De Rijke has been one of the architects of generative retrieval — a methodological alternative to the standard "embed the query, search a vector index" paradigm, in which a sequence-to-sequence model is trained to directly emit the identifier of the relevant document, with no separate retrieval step. The question this opens is whether the retriever and the generator should ever have been separate components in the first place, or whether the whole pipeline was an artifact of how the field happened to evolve. For ai100, the consequence is structural: if generative retrieval keeps getting better, the assumption that "retrieval" and "generation" are distinguishable layers — which underlies most current evaluation — may not hold for the next generation of engines.

Worth following when
you want to track the line of research that questions whether retrieval and generation should be architecturally distinct at all.
Topics
generative retrieval (Differentiable Search Index family); counterfactual evaluation in IR; conversational search systems.
Key works
generative-retrieval line of papers (2022 onward, senior author across multiple); contributions to Foundations and Trends in Information Retrieval; ongoing UvA IRLab publications.