How to cite/talk about preprint-subsequent works for a camera-ready version? [R]

The query posed by /u/Vulcapulae regarding preprint citation in the camera-ready version of a conference paper highlights a growing tension within the rapidly evolving landscape of AI research. The traditional academic cycle, with its emphasis on peer review and formal publication, is increasingly colliding with the speed and openness of preprint servers like arXiv. The scenario they describe – a preprint gaining traction, being cited by subsequent works, and then being formalized into a conference paper – is becoming commonplace, and the question of how to navigate the related work section reflects a deeper shift in how research is disseminated and acknowledged. It’s a challenge that underscores the need for updated guidelines and a more nuanced understanding of scholarly attribution in the age of preprints. The issue is further complicated by the legal and ethical considerations surrounding AI training data, as explored in Is it legal to train AI models on copyrighted books? It’s complicated, which adds another layer of complexity to determining proper credit and acknowledging the source material.

The core concern, as /u/Vulcapulae rightly points out, is avoiding a perceived undermining of both the original work and the subsequent research that built upon it. Citing a preprint of your own work can feel circular, yet ignoring it entirely risks downplaying the foundational nature of that initial contribution. The solution, we believe, lies in transparency and careful framing. The camera-ready version offers an opportunity to explicitly acknowledge the preprint’s role in the research ecosystem. A sentence or two within the related work section could state something along the lines of, "An initial version of this work was released as a preprint [arXiv link] and has since informed subsequent research in the field, including [cite relevant works]." This approach validates both the preprint's impact and the subsequent studies' contributions, while clearly establishing the conference paper as the formally peer-reviewed and finalized version. This mirrors the considerations around reviewer integrity and transparency discussed in AAAI 2027 Reviewer Bidding and Assignment Integrity, where openly addressing potential biases or conflicts of interest strengthens the overall rigor of the research.

Furthermore, the rise of models like Ox Alpha, detailed in Who’s behind the new ‘stealth model’ Ox Alpha?, demonstrates the rapid pace of development and the often-opaque nature of AI research. This creates a context where preprints serve as crucial mechanisms for sharing early findings and fostering open collaboration. Failing to acknowledge the preprint lineage risks obscuring the intellectual history of the work and potentially hindering future innovation. The conference committee should also be aware of this dynamic and provide clear guidance to authors on how to handle preprint citations in their camera-ready submissions. A blanket prohibition on citing preprints would be counterproductive, stifling the very open exchange of ideas that drives progress in the field.

Ultimately, this situation highlights a broader need for the academic community to adapt to the evolving role of preprints. They are no longer simply a preliminary step on the path to publication, but rather an integral part of the research lifecycle. The way we attribute credit and acknowledge prior work needs to reflect this reality, embracing transparency and recognizing the interconnectedness of research in the digital age. The question moving forward is whether formal guidelines and best practices will emerge to codify this evolving understanding, or if researchers will continue to navigate this terrain on a case-by-case basis, relying on careful judgment and clear communication to ensure appropriate attribution and maintain the integrity of the scholarly record.