At the end of the Program, Scholars are expected to produce tangible research outputs.
All Scholars are required to produce a written publication at the end of the program (e.g., report, preprint, or conference proceeding).
As a default, final outputs will be published on Zenodo, where they will receive a DOI and be shared on the program webpage, ensuring that all scholars leave the program with a citable research product. For projects that are not yet ready for a full paper, a short extended abstract (approximately two pages, including figures) is a suggested target. This provides Scholars with a citable research product while allowing them to continue developing their work, without necessarily disclosing all findings or conclusions before pursuing further publication opportunities. Scholars whose work is more developed may submit longer outputs, including preprints, where appropriate.
Scholars and mentors are encouraged to pursue publication opportunities that are appropriate for their work. In cases where a project is submitted to or accepted by another publication venue, the program team will work with scholars and mentors to ensure that dissemination aligns with Neuromatch's publication practices.
Scholars should submit their final output by
The Final Research Seminars will showcase the Scholars’ final research projects and celebrate the culmination of their work in the AI Sentience Scholars Program. These sessions will be , offering an opportunity to share findings, exchange ideas, and engage in discussion.
- Approximately five Scholars will present at each session.
- Short presentations (10-15 min), followed by discussion and Q&A.
- Both sessions are open to the public.
Scholars, Mentors, members of the wider AI Sentience community, and other interested participants are warmly invited to attend and engage in the discussion!
Global metacognition in large language models
The Narrative Prior: A Computational Model of Personhood for Advanced AI
Mariana Amendoeira Duarte
Self-conception and Mind and Moral Status Attribution in Large Language Models