AAAI-26双重投稿：新挑战
AAAI-26 Dual Submissions: Novel Challenges
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中文总结 AI 辅助
AAAI-26评审中发现双重投稿问题严重，通过标题+摘要相似性评估等方法处理，导致141篇主赛道投稿被拒稿。提醒注意双重投稿增长，其因生成式AI工具加剧，还给出更新政策、设检查工具等应对建议。
中文摘要 AI 辅助
双重投稿是指相同或实质相似的论文同时提交给一个或多个存档期刊且无交叉引用或披露，这对AAAI会议和其他科学出版场所来说是个日益严重的问题。作为AAAI-26评审过程的一部分，会议组织者将AAAI主赛道投稿与其他九个有重叠评审期的存档期刊进行比较，并在AAAI-26主赛道内搜索双重投稿。采用标题+摘要相似性评估来优先处理高度相似的论文对，再由基于大语言模型的重叠评估工具进行后续分类，最后人工评审最严重的论文对。人工评审导致141篇AAAI-26主赛道投稿被直接拒稿。我们提醒未来的组织者和更广泛的人工智能研究社区注意双重投稿的大幅增长。易于检测的完全重复投稿的发生率已被用不同词语描述相同贡献的论文数量超越，这种现象的增长可能因生成式人工智能工具的使用增加而加剧。我们还提出了应对这一挑战的建议，包括更新AAAI多重投稿政策并对社区进行可接受做法的教育、在投稿截止前设置双重投稿检查工具、跨期刊制定一致的政策和处罚措施以减少双重投稿发生率以及创建社区驱动的对抗性挑战以加速强大检测工具的开发。
英文摘要
Dual submissions, in which identical or substantially similar papers are simultaneously submitted to one or more archival venues, without cross-citation or disclosure, are a growing problem for the AAAI Conference and other scientific publication venues. These submissions increase the burden on the peer-review system and pollute the scientific record. As part of the AAAI-26 review process, we (conference organizers) compared AAAI main-track submissions to nine other archival venues with overlapping review periods. We also searched for dual submissions within the AAAI-26 main track. We employed title+abstract similarity assessment to prioritize highly similar paper pairs for subsequent triage by an LLM-based overlap assessment tool, followed by manual review of the highest severity pairs. Manual review of such pairs led to the desk-rejection of 141 AAAI-26 main-track submissions. We seek to alert future organizers, and the broader artificial intelligence research community, to the enormous growth in dual submissions. The incidence of exact duplicate submissions, which are easy to detect, has been eclipsed by the number of papers that use different words to describe the same contribution, which are extremely time-consuming to detect. The growth in this phenomenon is likely facilitated by increasing access to generative AI tools. We include several recommendations for addressing this challenge, including (1) updating the AAAI Multiple Submission Policy and educating the community about acceptable practice, (2) having dual-submission checking tools in place before submissions close, (3) working across venues to converge on consistent policies and penalties to aid in reducing the incidence of dual submission, and (4) creating a community-driven adversarial challenge to accelerate the development of robust detection tools.
发表机构
- OSU Libraries（俄勒冈州立大学图书馆）
- University of Texas at Austin（德克萨斯大学奥斯汀分校）
- Nanyang Technological University（南洋理工大学）
- AAAI Washington, DC（AAAI华盛顿特区）
- Indiana University（印第安纳大学）
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
