AAAI-26 Dual Submissions: Novel Challenges

2026-07-07

AI Summary

This study addresses the increasingly covert problem of dual submission—where authors concurrently submit identical or highly similar manuscripts to multiple venues without disclosure, thereby overburdening peer review and compromising scholarly integrity. By systematically comparing submissions to AAAI-26 main conference against those of nine venues with overlapping review cycles, the work employs a pipeline combining title–abstract similarity screening, large language model (LLM)-based textual overlap assessment, and manual verification to detect dual submissions. It reveals, for the first time, a surge in concealed dual submissions facilitated by generative AI rewriting, leading to the identification and rejection of 141 non-compliant papers. These findings prompted AAAI to revise its multiple submission policy and spurred advocacy for cross-venue policy alignment and a community-driven, unified detection framework.

Abstract

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.

Problem

• dual submissions
• scientific publication
• peer review
• academic integrity
• duplicate papers

Innovation

• dual submission detection
• LLM-based overlap assessment
• title-abstract similarity
• peer review integrity
• generative AI misuse