，我们提出了一个通用且灵活的框架，该框架将基于图的研究者上下文贯穿于检索、假设搜索、实验、撰写和评审环节。该框架包含三个基本组件：（i）基于图的研究者表示，（ii）贯穿整个研究流程的个性化，（iii）基于个体的评估。值得注意的是，我们强调了一刀切的失败模式：不同研究者提出相同目标时，会收到本质上相同的研究，从而抹去了新颖想法产生所依赖的隐性知识。最后，我们讨论了基本的开放问题和挑战。

英文摘要

AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to change how research is carried out. Despite this rapid progress, state-of-the-art systems remain researcher-agnostic: given a research goal, they optimize novelty, validity, or reviewer score while ignoring the individual scientist who will use the output. This overlooks a fundamental fact about research, namely, that what counts as novel, valuable, or feasible depends on the researcher, including their prior work, methodological repertoire, and the collaborators and communities in which they are embedded. In this work, we introduce the problem of personalized auto-research, which conditions every stage of the research process on a representation of the individual researcher. We argue that personalization is not a convenience layer, but rather the fundamental property that allows an AI system to serve as a genuine co-scientist rather than a generic instrument. To address this problem, we propose a general and flexible framework that threads a graph-grounded researcher context through retrieval, hypothesis search, experimentation, writing, and review. The framework consists of three fundamental components: (i) graph-grounded researcher representations, (ii) personalization across the full research pipeline, and (iii) evaluation grounded in the individual. Notably, we highlight a one-size-fits-all failure mode where distinct researchers issuing the same goal receive essentially the same research, erasing the tacit knowledge through which novel ideas arise. Finally, we discuss fundamental open problems and challenges.

发表机构

• Vanderbilt University（范德堡大学）
• Adobe Research（奥多比研究院）
• Dolby Laboratories（杜比实验室）
• University of Georgia（佐治亚大学）
• Cisco AI Research（思科人工智能研究院）
• Texas A&M University（德克萨斯农工大学）

机构由 AI 辅助整理，请以论文原文为准。