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Bibliographic Details
Main Author: Lee, Sungmin
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.06236
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author Lee, Sungmin
author_facet Lee, Sungmin
contents Scientific publishing systematically filters out negative results. We argue that this long-standing asymmetry has become an urgent problem in the era of large language models, which inherit the positive bias of the literature they are trained on, face an impending shortage of high-quality training data, and are increasingly deployed as both research tools and peer reviewers. We analyze three ways in which LLMs have changed the value of failure data and show that the systematic absence of such data degrades their utility as research tools, training data consumers, and peer reviewers alike. We outline experimental protocols to validate these claims and discuss the structural conditions under which a failure-inclusive publishing culture could emerge.
format Preprint
id arxiv_https___arxiv_org_abs_2604_06236
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle LLMs Have Made Failure Worth Publishing
Lee, Sungmin
Digital Libraries
Scientific publishing systematically filters out negative results. We argue that this long-standing asymmetry has become an urgent problem in the era of large language models, which inherit the positive bias of the literature they are trained on, face an impending shortage of high-quality training data, and are increasingly deployed as both research tools and peer reviewers. We analyze three ways in which LLMs have changed the value of failure data and show that the systematic absence of such data degrades their utility as research tools, training data consumers, and peer reviewers alike. We outline experimental protocols to validate these claims and discuss the structural conditions under which a failure-inclusive publishing culture could emerge.
title LLMs Have Made Failure Worth Publishing
topic Digital Libraries
url https://arxiv.org/abs/2604.06236