On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair.

Find out more about our session on Wednesday 21 January.

Should AI continue to be driven by a single paradigm, or does real progress lie in combining the strengths and weaknesses of many? Professor Luc De Raedt of KU Leuven has spent much of his career persistently addressing this question. Through pioneering work that bridges logic, probability, and machine learning, he has helped shape the field of neurosymbolic AI. In our conversation at IJCAI 2025 in Montreal, he spoke about what continues to fascinate him in this line of research, how he responds to criticisms of neurosymbolic AI, and why reconciling multiple paradigms is such an exciting challenge. Hello Professor De Raedt, thank you very much for joining me.

On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair.

We present the NeurIPS 2021 consistency experiment, a larger-scale variant of the 2014 NeurIPS experiment in which 10% of conference submissions were reviewed by two independent committees to quantify the randomness in the review process. We observe that the two committees disagree on their accept/reject recommendations for 23% of the papers and that, consistent with the results from 2014, approximately half of the list of accepted papers would change if the review process were randomly rerun. Our analysis suggests that making the conference more selective would increase the arbitrariness of the process. Taken together with previous research, our results highlight the inherent difficulty of objectively measuring the quality of research, and suggest that authors should not be excessively discouraged by rejected work.

This prestigious award is made for excellence in research in the area of autonomous agents. It is intended to recognize researchers in autonomous agents whose current work is an important influence on the field. Her work provides fundamental understanding of economic paradigms in multiagent systems, with a particular focus on computational social choice and game theory. She has made important contributions to the computational analysis of cooperative games, as well as to the studies of structured domains in elections, and hedonic games. Edith is also recognised for her service to the community.

The recent launches of two large language models, ChatGPT and Galactica, have led to much interest and controversy amongst the AI community, and beyond. These models, and in particular their potential use for writing scientific articles (and essays), provided the inspiration for this month's discussion. Joining the discussion this time are: Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), and Lucy Smith (AIhub). Sabine Hauert: Has anyone had a chance to use any of these new models yet? Sarit Kraus: During the summer I played with the previous version of GPT. Have you tried the latest version, Michael?

While researchers are trained to do research, there is little training for peer review. Several initiatives and experiments have looked to address this challenge. Recently, the ICML 2020 conference adopted a method to select and then mentor junior reviewers, who would not have been asked to review otherwise, with a motivation of expanding the reviewer pool to address the large volume of submissions.43 An analysis of their reviews revealed that the junior reviewers were more engaged through various stages of the process as compared to conventional reviewers. Moreover, the conference asked meta reviewers to rate all reviews, and 30% of reviews written by junior reviewers received the highest rating by meta reviewers, in contrast to 14% for the main pool. Training reviewers at the beginning of their careers is a good start but may not be enough. There is some evidence8 that quality of an individual's review falls over time, at a slow but steady rate, possibly because of increasing time constraints or in reaction to poor-quality reviews they themselves receive. While researchers are trained to do research, there is little training for peer review … Training reviewers at the beginning of their careers is a good start but may not be enough.