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当前的 AI 共同科学家在自动化研究生命周期的部分环节（如生成假设、设计实验、编写代码和撰写论文）方面表现出色。然而，这些最先进的系统仍然是“与研究人员无关的”（researcher-agnostic），它们纯粹为了新颖性、有效性或评审得分等通用指标进行优化，而忽视了使用该工具的具体人类科学家。本文指出了传统自动研究忽略个体差异的问题，并首次提出了“个性化自动研究”（personalized auto-research）的概念。

该论文认为，个性化并不是一个可有可无的便利层，而是让 AI 能够充当真正的共同科学家（而非通用仪器）的基本要求。为此，作者提出了一种灵活的、基于图谱接地（graph-grounded）的框架，将单个研究人员的上下文贯穿于研究流程的每一个步骤（检索、假设生成、实验、写作和评审），从而保留了推动真正科学突破的独特隐性知识和方法论储备。

Personalized Auto-Research: Towards a True AI Co-Scientist

Summary

Current AI co-scientists excel at automating parts of the research lifecycle—such as generating hypotheses, designing experiments, writing code, and drafting papers. However, these state-of-the-art systems remain researcher-agnostic, optimizing purely for generic metrics like novelty, validity, or reviewer scores while ignoring the human scientist utilizing the tool.

This paper introduces the problem of personalized auto-research, arguing that personalization is a fundamental requirement—not just a convenience layer—for an AI to act as a genuine co-scientist rather than a generic instrument. The authors propose a flexible, graph-grounded framework that weaves individual researcher context through every step of the research pipeline (retrieval, hypothesis generation, experimentation, writing, and review) to preserve the unique tacit knowledge and methodological repertoires that drive true scientific breakthroughs.

Current AI co-scientists excel at automating parts of the research lifecycle—such as generating hypotheses, designing experiments, writing code, and drafting papers. However, these state-of-the-art systems remain researcher-agnostic, optimizing purely for generic metrics like novelty, validity, or reviewer scores while ignoring the human scientist utilizing the tool.

This paper introduces the problem of personalized auto-research, arguing that personalization is a fundamental requirement—not just a convenience layer—for an AI to act as a genuine co-scientist rather than a generic instrument. The authors propose a flexible, graph-grounded framework that weaves individual researcher context through every step of the research pipeline (retrieval, hypothesis generation, experimentation, writing, and review) to preserve the unique tacit knowledge and methodological repertoires that drive true scientific breakthroughs.

Document Metadata

Metadata Field Details
arXiv Identifier arXiv:2608.14881 [cs.AI]
Primary Subject Artificial Intelligence (cs.AI), Computation and Language (cs.CL)
Submission Date August 14, 2026
Authors Bo Ni, Franck Dernoncourt, Hongjie Chen, Yu Wang, Nesreen K. Ahmed, Zhengzhong Tu, Tyler Derr, Ryan A. Rossi
License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
Metadata Field Details
arXiv Identifier arXiv:2608.14881 [cs.AI]
Primary Subject Artificial Intelligence (cs.AI), Computation and Language (cs.CL)
Submission Date August 14, 2026
Authors Bo Ni, Franck Dernoncourt, Hongjie Chen, Yu Wang, Nesreen K. Ahmed, Zhengzhong Tu, Tyler Derr, Ryan A. Rossi
License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0

Abstract

生成假设、检索相关工作、设计实验、执行代码并起草完整论文的 AI 共同科学家正在开始改变研究的开展方式。尽管取得了快速进展，但最先进的系统仍然对研究人员视若无睹：给定一个研究目标，它们会优化新颖性、有效性或评审得分，而完全忽略将要使用这些输出结果的具体科学家。

这忽略了关于研究的一个根本事实，即：什么算作新颖、有价值或可行，取决于研究人员本身，包括他们的前期工作、方法论储备，以及他们身处其中的合作者和学术共同体。在这项工作中，我们引入了个性化自动研究的问题，它将研究过程的每个阶段都条件化于对个体研究人员的表征之上。

我们认为，个性化并非一个便利层，而是使 AI 系统能够充当真正的共同科学家而非通用工具的基本属性。为了解决这个问题，我们提出了一个通用且灵活的框架，将图谱接地的研究人员上下文串联到检索、假设搜索、实验、写作和评审中。该框架包含三个基本组成部分： 1. 基于图谱接地的研究人员表征 2. 贯穿整个研究流程的个性化 3. 立足于个体的评估

值得注意的是，我们强调了一种“千篇一律”的失效模式：当不同的研究人员发布相同的研究目标时，他们得到的本质上是相同的研究，从而抹杀了孕育新颖想法的隐性知识。最后，我们讨论了基本的开放性问题与挑战。

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: 1. Graph-grounded researcher representations 2. Personalization across the full research pipeline 3. 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.

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