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TRACE: Governing AI-Assisted Research with Principles from Computer Science

Amirhossein Zohrehvand, Aldona Kapacinskaite · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24035 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Manfaat kecerdasan buatan (AI) membuat lebih mudah daripada sebelumnya untuk menghasilkan output penelitian. Pertanyaan mendesak adalah bagaimana para peneliti harus mengatur pekerjaan mereka dengan teknologi ini. Pertanyaan ini tidak baru: teknik perangkat lunak dan penelitian keamanan telah lama mempelajari bagaimana tim orang dan mesin mempertahankan kendali atas tubuh kerja bersama dengan banyak kontributor. Kami mendestil prinsip-prinsip dari praktik-praktik literatur ini melaporkan dan menerapkannya ke penelitian yang didukung oleh AI. Prinsip-prinsip ini membentuk TRACE: Track records, Review against sources, Accept independently, Control transitions, dan Evaluate corrections. Bersama-sama, lima prinsip memberikan peneliti dasar umum untuk tetap berada dalam kendali sementara mengintegrasikan AI ke dalam alur kerja mereka. Dengan membatasi unit-unit kerja komunitas dan menghadiri bagaimana masing-masing dibentuk, mereka membantu peneliti yang didukung oleh AI

Machine translation (Meta M2M100); the English original follows.

Abstract

Artificial intelligence (AI) makes it easier than ever to produce research outputs. The pressing question is how researchers should organize their work with this technology. The question is not new: software engineering and security research have long studied how teams of people and machines keep control of a shared body of work with many contributors. We distil principles from the practices these literatures report and transpose them to AI-assisted research. The principles form TRACE: Track records, Review against sources, Accept independently, Control transitions, and Evaluate corrections. Together, the five principles give researchers a common basis for remaining in control while integrating AI into their workflows. By bounding units of work and attending to how each is shaped, they help researchers pace AI-assisted work to human attention rather than to the speed at which AI produces content. We also introduce TRACE-OS, an open source operating system for AI-assisted research projects. It puts the principles into practice through shared instructions, tools, and records that fit researchers' existing workflows. The shared standard can contribute to building trust, which allows teams to delegate and realize the productivity that AI promises. More broadly, we aim to open a conversation about the governing principles through which the research community can benefit from AI.

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Topics: Delegate, Work (physics), Control (management), Computer science, Conversation, Pace

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